PROBABILISTIC DECISION ANALYSIS FOR SEISMIC REHABILITATION OF A REGIONAL BUILDING SYSTEM

Size: px
Start display at page:

Download "PROBABILISTIC DECISION ANALYSIS FOR SEISMIC REHABILITATION OF A REGIONAL BUILDING SYSTEM"

Transcription

1 3 th Worl Conference on Earthquake Engneerng Vancouver, B.C., Canaa August -6, 4 Paper No. 54 PROBABILISTIC DECISION ANALYSIS FOR SEISMIC REHABILITATION OF A REGIONAL BILDING SYSTEM Joonam PARK, Barry GOODNO, Ann BOSTROM 3 an James CRAIG 4 SMMARY Sesmc vulnerablty of bulng structures can be reuce wth approprate rehabltaton schemes. However, ecsons on rehabltaton of structures can epen on multple conflctng crtera such as cost, lfe loss, functonalty, etc. In ths stuy, a framework s evelope to support ecsons on sesmc structural rehabltaton. Three mult-crtera ecson moels are consere: an equvalent cost moel (ECM), mult-attrbute utlty theory (MAT) an Jont Probablty Decson Makng (JPDM). The ecson moels are apple to hosptal systems locate n Memphs, Tennessee, an the preferre rehabltaton optons are entfe base on the two ecson moels. INTRODCTION Sesmc falure or amage to bult systems n regons wth moerate to hgh sesmcty can exact a hgh toll, n lves lost, cost of amage, an other consequences of nterest an concern to stakeholers. However, the sesmc vulnerablty of such systems can be reuce wth approprate rehabltaton schemes (Abrams []). Structural rehabltaton ecsons can epen on multple crtera, such as structural performance, cost, aesthetcs, an functonalty. These crtera often conflct wth one another. There have been efforts on the ecson analyses for sesmc rehabltaton of bulng structures (e.g., Benthen [], Thel [3]). However, an effort to evelop a ecson support framework that takes nto account multple crtera nclung loss of lfe an bulng functon loss, whle utlzng comprehensve probablstc sesmc loss estmaton methos for structures (ether nvual structure or regonal systems) s sparse. In ths paper, a mult-crtera ecson support framework s propose to help ecson makers evaluate rehabltaton schemes, an to support regonal sesmc rehabltaton ecsons. Ths approach employs three mult-crtera ecson moels an equvalent cost moel (ECM), mult-attrbute Ph.D. Canate, School of Cvl an Envronmental Engneerng, Georga Insttute of Technology, Atlanta, GA , SA, Emal: [email protected] Professor, School of Cvl an Envronmental Engneerng, Georga Insttute of Technology, Atlanta, GA , SA, Emal: [email protected] 3 Assocate Professor, School of Publc Polcy, Georga Insttute of Technology, Atlanta, GA , SA, Emal: [email protected] 4 Professor, School of Aerospace Engneerng, Georga Insttute of Technology, Atlanta, GA 333-5, SA, Emal: [email protected]

