DESIGN OPTIMIZATION OF 3D STEEL FRAME STRUCTURES

Size: px
Start display at page:

Download "DESIGN OPTIMIZATION OF 3D STEEL FRAME STRUCTURES"

Transcription

1 I DESIGN OPTIMIZATION OF 3D STEEL FRAME STRUCTURES 9.1 Objectives Two objectives are associated with this chapter. First is to ascertai the advatages, metioed i Chapter 8, of the developed algorithm cosiderig more complex problems. Secod is to ivestigate the effect of the approaches, employed for determiig the effective bucklig legth of a colum, o the optimum desig. This chapter therefore exteds the work to the discrete optimum desig of 3 dimesioal (3D) steel frame structures usig the modified geetic algorithm (GA) liked to desig rules to BS 5950 ad BS I the formulatio of the optimizatio problem, the objective fuctio is the total weight of the structural bers. The cross sectioal properties of the structural bers, which form the desig variables, are chose from three separate catalogues (uiversal beams ad colums covered by BS 4, ad circular hollow sectios from BS 4848). Chapter 2 ad 3 idicate that the theory ad methods for the evaluatio of the effective legth of colums are based o a secod order aalysis assumig that the

2 Desig Optimizatio of 3D Steel Frame Structures 274 bucklig of bers out of plae of the framework is preveted. This, however, could be correct for 3D structures as well if a rigid bracig system or shear wall, etc., is provided. Therefore, due to the eed for the use of a more accurately evaluated effective bucklig legth of colums i 3D steel frame structures, the fiite elemet method is employed. Followig the desig procedure of steel structures to BS 5950, the miimum weight desigs of two 3D steel frame structures subjected to multiple loadig cases are obtaied. These examples show that the modified GA i combiatio with the structural desig rules provides a efficiet tool for practicig desigers of steel frame structures. This chapter starts with describig the desig procedure for 3D steel frame structures accordig to BS 5950, the combies that procedure with the modified GA to perform desig optimizatio of bechmark examples. 9.2 Desig procedure to BS 5950 The local ad global coordiate systems show i Figure 9.1 are assumed i order to correlate betwee the idices give by BS 5950 ad that employed i the cotext. Figure 9.2a shows a isometric view of a 3D structure with coordiate system while Figures 9.2b ad 9,2c display the structural system as well as the deformed cofiguratio. Z Z Figure 9.1. Local ad global coordiate systems

3 Desig Optimizatio of 3D Steel Frame Structures 275 Trasverse beams Z Successive frameworks Bracig system (a) Isometric view Z U, c max N b δ s, 1 I x max b δ L, c s, Nb + 1 I x h s 1, 1 I x 1, Nb + 1 I x 2 h 1 B 1 B Nb (b) Z projectio of the 3D structure Z U, c L, δ max c b h s h 1 SP 1 SP N bb (c) Z projectio of the 3D structure Figure D structure with the coordiate system ad deformed cofiguratio

4 Desig Optimizatio of 3D Steel Frame Structures 276 It is assumed that the successive frameworks are rigid joited. I additio, it is supposed that oe ed of each trasverse beam is free to rotate about its local axes, ad Z while the secod ed is free to rotate about ad axes. This assumptio has bee made because BS 5950 does ot cater for the desig of bers subjected to torsioal momet. Similarly, the structural system of the bracig bers is cosidered as show i Figure 9.2c. BS 5950 reuires the desiger to select appropriate stadard sectios for the bers of a steel structure i order to obtai a desig havig a sufficiet factor of safety. This is accomplished by cosiderig ultimate ad serviceability limit states. I elastic desig of rigid joited multi storey structures, BS 5950 recommeds that a liear aalysis of the whole structure is carried out. This is achieved by utilisig the fiite elemet package ANSS. The, the desig criteria are checked. This ca be summarised i the followig steps. Step 1. Preparatio of data files icludig structural geometry, loadig cases, etc. Step 2. Classificatio of the structure whether it is sway or o sway. This is achieved by applyig the otioal horizotal loadig case. A structure, aalysed without icludig the effect of claddig, is classified as o sway if each colum of the structure satisfies U, c L c 2000 L, c 1, (9.1) U, c L c 2000 L, c 1 ad c 1,Λ, N c =. (9.2)

5 Desig Optimizatio of 3D Steel Frame Structures 277 Step 3. Evaluatio of the effective legths eff, L ad eff, L of colums, beams ad bracig bers about the major () ad mior () local axes. I this work, the effective bucklig legth eff, L of colums has bee evaluated by the followig three c approaches: usig the charts from BS 5950; a more accurate method (SCI, 1988) based o fiite elemet aalysis (ANSS); selectio of the coservative (higher) value out of the two. I the secod approach, the effective legth L L x = (9.3) eff, FE c ( ), c F c P E, c where F ( x ) is the ormal force at the critical load of the structure, c P E, c 2 EI c 2 L c π =. (9.4) For a beam the effective bucklig legth eff, b L about the axis euals the urestraied legth of the compressio flage o the uderside of the beam (MacGiley, 1997). For colums ad beams, the effective legth eff, L about the axis euals the urestraied legth of the ber uder cosideratio. For bracig bers, BS 5950 specifies the effective legths eff, br L ad eff, br L depedig o the ed restraits of the bers. I this work, it is assumed that

6 Desig Optimizatio of 3D Steel Frame Structures 278 each bracig ber is ot restraied at either eds about the local axis. Therefore, eff, br L ad eff, br L ca be determied by eff 1. 0L, br br L =, (9.5) eff, br L = 0. 85L. (9.6) br Step 4. Calculatio of the slederess ratios λ x) ad λ ) of the ber usig (, eff,, ( x, i, j L λ ( x ) =, (9.7), r eff, L ( xi, j ) λ ( x ), i, j = (9.8) r, where r ad r are the radius of gyratios of the sectio ber about its,, ad axes respectively. Step 5. Check of the slederess costraits Sle s, G for each ber Sle s, G 1, s = 1, 2, (9.9) where G Sle 1, λ, = ad (9.10) 180 G Sle 2, ( x i, j λ ( x ), i, j ) =. (9.11) 180 Step 6. Aalysis of the structure uder each loadig case to obtai the ormal force, shearig forces ad bedig momets for each ber.

7 Desig Optimizatio of 3D Steel Frame Structures 279 Step 7. Check of the stregth criteria for each ber follows: uder the loadig case as a) Determiatio of the type of the sectio of the ber (e.g. sleder, semi compact, compact or plastic). b) Evaluatio of the desig stregth p of the ber. y, Str, c) Check of the stregth costraits G depedig o whether the ber is r, i tesio or compressio. This stage cotais five checks (r = 5) for each ber uder each loadig case. The stregth costraits are local capacity, overall capacity, shear capacities i ad directios ad the shear bucklig capacity. These ca be expressed as follows: where the local capacity G Str, 1, = A A e, g, F ( x ( x Str, G 1, r = 1, 2, 3, 4, 5 ad = 1,2, Λ, Q (9.12) r, i, j F ) p i, j ) p y, y, ( x ( x i, j i, j M + ) M M M + ) M M, C, M, C,, C, M, C, + ( x ) ( x + ( x ) ( x i, j i, j i, j i, j ) ) for tesio bers (9.13) for comprisso bers where F ( x ) is the axial force. The applied momet about the local axes ad (, (, are M x) ad M x ). The desig stregth is ). The momet p ( x y, i, j

8 Desig Optimizatio of 3D Steel Frame Structures 280 capacities of the ber about its ad axes are M x ) ad ( C, i, j M x ). It is assumed that A x ) ad A ( x ) ( C, i, j ( e, i, j g, i, j are eual. Accordig to clause of BS 5950, the desiger is ot reuired to check the bracig bers for lateral torsioal bucklig whe they are i tesio, therefore the Str, overall capacity G is determied by 2, G Str, 2, ( x ) = m A g, M M b, ( x F, i, j ) p for tesio bers (beams ad colums) C, p m ( x y, i, j ( x m + ) M i, j ) Z,, M M b, ( x i, j, ) (9.14) + for compressio bers. Str, The shear capacities G ad G ( x ) are computed by 3, G G Str, 4, Str, x F ( ), ( x ) =, (9.15) P ( ) 3, x, i, j Str, x F ( ), ( x ) = (9.16) P ( ) 4, x, i, j where P x ) ad P x ) are the shear capacities of the ber i the (, i, j (, i, j (, ad directios. The critical shear forces are F x). Str, Each ber should also checked for shear bucklig G if d( x t( x i, j i, j 5, ) 63 ε ( xi, j ). (9.17) )

9 Desig Optimizatio of 3D Steel Frame Structures 281 Hece, G Str, x F ( ), ( x ) =. (9.18) V ( ) 4, x cr, i, j d) For a sway structure, the otioal horizotal loadig case is cosidered, this is termed the sway stability criterio. Step 8. Checks of the horizotal (i ad directios) ad vertical odal displacemets that are kow as serviceability criteria This is performed by: Ser t, G 1, t = 1, 2 ad 3. (9.19) a) Computig the horizotal odal displacemets due to the ufactored imposed loads ad wid loadig cases i order to satisfy the limits o the horizotal displacemets, G Ser 1, c = U, c L c 300 L, c, (9.20) G U Ser, c = 2, c L c 300 L, c ad c 1,Λ, N c =. (9.21) b) Imposig the limits o the vertical odal displacemets (maximum value withi a beam) due to the ufactored imposed loadig case usig G δ max Ser b = 2, b L b 360, b, 2 b = 1, Λ, N. (9.22) The flowchart give i Figure 9.3 illustrates the desig procedure of 3D steel frame structures to BS 5950.

10 Desig Optimizatio of 3D Steel Frame Structures 282 Start Apply otioal horizotal loadig case, compute horizotal odal displacemets ad determie whether the framework is sway or o sway usig step 2 Compute the effective bucklig legths accordig the reuired approach metioed i step 3 Apply loadig case = 1, 2, Λ, Q : if the framework is sway, the iclude the otioal horizotal loadig case Aalyse the framework, compute ormal forces, shearig forces ad bedig momets for each ber Desig of ber = 1, 2, Λ, N Determie the type of the sectio (sleder, semi compact, compact or plastic) utilisig Table 7 of BS 5950 Evaluate the desig stregth p ( x ) y, i, j of the ber Check the slederess criteria employig (9.30) (9.11) NO Tesio ber? ES A B C D Figure 9.3a. Flowchart of desig procedure of 3D steel frame structures

11 Desig Optimizatio of 3D Steel Frame Structures 283 A B C D Local capacity check Local capacity check Overall capacity check Lateral torsioal bucklig check Carry out the checks of shear applyig (9.15) (9.16) ad shear bucklig usig (9.18) if ecessary Is = N? NO ES NO Is = Q? ES Compute the horizotal ad vertical odal displacemets due to the specified loadig cases Check of the serviceability criteria usig (9.19) (9.22) Ed Figure 9.3b. (cot.) Flowchart of desig procedure of 3D steel frame structures

12 Desig Optimizatio of 3D Steel Frame Structures Problem formulatio ad solutio techiue The geeral formulatio of the desig optimizatio problem ca be expressed by Miimize F( x ) = N = 1 W L Str, subject to: G 1, r = 1, 2, 3, 4, = 1, 2, Λ, Q r, Sle s, G 1, s = 1, 2 Ser t, G 1, t = 1, 2, 3 s, I b x s 1, b x I 1 = 1,,, N, =, 2, Λ, N 1 (9.23), s 2 Λ s b 1 b + T T T T 1, x2, x j, Λ, x J ) x = ( x, j = 1, 2, Λ, J x, D ad i j j where D j = ( d d d,, Λ, j, 1 j, 2 j, λ W is the mass per uit legth of the ber uder cosideratio ad is take from a catalogue. The stregth, slederess ad serviceability criteria are Sle x s, Ser x t, ) G Str, r,, G ( ) ad G ( ) respectively. The vector of desig variables x is divided ito J sub vectors x J. The compoets of these sub vectors take values from a correspodig catalogue D j. I the preset work, the cross sectioal properties of the structural bers, which form the desig variables, are chose from three separate catalogues (uiversal beams ad colums covered by BS 4, ad circular hollow sectios from BS 4848). Figure 9.4 demostrates the applied solutio techiue.

13 Desig Optimizatio of 3D Steel Frame Structures 285 Start Iput data files: GA parameters, FE model, loadig cases, etc. Radomly geerate the iitial populatio Desig set =1, 2, Λ o,n p Decode biary chromosomes to iteger values ad select the sectios from the appropriate catalogue accordig to their correspodig iteger values Apply the desig procedure illustrated i flowchart give i Figure 9.3 to check stregth, sway stability ad serviceability criteria to BS 5950 Save the feasibility checks of the desig set Desig ES set = N? o p NO New desig ES Evaluate the objective ad pealised fuctios o Select the best N p idividuals out of N p, ad impose them ito the first geeratio of GA algorithm A Figure 9.4a. Flowchart for the solutio techiue

14 Desig Optimizatio of 3D Steel Frame Structures 286 A Geeratio 1: Calculate the ew pealised objective fuctio, the carry out crossover ad mutatio Desig set = 2, 3 Λ, N p Decode biary chromosomes to iteger values ad select the sectios from the appropriate catalogue accordig to their correspodig iteger values Apply the desig procedure illustrated i flowchart give i Figure 9.3 to check stregth, sway stability ad serviceability criteria to BS 5950 Save the feasibility checks of the desig set New geeratio Desig set = N? p NO New desig ES Evaluate the objective ad pealised fuctios Covergece occurred? ES Stop NO Store the best idividuals, ad impose them ito the ext geeratio ad carry out crossover ad mutatio Figure 9.4b. (cot.) Flowchart for the solutio techiue

15 Desig Optimizatio of 3D Steel Frame Structures Bechmark examples Havig itroduced the desig procedure accordig to BS 5950 liked to the GA procedure ad the formulatio of desig optimizatio problem, the process of optimizatio i ow carried out. Two steel frame structures are demostrated here to illustrate the effectiveess ad beefits of the developed GA techiue as well as ivestigatig the effect of the employed approach for the determiatio of the effective bucklig legth o the optimum desig attaied. The catalogue of available cross sectios i BS 4 iclude 64 uiversal beams (UB) ad 32 uiversal colums (UC). These sectios are give i Sectio The catalogue of circular hollow sectios (CHS) is take from BS 4848 ad this icludes 64 sectios, listed i Table 9.1, varyig from CHS to CHS. I the preset work, the iitial populatio size o N p is assumed to be 1000 ad fixed populatio size N p of 70 durig successive geeratios, elite percetage 30 %, probability of crossover P c was 0.7, probability of mutatio E r was P m was 0.01 ad oe poit crossover is applied. I additio, the techiue described i Sectio 6.2 is utilised where the simple "exact" pealty fuctio employed is Miimize C - F( x), all costraits satisfied F ( x ) = (9.24) 0, ay of costraits violated. used where The covergece criteria ad termiatio coditios detailed i Sectio are av C = 0.001, cu max = C = ad ge 200.

16 Desig Optimizatio of 3D Steel Frame Structures 288 Table 9.1. The used circular hollow sectios Cross sectio Cross sectio Cross sectio CHS CHS CHS CHS CHS CHS CHS CHS CHS CHS CHS CHS CHS CHS CHS CHS CHS CHS CHS CHS CHS CHS CHS CHS CHS CHS CHS CHS CHS CHS CHS CHS CHS CHS CHS CHS CHS CHS CHS CHS CHS CHS CHS CHS CHS CHS CHS CHS CHS CHS CHS CHS CHS CHS CHS CHS CHS CHS CHS CHS CHS CHS CHS CHS

17 Desig Optimizatio of 3D Steel Frame Structures Example 1: Two bay by two bay by two storey structure The first 3D steel frame structure, aalysed i this chapter, is the two bay by two bay by two storey structure show i Figure 9.5. It ca be observed from Figure 9.5a that the structure cosists of three successive frameworks, trasverse beams ad the bracig system. The spacig betwee the successive frameworks is 10.0 m while the distace betwee the successive trasverse beams is 5 m. Because BS 5950 does ot cater for the desig of bers subjected to torsioal momets, it is assumed that oe ed of each trasverse beam is free to rotate about its local axes, ad Z while the secod ed is free to rotate about ad axes. Similarly, the structural system of the bracig bers is cosidered. The structural system is show i Figures 9.5b 9.5d. The structure was desiged for use as a office block icludig projectio rooms. Nie loadig cases were take ito accout ad these represet the most ufavourable combiatios of the factored dead load (DL), imposed load (LL) ad wid load (WL) as reuired by BS 5950 ad BS Because the structure is doubly symmetric, two orthogoal wid cases have bee cosidered where the wid loads are factored by 1.2. The values of the ufactored DL ad LL are tabulated i Table 9.2. These values are calculated accordig to the recommedatios give by Owes et al (1992), MacGiley (1997) ad Nethercot (1995). Table 9.2. The values of dead load ad imposed load Value of the load o roof Value of the load o first floor DL 7.0 kn/m kn/m 2 LL 2.0 kn/m kn/m 2

18 Desig Optimizatio of 3D Steel Frame Structures 290 Framework B Framework A Trasverse beams m = 20.0 m m = 10.0 m m = 20.0 m m = 20.0 m Bracig bers Frot face (a) Isometric view View F m m m 10 m 10 m 10 m (b) Framework A (c) Framework B m 5 m m m 10 m (d) View F 10 m m 5 m 5 m 5 m (c) Pla Figure 9.5. Two bay by two bay by two storey structure

19 Desig Optimizatio of 3D Steel Frame Structures 291 The reiforced cocrete slabs are assumed to trasmit the loads i oe directio because the ratio of the spacig betwee the successive frameworks ad the distaces betwee the successive trasverse beams eual 2 (see MacGiley, 1997). Accordig to BS 6399: Part 2, the stadard method was utilised to determie the values of the wid pressures o the vertical walls ad the flat roof assumig 1. the opeigs are domiat, 2. the buildig type factor eual 4.0, 3. the referece height of the structure (H r ) eual to the maximum height of the structure above the groud level (10.0 m), 4. the basic wid speed Vb is 23 m/s, 5. the terrai ad buildig factor S b is 1.58, 6. the altitude factor S a, the directioal factor probability factor Sp eual 1.0, S d, the seasoal factor S s ad the 7. the size effect factor C a is take as 0.90, 8. the exteral pressure coefficiet C pe for each surface of the buildig is determied accordig table 5 ad 8 of BS 6399 ad 9. the iteral pressure coefficiet C pi for each surface of the structure is C = 0 C (9.25) pi. 75 pe where C pe is the exteral pressure coefficiet of the surface uder cosideratio. At this stage havig itroduced the basic assumptios for evaluatig the desig loads, the values of these loads ca accordigly be computed depedig o the ie loadig cases summarised bellow: 1. the floors are subjected to the vertical uiform loads P = 1. 4DL LL, v