2 utlty theory (MAT) an jont probablty ecson-makng (JPDM) wthn a flexble confguraton to facltate use by a varety of stakeholers. ECM s a ecson technque n whch non-monetary values are converte nto equvalent monetary values. MAT s a wely use ecson theory that proves nsght nto preferences over a set of alternatves takng nto account the ecson maker s rsk atttues. JPDM s a ecson moel that gves an nex of system performance base on the probablty of achevng a preefne level of consequences for each attrbute of the system. To llustrate the ecson support framework, structures wthn a regonal system are ve nto several classes base on ther confguraton, functon an structural esgn. A sesmc hazar curve s use to represent the uncertanty n the sesmc hazar. Ths hazar curve an structural fraglty curves for the system are combne to obtan the overall probablstc strbuton of structural amage wthn a partcular tme pero. For the ecson analyses, consequences are efne n terms of crtera selecte by the ecson maker. Monte Carlo smulaton s use to estmate probablstcally the antcpate sesmc structural amage an overall consequences to the system, both wthout nterventon an wth alternatve rehabltaton schemes. The ecson analyses prove summary measures of consequences, to entfy the best rehabltaton scheme(s). The framework supports several forms of senstvty analyss, nclung ynamc restructurng of ecson crtera an rehabltaton alternatves, to prove ecson makers wth atonal nsghts nto the consequences of sesmc rehabltaton ecsons. SYSTEM DEFINITION Descrpton of the Bulng Systems Methost Healthcare s a hosptal system base n Memphs, Tennessee, servng the communtes of Eastern Arkansas, West Tennessee, an North Msssspp, an conssts of a number of hosptals an rural health clncs (Methost [4]). Among them, sx hosptal bulngs are selecte an examne to emonstrate the ecson support framework. Table shows the locatons (by zp coe) an the structural types of the hosptals. The locaton nformaton s use to efne the sesmc hazar, an the structural types are use to efne sesmc vulnerablty. Note that the table also shows the structural types base on HAZS bulng classfcatons (HAZS [5]) as loss estmaton n ths stuy follows a HAZS approach. Table Bulng Descrpton Hosptal ZIP Structural Type HAZS Moel Type Methost nversty Hosptal 384 Concrete Shear Wall (M-Rse) CM Methost North Hosptal 388 Concrete Shear Wall (M-Rse) CM T Bowl Hosptal 383 Concrete Shear Wall (M-Rse) CM Methost South Hosptal 386 Concrete Shear Wall (M-Rse) CM Methost Fayette Hosptal 3868 RM (Low Rse) RML Le Bonheur Germantown Hosptal 3838 Concrete Shear Wall (Low-Rse) CL Hazar Curves Earthquakes representatve for the locaton of the system of concern must be efne for use n the amage analyss. Groun moton ntensty s often characterze n terms of spectral splacement (S ) or spectral acceleraton (S a ). However, snce earthquakes are ranom events, whch epen on locaton, t s also necessary to entfy the probablstc characterstcs of the earthquake ntensty as well. sually the lkelhoo of fferent earthquake levels s expresse n terms of probablty of exceeance wthn certan tme lmts, for example, % probablty of exceeance n 5 years. The relatonshp between the earthquake ntensty an ts lkelhoo can be represente by a hazar functon H (Cornell [6], Yun [7]). The annual probablty of exceeance for earthquake ntensty (generally s a or s ) at the ste can be

3 obtane from the hazar functon. Accorng to Cornell [6], the hazar functon can be approxmate to le lnearly on a log-log plot. That s, f the hazar functon s efne n terms of spectral splacement s, the hazar functon can be expresse by the form = P[ S s ] = k k H ( s ) s () Parameters k an k are locaton-specfc. The hazar curve s shown schematcally n Fgure. H Ln(H) k S Ln(S ) H ( s ) Fgure Hazar Curve = P[ S s ] = k PROBABILISTIC EVALATION OF DAMAGE AND LOSSES The antcpate amage state of a bulng or a system of bulngs can be use to estmate sesmc losses. Before conuctng the amage assessment, the sesmc performance objectve for a structure must be specfe. A performance objectve can be efne n terms of the structural performance level an corresponng probablty that the performance level wll be exceee wthn a certan tme lmt (Yun [7]). Accorng to SAC [8], for example, the objectve performance level of a new bulng s that the bulng shoul have less than % chance of amage exceeng Collapse Preventon (CP) n 5 years. In other wors, the sesmc performance level of a structure can be represente n terms of the sesmc amage probablty. A close form soluton s avalable (Cornell [6]) to escrbe structural amage probablstcally. Three major sources of uncertanty n sesmc amage assessment for structural systems are: ) groun moton ntensty; ) structural eman; an 3) structural capacty. There are a number of ways to measure structural eman an capacty, nclung maxmum nter-story rft or varous types of amage nces. The generc expresson for the annual probablty that the eman D excees a specfc value s H D ( ) = p[ D ] = = P[ D S all x P[ D S = x] H( x) = x ] P[ S = x ] s k () The amage probablty (annual probablty of exceeng certan amage level) can then be expresse as