20 Desig Optimizatio of 3D Steel Frame Structures the floors are subjected to the vertical uiform loads P = 1. 4DL LL, ad left had side (LHS) odes of each framework are subjected to the horizotal cocetrated loads due to the otioal horizotal loadig coditio, 3. the floors of the first bay (coutig from the left) are subjected to the vertical v uiform loads P v = 1. 4DL while the rest of the floors are subjected to v P = 1. 4DL LL, 4. the floors are subjected to a staggered arragemet of vertical uiform loads v P = 1. 4DL LL ad P v = 1. 4DL, 5. the floors are subjected to the vertical uiform loads P = 1. 2DL LL ad the structure is subjected to the first factored wid loadig case whe the LHS face of the structure is the widward face, 6. the floors are subjected to the vertical uiform loads P = 1. 2DL LL ad the structure is subjected to the secod factored wid loadig case whe the frot face of the structure is the widward face, 7. the floors are subjected to the vertical uiform load P v = 1.0LL, v v 8. the floors are subjected to the vertical uiform loads P v = 1.0LL ad the structure is subjected to the first ufactored wid loadig case whe the LHS face of the structure is the widward face ad 9. the floors are subjected to the vertical uiform loads P v = 1.0LL ad the structure is subjected to the secod ufactored wid loadig case whe the frot face of the structure is the widward face. A more accurately evaluated effective bucklig legth of colums was determied usig the fiite elemet method usig the loadig patter displayed i Figure 9.6. The

21 Desig Optimizatio of 3D Steel Frame Structures 293 fiite elemet model of the structure was assembled i ANSS usig 5 elemets for each beam ad colum while oe elemet for each ber of the bracig system was used. 0.01P P 0.01P P 0.01P P P (a) Loadig patter of the roof level 0.01P 0.01P 0.01P 8P (b) Loadig patter of the first floor level Figure 9.6. Loadig patter used for the stability aalysis The desig optimizatio processes were carried out cosiderig 8 desig variables. The likig of the desig variables is displayed i Figures 9.3.b 9.3d. Referrig to BS 4 ad BS 4848, the catalogue of the available cross sectios icludes 64 uiversal beams (UB), 32 uiversal colums (UC) ad 64 circular hollow sectios (CHS). This results i a total strig legth of 44. The problem was aalysed utilisig the solutio parameters as described i Sectio 9.4. Five rus of the desig optimizatio processes were carried out usig 5

22 Desig Optimizatio of 3D Steel Frame Structures 294 differet seed umbers to geerate the iitial populatio. Solutios are preseted i Table 9.3. The desig variables correspodig to the best desigs are give i Table 9.4. Table 9.3. Two bay by two bay by two storey structure: compariso of the best desig obtaied i five rus Total weight (kg) Ru First approach (code) Secod approach (FE) Third approach (coservative) Average weight Miimum weight Table 9.4. Two bay by two bay by two storey structure: compariso of the desig variables for the obtaied optimum desigs Desig variable First approach (code) Cross sectios Secod approach (FE) Third approach (coservative) UC UC UC UC UC UC UC UC UC UC UC UC UB UB UB UB UB UB UB UB UB CHS CHS CHS Weight (kg)

23 Desig Optimizatio of 3D Steel Frame Structures 295 Durig the desig optimizatio process, the covergece characteristics of the solutios were examied. Figure 9.7 shows the covergece history of the best desigs. It ca be observed that the best solutios were obtaied withi 50 geeratios while the rest of the computatios were carried out to satisfy the covergece criteria. Best desig (kg) First approach (code) Secod approach (FE) Third approach (coservative) Geeratio umber Figure 9.7. Two bay by two bay by two storey structure: best desig versus geeratio umber Example 2: Three bay by four bay by four storey structure The ext 3D structure is the three bay by four bay by four storey structure show i Figure 9.8. The structure cosists of four successive frameworks, trasverse beams ad a bracig system as show i Figure 9.8a. The spacig betwee the successive frameworks is 8.0 m. The distace betwee the successive trasverse beams is 4.0 m. The structural system is show i Figures 9.8b 9.8f. The structure is desiged for use as a office block icludig projectio rooms. Te loadig cases represetig the most ufavourable combiatios of the factored DL, LL ad WL are take ito accout.

24 Desig Optimizatio of 3D Steel Frame Structures 296 Framework A Framework B m = 24.0 m Trasverse beams 17.0 m Framework D Bracig system m = 32.0 m Frot face Framework C View F (a) Isometric view m 4 m 4 m 5 m m 8 m 8 m 8 m 8 m 8 m (b) Framework A (c) Framework B m m 4 m m m 8 m 8 m 8 m 8 m 8 m (d) Framework C (e) Framework D (f) View F Figure 9.8. Three bay by four bay by four storey structure

25 Desig Optimizatio of 3D Steel Frame Structures 297 Because the structure is sigly symmetric, three orthogoal wid cases have bee cosidered where the wid loads are factored by 1.2. The values of the ufactored DL ad LL are tabulated i Table 9.2. Table 9.5. The values of deal load ad imposed load Value of the load o roof Value of the load o other floors DL 6.5 kn/m kn/m 2 LL 2.0 kn/m kn/m 2 Accordig to BS 6399: Part 2, the stadard method was utilised to determie the values of the wid pressures o the vertical walls ad the flat roof cosiderig: 1. the opeigs are domiat, 2. the buildig type factor euals 4.0, 3. the referece height of the structure (H r ) euals to the maximum height of the structure above the groud level (17.0 m), 4. the basic wid speed Vb is 23 m/s, 5. the terrai ad buildig factor S b is 1.71, 6. the altitude factor S a, the directioal factor probability factor Sp eual 1.0, S d, the seasoal factor S s ad the 7. the size effect factor C a is take as 0.87, 8. the exteral pressure coefficiet C pe for each surface of the buildig is determied (see Sectio 2.3.3) accordig Tables 5 ad 8 of BS 6399 ad 9. the iteral pressure coefficiet C pi is calculated by (9.25). The desig loads were computed accordig to the followig loadig cases:

26 Desig Optimizatio of 3D Steel Frame Structures the floors are subjected to the vertical uiform loads P = 1. 4DL LL, v 2. the floors are subjected to the vertical uiform loads P = 1. 4DL LL, ad left had side (LHS) odes of each framework are subjected to the horizotal cocetrated loads due to the otioal horizotal loadig coditio (see Chapter 2), 3. the floors of the first bay (coutig from the left) are subjected to the vertical v uiform loads P v = 1. 4DL while the rest of the floors are subjected to v P = 1. 4DL LL, 4. the floors are subjected to a staggered arragemet of vertical uiform loads v P = 1. 4DL LL ad P v = 1. 4DL, 5. the floors are subjected to the vertical uiform loads P = 1. 2DL LL ad the structure is subjected to the first factored wid loadig case whe the LHS face of the structure is the widward face, 6. the floors are subjected to the vertical uiform loads P = 1. 2DL LL ad the structure is subjected to the secod factored wid loadig case whe the frot face of the structure is the widward face, 7. the floors are subjected to the vertical uiform loads P = 1. 2DL LL ad the structure is subjected to the third factored wid loadig case whe the rear face of the structure is the widward face, 8. the floors are subjected to the vertical uiform load P v = 1.0LL, v v v 9. the floors are subjected to the vertical uiform loads P v = 1.0LL ad the structure is subjected to the first ufactored wid loadig case whe the LHS face of the structure is the widward face ad

27 Desig Optimizatio of 3D Steel Frame Structures the floors are subjected to the vertical uiform loads P v = 1.0LL ad the structure is subjected to the secod ufactored wid loadig case whe the frot face of the structure is the widward face. The fiite elemet method was employed (see Toropov et al., 1999) i order to evaluate the effective bucklig legth of colums. This was performed by utilisig the loadig patter displayed i Figure 9.9. I the fiite elemet model, the structure was assembled i ANSS usig 5 elemets for each beam ad colum while oe elemet for each ber of the bracig system. The optimizatio process was carried out cosiderig 12 desig variables. The likig of the desig variables is displayed i Figures 9.8.b 9.8f. Referrig to BS 4 ad BS 4848, the catalogue of the available cross sectios iclude 64 uiversal beams (UB), 32 uiversal colums (UC) ad 64 circular hollow sectios (CHS). This results i a total strig legth of 64. The problem was aalysed utilisig the solutio parameters described i Sectio 9.4. Five rus of the optimizatio process were carried out usig 5 differet seed umbers to geerate the iitial populatio. The optimizatio process was termiated whe ay of the covergece criteria is satisfied. Solutios are preseted i Table 9.6. The desig variables correspodig to the best desigs are give i Table 9.7.

28 Desig Optimizatio of 3D Steel Frame Structures P P 0.01P 0.01P P P P (a) Loadig patter of the roof level 0.01P 0.01P 0.01P 8P 8P 4 (b) Loadig patter of the third floor level 0.01P 0.01P 0.01P P 0.01P 5P 0.01P 8P 5P 8 4 P (c) Loadig patter of the secod floor level 0.01P 0.01P 0.01P 0.01P 6P 0.01P 8P 6P 8P (d) Loadig patter of the first floor level Figure 9.9. Loadig patter used for the stability aalysis

29 Desig Optimizatio of 3D Steel Frame Structures 301 Table 9.6. Three bay by four bay by four storey structure: compariso of the best desig obtaied i five rus Total weight (kg) Ru First approach (code) Secod approach (FE) Third approach (coservative) Average weight Miimum weight Table 9.7. Three bay by four bay by four storey structure: compariso of the desig variables for the obtaied optimum desigs. Desig variable First approach (code) Cross sectios Secod approach (FE) Third approach (coservative) UC UC UC UC UC UC UC UC UC UC UC UC UB UB UB UC UC UC UC UC UC UC UC UC UC UC UC UB UB UB UB UB UB CHS CHS CHS Weight (kg)

30 Desig Optimizatio of 3D Steel Frame Structures 302 From Table 9.6, it ca deduced that the optimizer was able to obtai several solutios ad the differeces betwee them are small. Durig the optimizatio process, the covergece characteristics of the solutios were examied. Figure 9.10 shows the covergece history of the best desigs. Best desig (kg) First approach (code) Secod approach (FE) Third approach (coservative) Geeratio umber Figure Three bay by four bay by four storey structure: best desig versus geeratio umber. From this figure, it ca be observed that the optimum solutios were achieved withi 50 geeratios while the rest of the computatioal effort was eeded to satisfy the termiatio coditios described i sectio Validatio of the optimum desig This sectio shows that the developed FORTRAN code for desig of 3D steel frame structures is successfully implemeted. As discussed i Sectio 8.5, to validate the applied FORTRAN code, the problem should be first ru whe 2). The, CSC software is used to check the calculated costraits. m is 1 (techiue

31 Desig Optimizatio of 3D Steel Frame Structures 303 The two bay by two bay by two storey structure (studied i Sectio 9.4.1) was aalysed. The loadig cases metioed i Sectio are utilised. The optimizatio process was carried out usig the desig procedure discussed i Sectio 9.2 while the solutio parameters ad the covergece criteria were applied as cosidered i Sectio 9.4. Five rus were carried out whe applyig the first approach for determiig the effective bucklig legths. The desig variables correspodig to the best solutio are tabulated i Table 9.8. It is worth comparig the desig variables obtaied with those achieved i sectio (techiue 1) whe a more accurate euatio for determiig m ( x ) was applied. This compariso is also preseted i Table 9.8. It ca be observed that whe applyig techiue 2, the optimizer succeeded i obtaiig a solutio ( kg) uite ear to that achieved whe usig techiue 1 ( kg). Table 9.8. Two bay by two bay by two storey structure: compariso of the desig variables for the optimum desigs. Desig variable Cross sectios Techiue 1 Techiue UC UC UC UC UC UC UC UC UB UB UB UB UB UB CHS CHS Weight (kg)

32 Desig Optimizatio of 3D Steel Frame Structures 304 The covergece characteristics were also examied. This was achieved by plottig the chages of the best desig with the umber of geeratios performed for each ru as show i Figure Best desig (kg) First ru Secod ru Third ru Fourth ru Fifth ru Geeratio umber Figure Two bay by two bay by two storey structure: best desig versus geeratio umber At this stage, the structural weight has bee optimized ad the sectio of each ber has bee determied. The ext step is to validate the code checks usig CSC software. This is achieved by usig the followig proceduce: 1) I S FRAME, the structural geometry, ber sectios ad loadig cases are defied. The, the bedig momets, shear forces, ad odal displacemets are calculated accordig to the aalysis type presupposed (liear aalysis). 2) Startig the S STEEL program. The desig checks are the carried out. 3) The desig results are the displayed o a separate widow as show i Figure 9.12.

33 Figure The desig results of two bay by two bay by two storey structure (captured from S STEEL)

34 Desig Optimizatio of 3D Steel Frame structures 306 I this figure, the desig checks of each ber are idicated i colour i which the code utilisatio meu gives the rage for of each colour. It ca be observed that most of the bers reach their maximum capacities. This idicates that the developed algorithm is successfully icorporated i desig optimizatio. It is worth otig that the desig results vary betwee 0.7 ad Cocludig remarks Desig optimizatio techiue based o GA was preseted for 3D steel frame structures where the structures were subjected to multiple loadig coditios. The desig method obtaied a 3D steel frame structure with the least weight by selectig appropriate sectios for beams, colums ad bracig bers from the British stadard for uiversal beam sectios, uiversal colum sectios ad circular hollow sectios. The followig cocludig remarks ca be made. 1) It has bee prove that the developed GA approach ca be successfully icorporated i desig optimizatio i which the structural bers have to be selected from the available stadard sectios while the desig satisfies the desig criteria. This idicates that the developed approach ca be utilised by a practisig desigers. 2) I the preset chapter, the skills ad experiece of the desiger have bee reflected i the optimizatio problem by imposig costraits o the secod momet of area of two adjacet colums i two adjacet storey levels. This ca be implemeted usig other costraits such as sectioal dimesios, sectioal area, etc. This idicates that the optimizer is able to treat differet practical costraits depedig o the ature of the problem. 3) It has bee show that the developed GA provides the desiger with more tha oe solutio to choose from, ad the differece betwee them was small. This could be

35 Desig Optimizatio of 3D Steel Frame structures 307 a advatage whe a desiger eeds to choose a appropriate solutio depedig o the availability of the sectios. 4) From Tables 9.4 ad 9.7, it ca be observed that the same sectios have bee obtaied for differet bers of a structure eve though these bers are liked to differet desig variables. This idicates that it ca be beeficial to iclude the groupig of structural bers as a additioal criterio i the formulatio of the desig optimizatio problem. 5) I the preset study, computatio of the effective bucklig legth has bee automated ad icluded i the developed algorithm. This was achieved by employig three differet approaches. Applicatio of a modified GA to desig optimizatio of structural steelwork allows the best set from a appropriate catalogue of steel cross sectios to be chose. The optimizer has bee liked to a commercial fiite elemet code ad the British codes of practice i order to obtai optimum desigs accepted by practisig structural egieers.

Modified Line Search Method for Global Optimization

Modified Line Search Method for Global Optimization Modified Lie Search Method for Global Optimizatio Cria Grosa ad Ajith Abraham Ceter of Excellece for Quatifiable Quality of Service Norwegia Uiversity of Sciece ad Techology Trodheim, Norway {cria, ajith}@q2s.tu.o

More information

A Combined Continuous/Binary Genetic Algorithm for Microstrip Antenna Design

A Combined Continuous/Binary Genetic Algorithm for Microstrip Antenna Design A Combied Cotiuous/Biary Geetic Algorithm for Microstrip Atea Desig Rady L. Haupt The Pesylvaia State Uiversity Applied Research Laboratory P. O. Box 30 State College, PA 16804-0030 haupt@ieee.org Abstract:

More information

Confidence Intervals for One Mean

Confidence Intervals for One Mean Chapter 420 Cofidece Itervals for Oe Mea Itroductio This routie calculates the sample size ecessary to achieve a specified distace from the mea to the cofidece limit(s) at a stated cofidece level for a

More information

Vladimir N. Burkov, Dmitri A. Novikov MODELS AND METHODS OF MULTIPROJECTS MANAGEMENT

Vladimir N. Burkov, Dmitri A. Novikov MODELS AND METHODS OF MULTIPROJECTS MANAGEMENT Keywords: project maagemet, resource allocatio, etwork plaig Vladimir N Burkov, Dmitri A Novikov MODELS AND METHODS OF MULTIPROJECTS MANAGEMENT The paper deals with the problems of resource allocatio betwee

More information

Chapter 7 Methods of Finding Estimators

Chapter 7 Methods of Finding Estimators Chapter 7 for BST 695: Special Topics i Statistical Theory. Kui Zhag, 011 Chapter 7 Methods of Fidig Estimators Sectio 7.1 Itroductio Defiitio 7.1.1 A poit estimator is ay fuctio W( X) W( X1, X,, X ) of

More information

Department of Computer Science, University of Otago

Department of Computer Science, University of Otago Departmet of Computer Sciece, Uiversity of Otago Techical Report OUCS-2006-09 Permutatios Cotaiig May Patters Authors: M.H. Albert Departmet of Computer Sciece, Uiversity of Otago Micah Colema, Rya Fly

More information

Soving Recurrence Relations

Soving Recurrence Relations Sovig Recurrece Relatios Part 1. Homogeeous liear 2d degree relatios with costat coefficiets. Cosider the recurrece relatio ( ) T () + at ( 1) + bt ( 2) = 0 This is called a homogeeous liear 2d degree

More information

SECTION 1.5 : SUMMATION NOTATION + WORK WITH SEQUENCES

SECTION 1.5 : SUMMATION NOTATION + WORK WITH SEQUENCES SECTION 1.5 : SUMMATION NOTATION + WORK WITH SEQUENCES Read Sectio 1.5 (pages 5 9) Overview I Sectio 1.5 we lear to work with summatio otatio ad formulas. We will also itroduce a brief overview of sequeces,

More information

INVESTMENT PERFORMANCE COUNCIL (IPC)

INVESTMENT PERFORMANCE COUNCIL (IPC) INVESTMENT PEFOMANCE COUNCIL (IPC) INVITATION TO COMMENT: Global Ivestmet Performace Stadards (GIPS ) Guidace Statemet o Calculatio Methodology The Associatio for Ivestmet Maagemet ad esearch (AIM) seeks

More information

Systems Design Project: Indoor Location of Wireless Devices

Systems Design Project: Indoor Location of Wireless Devices Systems Desig Project: Idoor Locatio of Wireless Devices Prepared By: Bria Murphy Seior Systems Sciece ad Egieerig Washigto Uiversity i St. Louis Phoe: (805) 698-5295 Email: bcm1@cec.wustl.edu Supervised

More information

, a Wishart distribution with n -1 degrees of freedom and scale matrix.