4 P PL = P[ C D] = = all P[ C ] H D P[ C D D = ( ) ] P[ D = ] (3) where C s a generc expresson for structural capacty. The amage probablty (annual probablty of exceeng certan amage level) n Equaton (3) can be approxmate as P PL ( D S + ) Cˆ k H ( S ) exp β β (4) = C where C S ˆ s the spectral splacement corresponng to the mean capacty. Therefore, f the eman hazar curve s efne for the regon an, f the mean capacty can be obtane along wth the spersons of the capacty an the eman, the amage probablty strbuton of a structure locate n a partcular regon can be obtane. The probablstc strbuton of sesmc losses of the structure can then also be obtane from the amage strbuton. It shoul be note that ths close form soluton for the amage strbuton shoul be use for a sngle structure or a class of structures wth the same structural type that are locate relatvely close to each other wthn a regon n whch the sesmcty can be represente by a sngle hazar curve. For aggregaton of losses of fferent types of structures, the close form expresson for the amage strbuton s rarely avalable. To assess expecte losses wthn a partcular tme pero, losses are estmate for a sute of earthquake levels. The amage strbuton of a structure or type of structure from a partcular earthquake level can be obtane from the fraglty curve for the structure. In ths stuy, HAZS [5] s use to estmate structural amage an losses, whch requres that the bulngs be classfe (nto one of 36 categores) base on structural type an heght. Bulng fraglty an capacty curves are use to etermne bulng amage state probablty n HAZS. The fraglty curves for a partcular structural type can be obtane for fferent coe levels (pre, low, moerate, an hgh coe level) n force when the structures were bult (assumng coe complance). HAZS proves an extensve lst of parameters that are neee to generate fraglty curves (for both structural an nonstructural amage) for all 36 types of structures an for four fferent coe levels. Base on these, fraglty curves can be generate for four fferent amage states slght, moerate, extensve, an complete amage. For etale escrpton of the amage states, see HAZS [5]. The HAZS loss estmaton methoology s assumes that there are strong relatonshps between bulng amage an major socal an economc losses (HAZS [5]). Socal losses nclue eath an njury, loss of housng habtablty, short term shelter nees, etc; economc losses nclue structural repar costs, nonstructural repar costs, bulng contents loss, busness nventory loss, loss of bulng functon, ntal rehabltaton cost, etc. The ecson maker chooses whch losses to assess n the rehabltaton ecson analyss, by selectng them as attrbutes of the system. Table shows the losses consere n ths example, where hosptals comprse the system of nterest. These are the system attrbutes for the ecson analyss. Note that only rect losses are consere, an nrect losses are not taken nto account here. The expecte sesmc losses are estmate for four fferent earthquake levels: %, %, 5%, an % probablty of exceeance n 5 years. The loss hazar curve can then be plotte for each kn of loss. For example, Fgure shows the hazar curve for monetary loss for four CM type structures among the structures lste n Table. The expecte loss s then calculate from the area uner the loss hazar curve.

5 Table Losses Consere Category Loss Descrpton Economc Loss Socal Loss Intal Cost Structural Repar Cost Nonstructural Repar Cost Loss of Bulng Contents Relocaton Expenses Loss of Functonalty Death Cost for sesmc rehabltaton or rebulng a new bulng to mprove structural performance Cost for reparng amage to structural components such as beams, columns, jonts, etc. Cost for reparng amage to nonstructural components such as wall parttons, panels, veneers, floors, general mechancal systems, etc. Cost equvalent to the loss of bulng contents such as furnture, equpment (not connecte to the structure), computers, etc. Dsrupton cost an rental cost for usng temporary space n case the bulng must be shut own for repar. Loss of functon for a hosptal may result n atonal human lfe losses ue to lack of mecal actvty. Number of eaths. Injury Number of serously njure people Loss Hazar Curve (Monetary Cost) Loss (,$) Probablty of Exceeance Fgure Monetary Loss Hazar Curve for CM Type Structures Four generc alternatves (sesmc rehabltaton alternatve schemes) are consere for each structural type: ) no acton; ) rehabltaton to lfe safety level; 3) rehabltaton to mmeate occupancy level; an 4) bul a new bulng to comply wth the current coe level. The rehabltaton levels mentone above are, as efne n FEMA [9], the target performance levels of the rehabltaton aganst an earthquake wth % exceeance n 5 years. The cost of sesmc rehabltaton of bulng systems epens on many factors, such as bulng type, earthquake hazar level, esre performance level, occupancy or usage type, etc. The ntal rehabltaton cost for fferent optons are obtane from FEMA ocuments (FEMA [] an FEMA []), whch prove the typcal cost for rehabltaton of exstng structures takng nto account above-mentone factors. For amage assessment of the alternatve systems, a specfc coe level, whch s utlze n HAZS, s assgne to each level of rehabltaton so that the fraglty curves can be obtane for each sesmc alternatve. It s assume that the no acton opton, whch means retanng the exstng structures, correspons to the low coe level. Rehabltaton to lfe safety level opton s

6 assume to be a moerate coe level, an rehabltaton to mmeate occupancy level opton s assume to be a hgh coe level. For the rebul opton, a specal hgh coe s assume because hosptals are classfe as essental facltes. The alternatves an ther coe levels are shown n Table 3 along wth the total floor area of each type of structure. Note that the fraglty curves for CL are use for amage assessment of the sesmc alternatves of a RML type structure, as they are not avalable n HAZS. Str. Type Alternatves CM (4, ft ) CL (4, ft ) RML (4, ft ) No Acton Table 3 HAZS Coe Levels for Alternatve Systems Rehabltaton to Lfe Safety Level Rehabltaton to Immeate Occupancy Level Rebul Low Coe Moerate Coe Hgh Coe Specal Hgh Coe Low Coe Moerate Coe Hgh Coe Specal Hgh Coe Low Coe Moerate Coe (usng CL) Hgh Coe (usng CL) Specal Hgh Coe (usng CL) EQIVALENT COST ANALYSIS For equvalent cost analyses, consequences measure n fferent unts are converte nto a sngle composte measure usually a monetary measure by ntroucng converson factors. For example, one ay of constructon elay can be consere equvalent to three mllon ollars. Ths cost-beneft analyss approach (Keeney []) s calle an equvalent cost analyss n ths stuy because n ecson problems regarng sesmc events, that the only beneft apparent s the mnmzaton of losses (or costs). However, there are several known problems wth ths metho (Keeney []). In orer to use a smple atve metho for estmatng the prce out consequences, several assumptons must be verfe. These assumptons are: ) the monetary value of an attrbute can be etermne wthout conserng other attrbutes; ) the monetary value of an attrbute oes not epen on the overall monetary value level. Even when these assumptons are consere val, many mportant attrbutes such as the value of a lfe are very har (an sometmes consere mpossble or mmoral) to prce out. Moreover, attrbutes may be gnore or not nclue n the analyss when t s har to convert them nto monetary values usng market mechansms (e.g., aesthetcs). Nevertheless, the equvalent cost moel s stll wely use because of ts smplcty n use an straghtforwarness. Among the non-monetary attrbutes, the value of human lfe s very ffcult to etermne an has been hghly ebate. Moreover, the value of human lfe wll have a we range of values epenng on the ecson context. Accorng to FEMA [], the typcal value of a statstcal lfe ranges from $. mllon to $8 mllon per lfe (other authors have foun fferent ranges). In ths stuy, the ecson analyss wll not be performe wth fxe values for non-monetary attrbutes, but nstea wth a range of values ($.m to $8m for the value of a statstcal human lfe), to nvestgate the effects of the equvalent monetary values on the ecson. The equvalent cost for the loss of functon s expresse n terms of the functon recovery tme (ays) per, square feet. For example, f one ay of loss of functon of a hosptal wth the total floor area of, square feet s estmate to cost $,, the equvalent cost for 5 ays of loss of functon of 5, square feet hosptal woul be $,5,. Obvously, ths rough approach for etermnaton of equvalent cost for loss of functon nees future refnement. As escrbe n Table 3, the value of loss of functon shoul be taken nto account that the loss of functon may result n atonal loss of lfe. In ths stuy, senstvty analyss wll be performe for the value of loss of functon rangng from $ to $5, for one ay of loss of functon of a hosptal per, square feet. Table 4 shows the baselne values for the non-monetary attrbutes for the ecson analyss. Note that the value of njury s estmate (cruely) at 3% of the value of a statstcal lfe loss.