, a Wishart distribution with n -1 degrees of freedom and scale matrix. UMEÅ UNIVERSITET Matematisk-statistiska istitutioe Multivariat dataaalys D MSTD79 PA TENTAMEN 004-0-9 LÖSNINGSFÖRSLAG TILL TENTAMEN I MATEMATISK STATISTIK Multivariat dataaalys D, 5 poäg.. Assume that

More information

where: T = number of years of cash flow in investment's life n = the year in which the cash flow X n i = IRR = the internal rate of return

where: T = number of years of cash flow in investment's life n = the year in which the cash flow X n i = IRR = the internal rate of return EVALUATING ALTERNATIVE CAPITAL INVESTMENT PROGRAMS By Ke D. Duft, Extesio Ecoomist I the March 98 issue of this publicatio we reviewed the procedure by which a capital ivestmet project was assessed. The

More information

Determining the sample size

Determining the sample size Determiig the sample size Oe of the most commo questios ay statisticia gets asked is How large a sample size do I eed? Researchers are ofte surprised to fid out that the aswer depeds o a umber of factors

More information

Taking DCOP to the Real World: Efficient Complete Solutions for Distributed Multi-Event Scheduling

Taking DCOP to the Real World: Efficient Complete Solutions for Distributed Multi-Event Scheduling Taig DCOP to the Real World: Efficiet Complete Solutios for Distributed Multi-Evet Schedulig Rajiv T. Maheswara, Milid Tambe, Emma Bowrig, Joatha P. Pearce, ad Pradeep araatham Uiversity of Souther Califoria

More information

Convention Paper 6764

Convention Paper 6764 Audio Egieerig Society Covetio Paper 6764 Preseted at the 10th Covetio 006 May 0 3 Paris, Frace This covetio paper has bee reproduced from the author's advace mauscript, without editig, correctios, or

More information

The following example will help us understand The Sampling Distribution of the Mean. C1 C2 C3 C4 C5 50 miles 84 miles 38 miles 120 miles 48 miles

The following example will help us understand The Sampling Distribution of the Mean. C1 C2 C3 C4 C5 50 miles 84 miles 38 miles 120 miles 48 miles The followig eample will help us uderstad The Samplig Distributio of the Mea Review: The populatio is the etire collectio of all idividuals or objects of iterest The sample is the portio of the populatio

More information

Output Analysis (2, Chapters 10 &11 Law)

Output Analysis (2, Chapters 10 &11 Law) B. Maddah ENMG 6 Simulatio 05/0/07 Output Aalysis (, Chapters 10 &11 Law) Comparig alterative system cofiguratio Sice the output of a simulatio is radom, the comparig differet systems via simulatio should

More information

Evaluation of Different Fitness Functions for the Evolutionary Testing of an Autonomous Parking System

Evaluation of Different Fitness Functions for the Evolutionary Testing of an Autonomous Parking System Evaluatio of Differet Fitess Fuctios for the Evolutioary Testig of a Autoomous Parkig System Joachim Wegeer 1, Oliver Bühler 2 1 DaimlerChrysler AG, Research ad Techology, Alt-Moabit 96 a, D-1559 Berli,

More information

Measures of Spread and Boxplots Discrete Math, Section 9.4

Measures of Spread and Boxplots Discrete Math, Section 9.4 Measures of Spread ad Boxplots Discrete Math, Sectio 9.4 We start with a example: Example 1: Comparig Mea ad Media Compute the mea ad media of each data set: S 1 = {4, 6, 8, 10, 1, 14, 16} S = {4, 7, 9,

More information

Building Blocks Problem Related to Harmonic Series

Building Blocks Problem Related to Harmonic Series TMME, vol3, o, p.76 Buildig Blocks Problem Related to Harmoic Series Yutaka Nishiyama Osaka Uiversity of Ecoomics, Japa Abstract: I this discussio I give a eplaatio of the divergece ad covergece of ifiite

More information

CS103X: Discrete Structures Homework 4 Solutions

CS103X: Discrete Structures Homework 4 Solutions CS103X: Discrete Structures Homewor 4 Solutios Due February 22, 2008 Exercise 1 10 poits. Silico Valley questios: a How may possible six-figure salaries i whole dollar amouts are there that cotai at least

More information

Biology 171L Environment and Ecology Lab Lab 2: Descriptive Statistics, Presenting Data and Graphing Relationships

Biology 171L Environment and Ecology Lab Lab 2: Descriptive Statistics, Presenting Data and Graphing Relationships Biology 171L Eviromet ad Ecology Lab Lab : Descriptive Statistics, Presetig Data ad Graphig Relatioships Itroductio Log lists of data are ofte ot very useful for idetifyig geeral treds i the data or the

More information

LECTURE 13: Cross-validation

LECTURE 13: Cross-validation LECTURE 3: Cross-validatio Resampli methods Cross Validatio Bootstrap Bias ad variace estimatio with the Bootstrap Three-way data partitioi Itroductio to Patter Aalysis Ricardo Gutierrez-Osua Texas A&M

More information

INVESTMENT PERFORMANCE COUNCIL (IPC) Guidance Statement on Calculation Methodology

INVESTMENT PERFORMANCE COUNCIL (IPC) Guidance Statement on Calculation Methodology Adoptio Date: 4 March 2004 Effective Date: 1 Jue 2004 Retroactive Applicatio: No Public Commet Period: Aug Nov 2002 INVESTMENT PERFORMANCE COUNCIL (IPC) Preface Guidace Statemet o Calculatio Methodology

More information

CHAPTER 3 THE TIME VALUE OF MONEY

CHAPTER 3 THE TIME VALUE OF MONEY CHAPTER 3 THE TIME VALUE OF MONEY OVERVIEW A dollar i the had today is worth more tha a dollar to be received i the future because, if you had it ow, you could ivest that dollar ad ear iterest. Of all

More information

5 Boolean Decision Trees (February 11)

5 Boolean Decision Trees (February 11) 5 Boolea Decisio Trees (February 11) 5.1 Graph Coectivity Suppose we are give a udirected graph G, represeted as a boolea adjacecy matrix = (a ij ), where a ij = 1 if ad oly if vertices i ad j are coected

More information

Automatic Tuning for FOREX Trading System Using Fuzzy Time Series

Automatic Tuning for FOREX Trading System Using Fuzzy Time Series utomatic Tuig for FOREX Tradig System Usig Fuzzy Time Series Kraimo Maeesilp ad Pitihate Soorasa bstract Efficiecy of the automatic currecy tradig system is time depedet due to usig fixed parameters which

More information

Lecture 13. Lecturer: Jonathan Kelner Scribe: Jonathan Pines (2009)

Lecture 13. Lecturer: Jonathan Kelner Scribe: Jonathan Pines (2009) 18.409 A Algorithmist s Toolkit October 27, 2009 Lecture 13 Lecturer: Joatha Keler Scribe: Joatha Pies (2009) 1 Outlie Last time, we proved the Bru-Mikowski iequality for boxes. Today we ll go over the

More information

Domain 1: Designing a SQL Server Instance and a Database Solution

Domain 1: Designing a SQL Server Instance and a Database Solution Maual SQL Server 2008 Desig, Optimize ad Maitai (70-450) 1-800-418-6789 Domai 1: Desigig a SQL Server Istace ad a Database Solutio Desigig for CPU, Memory ad Storage Capacity Requiremets Whe desigig a

More information

Listing terms of a finite sequence List all of the terms of each finite sequence. a) a n n 2 for 1 n 5 1 b) a n for 1 n 4 n 2

Listing terms of a finite sequence List all of the terms of each finite sequence. a) a n n 2 for 1 n 5 1 b) a n for 1 n 4 n 2 74 (4 ) Chapter 4 Sequeces ad Series 4. SEQUENCES I this sectio Defiitio Fidig a Formula for the th Term The word sequece is a familiar word. We may speak of a sequece of evets or say that somethig is

More information

CHAPTER 7: Central Limit Theorem: CLT for Averages (Means)

CHAPTER 7: Central Limit Theorem: CLT for Averages (Means) CHAPTER 7: Cetral Limit Theorem: CLT for Averages (Meas) X = the umber obtaied whe rollig oe six sided die oce. If we roll a six sided die oce, the mea of the probability distributio is X P(X = x) Simulatio:

More information

Your organization has a Class B IP address of 166.144.0.0 Before you implement subnetting, the Network ID and Host ID are divided as follows:

Your organization has a Class B IP address of 166.144.0.0 Before you implement subnetting, the Network ID and Host ID are divided as follows: Subettig Subettig is used to subdivide a sigle class of etwork i to multiple smaller etworks. Example: Your orgaizatio has a Class B IP address of 166.144.0.0 Before you implemet subettig, the Network

More information

.04. This means $1000 is multiplied by 1.02 five times, once for each of the remaining sixmonth

.04. This means $1000 is multiplied by 1.02 five times, once for each of the remaining sixmonth Questio 1: What is a ordiary auity? Let s look at a ordiary auity that is certai ad simple. By this, we mea a auity over a fixed term whose paymet period matches the iterest coversio period. Additioally,

More information

Estimating Probability Distributions by Observing Betting Practices

Estimating Probability Distributions by Observing Betting Practices 5th Iteratioal Symposium o Imprecise Probability: Theories ad Applicatios, Prague, Czech Republic, 007 Estimatig Probability Distributios by Observig Bettig Practices Dr C Lych Natioal Uiversity of Irelad,

More information

Chatpun Khamyat Department of Industrial Engineering, Kasetsart University, Bangkok, Thailand ocpky@hotmail.com

Chatpun Khamyat Department of Industrial Engineering, Kasetsart University, Bangkok, Thailand ocpky@hotmail.com SOLVING THE OIL DELIVERY TRUCKS ROUTING PROBLEM WITH MODIFY MULTI-TRAVELING SALESMAN PROBLEM APPROACH CASE STUDY: THE SME'S OIL LOGISTIC COMPANY IN BANGKOK THAILAND Chatpu Khamyat Departmet of Idustrial

More information

Research Method (I) --Knowledge on Sampling (Simple Random Sampling)

Research Method (I) --Knowledge on Sampling (Simple Random Sampling) Research Method (I) --Kowledge o Samplig (Simple Radom Samplig) 1. Itroductio to samplig 1.1 Defiitio of samplig Samplig ca be defied as selectig part of the elemets i a populatio. It results i the fact

More information

In nite Sequences. Dr. Philippe B. Laval Kennesaw State University. October 9, 2008

In nite Sequences. Dr. Philippe B. Laval Kennesaw State University. October 9, 2008 I ite Sequeces Dr. Philippe B. Laval Keesaw State Uiversity October 9, 2008 Abstract This had out is a itroductio to i ite sequeces. mai de itios ad presets some elemetary results. It gives the I ite Sequeces

More information

Theorems About Power Series

Theorems About Power Series Physics 6A Witer 20 Theorems About Power Series Cosider a power series, f(x) = a x, () where the a are real coefficiets ad x is a real variable. There exists a real o-egative umber R, called the radius

More information

Chapter 5 Unit 1. IET 350 Engineering Economics. Learning Objectives Chapter 5. Learning Objectives Unit 1. Annual Amount and Gradient Functions

Chapter 5 Unit 1. IET 350 Engineering Economics. Learning Objectives Chapter 5. Learning Objectives Unit 1. Annual Amount and Gradient Functions Chapter 5 Uit Aual Amout ad Gradiet Fuctios IET 350 Egieerig Ecoomics Learig Objectives Chapter 5 Upo completio of this chapter you should uderstad: Calculatig future values from aual amouts. Calculatig

More information

1 Computing the Standard Deviation of Sample Means

1 Computing the Standard Deviation of Sample Means Computig the Stadard Deviatio of Sample Meas Quality cotrol charts are based o sample meas ot o idividual values withi a sample. A sample is a group of items, which are cosidered all together for our aalysis.

More information

Analyzing Longitudinal Data from Complex Surveys Using SUDAAN

Analyzing Longitudinal Data from Complex Surveys Using SUDAAN Aalyzig Logitudial Data from Complex Surveys Usig SUDAAN Darryl Creel Statistics ad Epidemiology, RTI Iteratioal, 312 Trotter Farm Drive, Rockville, MD, 20850 Abstract SUDAAN: Software for the Statistical

More information

DAME - Microsoft Excel add-in for solving multicriteria decision problems with scenarios Radomir Perzina 1, Jaroslav Ramik 2

DAME - Microsoft Excel add-in for solving multicriteria decision problems with scenarios Radomir Perzina 1, Jaroslav Ramik 2 Itroductio DAME - Microsoft Excel add-i for solvig multicriteria decisio problems with scearios Radomir Perzia, Jaroslav Ramik 2 Abstract. The mai goal of every ecoomic aget is to make a good decisio,

More information

Subject CT5 Contingencies Core Technical Syllabus

Subject CT5 Contingencies Core Technical Syllabus Subject CT5 Cotigecies Core Techical Syllabus for the 2015 exams 1 Jue 2014 Aim The aim of the Cotigecies subject is to provide a groudig i the mathematical techiques which ca be used to model ad value

More information

CME 302: NUMERICAL LINEAR ALGEBRA FALL 2005/06 LECTURE 8

CME 302: NUMERICAL LINEAR ALGEBRA FALL 2005/06 LECTURE 8 CME 30: NUMERICAL LINEAR ALGEBRA FALL 005/06 LECTURE 8 GENE H GOLUB 1 Positive Defiite Matrices A matrix A is positive defiite if x Ax > 0 for all ozero x A positive defiite matrix has real ad positive

More information

DOME ROOF OF ABOVEGROUND STEEL TANK WITH V=70000 m 3 CAPACITY. Lyubomir A. Zdravkov 1

DOME ROOF OF ABOVEGROUND STEEL TANK WITH V=70000 m 3 CAPACITY. Lyubomir A. Zdravkov 1 DOME ROOF OF ABOVEGROUND STEEL TANK WITH V=70000 m 3 CAPACITY Lyubomir A. Zdrako 1 Abstract: Dome roos are the lightest structure to coer cylidrical taks. Whe they are steel made they could ot be used

More information

Partial Di erential Equations

Partial Di erential Equations Partial Di eretial Equatios Partial Di eretial Equatios Much of moder sciece, egieerig, ad mathematics is based o the study of partial di eretial equatios, where a partial di eretial equatio is a equatio

More information

8 CHAPTER 8: DESIGN OF ONE-WAY SLABS

8 CHAPTER 8: DESIGN OF ONE-WAY SLABS CHAPTER EIGHT DESIGN OF ONE-WAY SLABS 1 8 CHAPTER 8: DESIGN OF ONE-WAY SLABS 8.1 Itrodctio A slab is strctral elemet whose thickess is small compared to its ow legth ad width. Slabs are sally sed i floor

More information

How to read A Mutual Fund shareholder report

How to read A Mutual Fund shareholder report Ivestor BulletI How to read A Mutual Fud shareholder report The SEC s Office of Ivestor Educatio ad Advocacy is issuig this Ivestor Bulleti to educate idividual ivestors about mutual fud shareholder reports.

More information

THE REGRESSION MODEL IN MATRIX FORM. For simple linear regression, meaning one predictor, the model is. for i = 1, 2, 3,, n

THE REGRESSION MODEL IN MATRIX FORM. For simple linear regression, meaning one predictor, the model is. for i = 1, 2, 3,, n We will cosider the liear regressio model i matrix form. For simple liear regressio, meaig oe predictor, the model is i = + x i + ε i for i =,,,, This model icludes the assumptio that the ε i s are a sample

More information

Cantilever Beam Experiment

Cantilever Beam Experiment Mechaical Egieerig Departmet Uiversity of Massachusetts Lowell Catilever Beam Experimet Backgroud A disk drive maufacturer is redesigig several disk drive armature mechaisms. This is the result of evaluatio

More information

*The most important feature of MRP as compared with ordinary inventory control analysis is its time phasing feature.

*The most important feature of MRP as compared with ordinary inventory control analysis is its time phasing feature. Itegrated Productio ad Ivetory Cotrol System MRP ad MRP II Framework of Maufacturig System Ivetory cotrol, productio schedulig, capacity plaig ad fiacial ad busiess decisios i a productio system are iterrelated.

More information

BENEFIT-COST ANALYSIS Financial and Economic Appraisal using Spreadsheets

BENEFIT-COST ANALYSIS Financial and Economic Appraisal using Spreadsheets BENEIT-CST ANALYSIS iacial ad Ecoomic Appraisal usig Spreadsheets Ch. 2: Ivestmet Appraisal - Priciples Harry Campbell & Richard Brow School of Ecoomics The Uiversity of Queeslad Review of basic cocepts

More information

Section 11.3: The Integral Test

Section 11.3: The Integral Test Sectio.3: The Itegral Test Most of the series we have looked at have either diverged or have coverged ad we have bee able to fid what they coverge to. I geeral however, the problem is much more difficult

More information

Definition. A variable X that takes on values X 1, X 2, X 3,...X k with respective frequencies f 1, f 2, f 3,...f k has mean

Definition. A variable X that takes on values X 1, X 2, X 3,...X k with respective frequencies f 1, f 2, f 3,...f k has mean 1 Social Studies 201 October 13, 2004 Note: The examples i these otes may be differet tha used i class. However, the examples are similar ad the methods used are idetical to what was preseted i class.