7 Table 4 Baselne Values for Non-monetary Attrbutes Attrbute Equvalent Cost Value of Death $5,, / person Value of Injury $,5, / person Value of Loss of Functon $, / ay to recover /, ft If a temporal trae-off s consere n performng a ecson analyss, future costs may be scounte to net present value, f the ecson maker consers them less panful. If we have a tme stream of costs (c, c,, c T ), the total net present value of the cost can be expresse as follows: c npv = c T t t t= ( + λ) (5) where λ s the effectve pero-to-pero scount rate. Accorng to FEMA [], several fferent approaches have been use to estmate the scount rate for publc nvestments, wth the resultng scount rates rangng between 3% an %. Determnaton of the tme pero T also epens on the ecson maker. In ths stuy, a 3-year tme pero an wth 6% scount rate are use as baselne values, wth senstvty analyses on tme peros rangng from years to 5 years, an scount rates from 3% to %. Note that the probablty of exceeance of fferent earthquake levels must be calbrate to be consstent wth the tme pero. Fgure 3 shows the loss hazar curves for each type of structure wth the expecte equvalent losses corresponng to fferent earthquake levels. Note that the losses shown n ths fgure are the equvalent cost, where non-monetary attrbutes are prce out. Table 5 shows the expecte earthquake losses for each rehabltaton scheme, whch are obtane from the loss hazar curves, along wth the ntal costs for the rehabltaton, followe by the total expecte losses (for 3 years of tme pero). Ths specfc expecte equvalent cost analyss ncates that none of the rehabltaton actons are justfe. 7 Loss Curve (Money Cost) 8 Loss Curve (Money Cost) 8 Loss Curve (Money Cost) Loss ($M) No Acton Rehab LS Rehab IO Rebul % 5% % 5% Prob. of Exceeance Loss ($M) 6 4 No Acton Rehab LS Rehab IO Rebul % 5% % 5% Prob. of Exceeance Loss ($M) No Acton Rehab LS Rehab IO Rebul % 5% % 5% Prob. of Exceeance (a) CM Structures (4 unts) (b) CL Structure ( unt) (c) RML Structure ( unt) Fgure 3 Loss Hazar Curves for CM type Structures (4 unts) Fgure 4 shows the senstvty plots for fferent values of functon loss (the senstvty plots for other varables are not shown n ths paper). Among the varables nclue n ths ecson analyss, the relatve fferences of the expecte equvalent costs of the alternatve systems are most senstve to the change of the value of functon loss. Note that the slopes (senstvty) of the optons are fferent an ecson reverses occur when the value of functon loss excees approxmately $,.