More information

NEW HIGH PERFORMANCE COMPUTATIONAL METHODS FOR MORTGAGES AND ANNUITIES. Yuri Shestopaloff,

NEW HIGH PERFORMANCE COMPUTATIONAL METHODS FOR MORTGAGES AND ANNUITIES. Yuri Shestopaloff, NEW HIGH PERFORMNCE COMPUTTIONL METHODS FOR MORTGGES ND NNUITIES Yuri Shestopaloff, Geerally, mortgage ad auity equatios do ot have aalytical solutios for ukow iterest rate, which has to be foud usig umerical

More information

0.7 0.6 0.2 0 0 96 96.5 97 97.5 98 98.5 99 99.5 100 100.5 96.5 97 97.5 98 98.5 99 99.5 100 100.5

0.7 0.6 0.2 0 0 96 96.5 97 97.5 98 98.5 99 99.5 100 100.5 96.5 97 97.5 98 98.5 99 99.5 100 100.5 Sectio 13 Kolmogorov-Smirov test. Suppose that we have a i.i.d. sample X 1,..., X with some ukow distributio P ad we would like to test the hypothesis that P is equal to a particular distributio P 0, i.e.

More information

Finding the circle that best fits a set of points

Finding the circle that best fits a set of points Fidig the circle that best fits a set of poits L. MAISONOBE October 5 th 007 Cotets 1 Itroductio Solvig the problem.1 Priciples............................... Iitializatio.............................

More information

Non-life insurance mathematics. Nils F. Haavardsson, University of Oslo and DNB Skadeforsikring

Non-life insurance mathematics. Nils F. Haavardsson, University of Oslo and DNB Skadeforsikring No-life isurace mathematics Nils F. Haavardsso, Uiversity of Oslo ad DNB Skadeforsikrig Mai issues so far Why does isurace work? How is risk premium defied ad why is it importat? How ca claim frequecy

More information

FOUNDATIONS OF MATHEMATICS AND PRE-CALCULUS GRADE 10

FOUNDATIONS OF MATHEMATICS AND PRE-CALCULUS GRADE 10 FOUNDATIONS OF MATHEMATICS AND PRE-CALCULUS GRADE 10 [C] Commuicatio Measuremet A1. Solve problems that ivolve liear measuremet, usig: SI ad imperial uits of measure estimatio strategies measuremet strategies.

More information

Hypergeometric Distributions

Hypergeometric Distributions 7.4 Hypergeometric Distributios Whe choosig the startig lie-up for a game, a coach obviously has to choose a differet player for each positio. Similarly, whe a uio elects delegates for a covetio or you

More information

THE HEIGHT OF q-binary SEARCH TREES

THE HEIGHT OF q-binary SEARCH TREES THE HEIGHT OF q-binary SEARCH TREES MICHAEL DRMOTA AND HELMUT PRODINGER Abstract. q biary search trees are obtaied from words, equipped with the geometric distributio istead of permutatios. The average

More information

Hypothesis testing. Null and alternative hypotheses

Hypothesis testing. Null and alternative hypotheses Hypothesis testig Aother importat use of samplig distributios is to test hypotheses about populatio parameters, e.g. mea, proportio, regressio coefficiets, etc. For example, it is possible to stipulate

More information

COMPARISON OF THE EFFICIENCY OF S-CONTROL CHART AND EWMA-S 2 CONTROL CHART FOR THE CHANGES IN A PROCESS

COMPARISON OF THE EFFICIENCY OF S-CONTROL CHART AND EWMA-S 2 CONTROL CHART FOR THE CHANGES IN A PROCESS COMPARISON OF THE EFFICIENCY OF S-CONTROL CHART AND EWMA-S CONTROL CHART FOR THE CHANGES IN A PROCESS Supraee Lisawadi Departmet of Mathematics ad Statistics, Faculty of Sciece ad Techoology, Thammasat

More information

I. Chi-squared Distributions

I. Chi-squared Distributions 1 M 358K Supplemet to Chapter 23: CHI-SQUARED DISTRIBUTIONS, T-DISTRIBUTIONS, AND DEGREES OF FREEDOM To uderstad t-distributios, we first eed to look at aother family of distributios, the chi-squared distributios.

More information

Baan Service Master Data Management

Baan Service Master Data Management Baa Service Master Data Maagemet Module Procedure UP069A US Documetiformatio Documet Documet code : UP069A US Documet group : User Documetatio Documet title : Master Data Maagemet Applicatio/Package :

More information

1. Introduction. Scheduling Theory

1. Introduction. Scheduling Theory . Itroductio. Itroductio As a idepedet brach of Operatioal Research, Schedulig Theory appeared i the begiig of the 50s. I additio to computer systems ad maufacturig, schedulig theory ca be applied to may

More information

Confidence Intervals. CI for a population mean (σ is known and n > 30 or the variable is normally distributed in the.

Confidence Intervals. CI for a population mean (σ is known and n > 30 or the variable is normally distributed in the. Cofidece Itervals A cofidece iterval is a iterval whose purpose is to estimate a parameter (a umber that could, i theory, be calculated from the populatio, if measuremets were available for the whole populatio).

More information

Chair for Network Architectures and Services Institute of Informatics TU München Prof. Carle. Network Security. Chapter 2 Basics

Chair for Network Architectures and Services Institute of Informatics TU München Prof. Carle. Network Security. Chapter 2 Basics Chair for Network Architectures ad Services Istitute of Iformatics TU Müche Prof. Carle Network Security Chapter 2 Basics 2.4 Radom Number Geeratio for Cryptographic Protocols Motivatio It is crucial to

More information

Matrix Model of Trust Management in P2P Networks

Matrix Model of Trust Management in P2P Networks Matrix Model of Trust Maagemet i P2P Networks Miroslav Novotý, Filip Zavoral Faculty of Mathematics ad Physics Charles Uiversity Prague, Czech Republic miroslav.ovoty@mff.cui.cz Abstract The trust maagemet

More information

Using a genetic algorithm to optimize the total cost for a location-routing-inventory problem in a supply chain with risk pooling

Using a genetic algorithm to optimize the total cost for a location-routing-inventory problem in a supply chain with risk pooling Joural of Applied Operatioal Research (2012) 4(1), 2 13 2012 Tadbir Operatioal Research Group Ltd. All rights reserved. www.tadbir.ca ISSN 1735-8523 (Prit), ISSN 1927-0089 (Olie) Usig a geetic algorithm

More information

NATIONAL SENIOR CERTIFICATE GRADE 11

NATIONAL SENIOR CERTIFICATE GRADE 11 NATIONAL SENIOR CERTIFICATE GRADE MATHEMATICS P EXEMPLAR 007 MARKS: 50 TIME: 3 hours This questio paper cosists of pages, 4 diagram sheets ad a -page formula sheet. Please tur over Mathematics/P DoE/Exemplar

More information

Research Article Allocating Freight Empty Cars in Railway Networks with Dynamic Demands

Research Article Allocating Freight Empty Cars in Railway Networks with Dynamic Demands Discrete Dyamics i Nature ad Society, Article ID 349341, 12 pages http://dx.doi.org/10.1155/2014/349341 Research Article Allocatig Freight Empty Cars i Railway Networks with Dyamic Demads Ce Zhao, Lixig

More information

1 Correlation and Regression Analysis

1 Correlation and Regression Analysis 1 Correlatio ad Regressio Aalysis I this sectio we will be ivestigatig the relatioship betwee two cotiuous variable, such as height ad weight, the cocetratio of a ijected drug ad heart rate, or the cosumptio

More information

Trigonometric Form of a Complex Number. The Complex Plane. axis. ( 2, 1) or 2 i FIGURE 6.44. The absolute value of the complex number z a bi is

Trigonometric Form of a Complex Number. The Complex Plane. axis. ( 2, 1) or 2 i FIGURE 6.44. The absolute value of the complex number z a bi is 0_0605.qxd /5/05 0:45 AM Page 470 470 Chapter 6 Additioal Topics i Trigoometry 6.5 Trigoometric Form of a Complex Number What you should lear Plot complex umbers i the complex plae ad fid absolute values

More information

SWARM INTELLIGENCE OPTIMIZATION OF WORM AND WORM WHEEL DESIGN

SWARM INTELLIGENCE OPTIMIZATION OF WORM AND WORM WHEEL DESIGN VOL. 0, NO. 3, JULY 05 ISSN 89-6608 006-05 Asia Research Publishig Network (ARPN). All rights reserved. SWARM INTELLIGENCE OPTIMIZATION OF WORM AND WORM WHEEL DESIGN M. Chadrasekara, Padmaabha S. ad V.

More information

Center, Spread, and Shape in Inference: Claims, Caveats, and Insights

Center, Spread, and Shape in Inference: Claims, Caveats, and Insights Ceter, Spread, ad Shape i Iferece: Claims, Caveats, ad Isights Dr. Nacy Pfeig (Uiversity of Pittsburgh) AMATYC November 2008 Prelimiary Activities 1. I would like to produce a iterval estimate for the

More information

5.4 Amortization. Question 1: How do you find the present value of an annuity? Question 2: How is a loan amortized?

5.4 Amortization. Question 1: How do you find the present value of an annuity? Question 2: How is a loan amortized? 5.4 Amortizatio Questio 1: How do you fid the preset value of a auity? Questio 2: How is a loa amortized? Questio 3: How do you make a amortizatio table? Oe of the most commo fiacial istrumets a perso

More information

CHAPTER 3 DIGITAL CODING OF SIGNALS

CHAPTER 3 DIGITAL CODING OF SIGNALS CHAPTER 3 DIGITAL CODING OF SIGNALS Computers are ofte used to automate the recordig of measuremets. The trasducers ad sigal coditioig circuits produce a voltage sigal that is proportioal to a quatity

More information

THE ROLE OF EXPORTS IN ECONOMIC GROWTH WITH REFERENCE TO ETHIOPIAN COUNTRY

THE ROLE OF EXPORTS IN ECONOMIC GROWTH WITH REFERENCE TO ETHIOPIAN COUNTRY - THE ROLE OF EXPORTS IN ECONOMIC GROWTH WITH REFERENCE TO ETHIOPIAN COUNTRY BY: FAYE ENSERMU CHEMEDA Ethio-Italia Cooperatio Arsi-Bale Rural developmet Project Paper Prepared for the Coferece o Aual Meetig

More information

Lecture 3. denote the orthogonal complement of S k. Then. 1 x S k. n. 2 x T Ax = ( ) λ x. with x = 1, we have. i = λ k x 2 = λ k.

Lecture 3. denote the orthogonal complement of S k. Then. 1 x S k. n. 2 x T Ax = ( ) λ x. with x = 1, we have. i = λ k x 2 = λ k. 18.409 A Algorithmist s Toolkit September 17, 009 Lecture 3 Lecturer: Joatha Keler Scribe: Adre Wibisoo 1 Outlie Today s lecture covers three mai parts: Courat-Fischer formula ad Rayleigh quotiets The

More information

Lesson 17 Pearson s Correlation Coefficient

Lesson 17 Pearson s Correlation Coefficient Outlie Measures of Relatioships Pearso s Correlatio Coefficiet (r) -types of data -scatter plots -measure of directio -measure of stregth Computatio -covariatio of X ad Y -uique variatio i X ad Y -measurig

More information

Decomposition of Gini and the generalized entropy inequality measures. Abstract

Decomposition of Gini and the generalized entropy inequality measures. Abstract Decompositio of Gii ad the geeralized etropy iequality measures Stéphae Mussard LAMETA Uiversity of Motpellier I Fraçoise Seyte LAMETA Uiversity of Motpellier I Michel Terraza LAMETA Uiversity of Motpellier

More information

Cooley-Tukey. Tukey FFT Algorithms. FFT Algorithms. Cooley

Cooley-Tukey. Tukey FFT Algorithms. FFT Algorithms. Cooley Cooley Cooley-Tuey Tuey FFT Algorithms FFT Algorithms Cosider a legth- sequece x[ with a -poit DFT X[ where Represet the idices ad as +, +, Cooley Cooley-Tuey Tuey FFT Algorithms FFT Algorithms Usig these

More information

The analysis of the Cournot oligopoly model considering the subjective motive in the strategy selection

The analysis of the Cournot oligopoly model considering the subjective motive in the strategy selection The aalysis of the Courot oligopoly model cosiderig the subjective motive i the strategy selectio Shigehito Furuyama Teruhisa Nakai Departmet of Systems Maagemet Egieerig Faculty of Egieerig Kasai Uiversity

More information

Forecasting techniques

Forecasting techniques 2 Forecastig techiques this chapter covers... I this chapter we will examie some useful forecastig techiques that ca be applied whe budgetig. We start by lookig at the way that samplig ca be used to collect

More information

A Fuzzy Model of Software Project Effort Estimation

A Fuzzy Model of Software Project Effort Estimation TJFS: Turkish Joural of Fuzzy Systems (eissn: 309 90) A Official Joural of Turkish Fuzzy Systems Associatio Vol.4, No.2, pp. 68-76, 203 A Fuzzy Model of Software Project Effort Estimatio Oumout Chouseioglou

More information

THE ARITHMETIC OF INTEGERS. - multiplication, exponentiation, division, addition, and subtraction

THE ARITHMETIC OF INTEGERS. - multiplication, exponentiation, division, addition, and subtraction THE ARITHMETIC OF INTEGERS - multiplicatio, expoetiatio, divisio, additio, ad subtractio What to do ad what ot to do. THE INTEGERS Recall that a iteger is oe of the whole umbers, which may be either positive,

More information

Approximating Area under a curve with rectangles. To find the area under a curve we approximate the area using rectangles and then use limits to find

Approximating Area under a curve with rectangles. To find the area under a curve we approximate the area using rectangles and then use limits to find 1.8 Approximatig Area uder a curve with rectagles 1.6 To fid the area uder a curve we approximate the area usig rectagles ad the use limits to fid 1.4 the area. Example 1 Suppose we wat to estimate 1.

More information

Sequences and Series

Sequences and Series CHAPTER 9 Sequeces ad Series 9.. Covergece: Defiitio ad Examples Sequeces The purpose of this chapter is to itroduce a particular way of geeratig algorithms for fidig the values of fuctios defied by their

More information

Solutions to Selected Problems In: Pattern Classification by Duda, Hart, Stork

Solutions to Selected Problems In: Pattern Classification by Duda, Hart, Stork Solutios to Selected Problems I: Patter Classificatio by Duda, Hart, Stork Joh L. Weatherwax February 4, 008 Problem Solutios Chapter Bayesia Decisio Theory Problem radomized rules Part a: Let Rx be the

More information

Heterogeneous Vehicle Routing Problem with profits Dynamic solving by Clustering Genetic Algorithm

Heterogeneous Vehicle Routing Problem with profits Dynamic solving by Clustering Genetic Algorithm IJCSI Iteratioal Joural of Computer Sciece Issues, Vol. 10, Issue 4, No 1, July 2013 ISSN (Prit): 1694-0814 ISSN (Olie): 1694-0784 www.ijcsi.org 247 Heterogeeous Vehicle Routig Problem with profits Dyamic

More information

Incremental calculation of weighted mean and variance

Incremental calculation of weighted mean and variance Icremetal calculatio of weighted mea ad variace Toy Fich faf@cam.ac.uk dot@dotat.at Uiversity of Cambridge Computig Service February 009 Abstract I these otes I eplai how to derive formulae for umerically

More information

Savings and Retirement Benefits

Savings and Retirement Benefits 60 Baltimore Couty Public Schools offers you several ways to begi savig moey through payroll deductios. Defied Beefit Pesio Pla Tax Sheltered Auities ad Custodial Accouts Defied Beefit Pesio Pla Did you

More information

Volatility of rates of return on the example of wheat futures. Sławomir Juszczyk. Rafał Balina

Volatility of rates of return on the example of wheat futures. Sławomir Juszczyk. Rafał Balina Overcomig the Crisis: Ecoomic ad Fiacial Developmets i Asia ad Europe Edited by Štefa Bojec, Josef C. Brada, ad Masaaki Kuboiwa http://www.hippocampus.si/isbn/978-961-6832-32-8/cotets.pdf Volatility of

More information

NATIONAL SENIOR CERTIFICATE GRADE 12

NATIONAL SENIOR CERTIFICATE GRADE 12 NATIONAL SENIOR CERTIFICATE GRADE MATHEMATICS P EXEMPLAR 04 MARKS: 50 TIME: 3 hours This questio paper cosists of 8 pages ad iformatio sheet. Please tur over Mathematics/P DBE/04 NSC Grade Eemplar INSTRUCTIONS

More information

Open Access Non-operating Urban Infrastructure Project Management Maturity Model on Agent Construction Based on the Evolutionary Algorithm

Open Access Non-operating Urban Infrastructure Project Management Maturity Model on Agent Construction Based on the Evolutionary Algorithm Sed Orders for Reprits to reprits@bethamsciece.ae 112 The Ope Costructio ad Buildig Techology Joural, 2015, 9, 112-116 Ope Access No-operatig Urba Ifrastructure Project Maagemet Maturity Model o Aget Costructio

More information

3. Greatest Common Divisor - Least Common Multiple

3. Greatest Common Divisor - Least Common Multiple 3 Greatest Commo Divisor - Least Commo Multiple Defiitio 31: The greatest commo divisor of two atural umbers a ad b is the largest atural umber c which divides both a ad b We deote the greatest commo gcd

More information

Ekkehart Schlicht: Economic Surplus and Derived Demand

Ekkehart Schlicht: Economic Surplus and Derived Demand Ekkehart Schlicht: Ecoomic Surplus ad Derived Demad Muich Discussio Paper No. 2006-17 Departmet of Ecoomics Uiversity of Muich Volkswirtschaftliche Fakultät Ludwig-Maximilias-Uiversität Müche Olie at http://epub.ub.ui-mueche.de/940/

More information

Iran. J. Chem. Chem. Eng. Vol. 26, No.1, 2007. Sensitivity Analysis of Water Flooding Optimization by Dynamic Optimization

Iran. J. Chem. Chem. Eng. Vol. 26, No.1, 2007. Sensitivity Analysis of Water Flooding Optimization by Dynamic Optimization Ira. J. Chem. Chem. Eg. Vol. 6, No., 007 Sesitivity Aalysis of Water Floodig Optimizatio by Dyamic Optimizatio Gharesheiklou, Ali Asghar* + ; Mousavi-Dehghai, Sayed Ali Research Istitute of Petroleum Idustry

More information

Chapter 7: Confidence Interval and Sample Size

Chapter 7: Confidence Interval and Sample Size Chapter 7: Cofidece Iterval ad Sample Size Learig Objectives Upo successful completio of Chapter 7, you will be able to: Fid the cofidece iterval for the mea, proportio, ad variace. Determie the miimum

More information