8 Table 5 Expecte Equvalent Costs ($Mllon) of Dfferent Rehabltaton Schemes Intal Cost (Rehab. Cost) Expecte Earthquake Loss (n 3 years) Total Expecte Cost (n 3 years) No Acton CM Rehab LS (4 unts) Rehab IO Rebul CL ( unt) RML ( unt) No Acton.9.9 Rehab LS Rehab IO Rebul No Acton Rehab LS Rehab IO Rebul Senstvty to Value of Functon (for CM) Senstvty to Value of Functon (for CL) Senstvty to Value of Functon (for RML) Equvalent Cost ($M) No Acton 4 Rehab LS Rehab IO New..4.6 Value of Functon Loss ($M/ay/,sq.ft) Equvalent Cost ($M) No Acton 6 Rehab LS 4 Rehab IO New..4.6 Value of Functon Loss ($M/ay/,sq.ft) Equvalent Cost ($M) No Acton 6 Rehab LS 4 Rehab IO New..4.6 Value of Functon Loss ($M/ay/,sq.ft) (a) CM Structures (b) CL Structure (c) RML Structure Fgure 4 Senstvty Plot for Value of Functon Loss MLTI-ATTRIBTE TILITY ANALYSIS Mult-attrbute utlty theory (MAT), whch has been wely use n the fel of ecson analyss, ncorporates ecson makers unque preferences for multple attrbutes, thus allowng ncorporaton of multple crtera nto a ecson. Preferences (or values) are measure n terms of utlty functons, whch can be lnear or nonlnear. For multple crtera ecson-makng problems, a mult-attrbute utlty functon s generate as a functon of a number of sngle utlty functons, conserng ther relatve mpact on the overall value as well as ther nteractons. The etals of the theory an the technques for utlty elctaton are well escrbe n the lterature (Keeney []). If uncertanty s nvolve n the problem, the expecte utlty s obtane for each alternatve an the alternatve wth hghest expecte utlty s the one wth hghest prorty. To examne the effect of nclung rsk atttues, a set of utlty functons s assume n ths stuy. From the fact that ecson makers ten to be rsk seekng (.e., the shape of the utlty functon s convex) for losses (Kahneman [3]), four rsk seekng utlty functons are assume as shown n Fgure 5. Note that loss of functon s measure as ays of loss of functon multple by the sze of the faclty (n terms of, ft ). In constructon of the mult-attrbute utlty functon, the utlty functons are assume to be atve for smplcty, an the scalng factors are efne as shown n Table 6. For the purpose of comparng these results wth the equvalent cost analyss, the scalng factors for the attrbutes

9 are etermne such that the ratos of the scalng factors are same as the ratos of the equvalent costs for the maxmum values. These scalng factors are presente as baselne values an are subjecte to senstvty analyss. The mult-attrbute utlty functon can then be formulate as u x, x, x, x ) = k u ( x ) + k u ( x ) + k u ( x ) + k u ( ) (6) ( x4 where u( x, x, x3, x4 ) s the mult-attrbute utlty functon, k s are the scalng factors an u ( x ) s are the margnal utlty functons of the attrbutes. Table 6 Scalng Factors of Attrbutes Attrbutes Mn. Value Max. Value Scalng Factor Monetary Cost ($M) k =. Functon Loss ( ays, ft ) 5, k =.6 Death 3 k 3 =.8 Injury 55 k 4 = y = e -.5x.6.4. y = e -.x Cost (Mllon $).8 (a) Monetary Cost Loss of Functon (ays*area/,) (b) Functon Loss.6.4. y = e -.5x.6.4. y = e -.x Death (c) Death Injury () Injury Fgure 5 tlty Functons (Rsk-Seekng) for Attrbutes tlty functons are measure over the range of total consequences, an shoul be performe on the system as a whole. Accorngly, the alternatve systems are efne n terms of the combnatons of the alternatves for each nvual structure or each type of structures. In ths example, eght combnatons of the alternatve systems are analyze, usng two alternatves for each type of structure (here the best two optons from the equvalent cost analyss). The expecte utlty of a rehabltaton scheme can be calculate as

10 E =... u( x, x, x, x ) f X, X, X, X ( x, x, x, x x x x x (7) x x ) where E s the expecte utlty of th scheme an f X, X, X 3, X 4 ( x, x, x3, x4 ) s the jont probablty ensty functon for the rehabltaton alternatves corresponng to the th scheme. Same as the equvalent cost analyss, the Monte-Carlo smulaton s performe to obtan the expecte utlty of each alternatve combnaton scheme. Table 7 shows the lst of combnatons of the alternatve systems; expecte utltes for these combnatons are gven as well. Note that the expecte utltes are obtane for two fferent values of the scalng factors for loss of functon, k. These two values of k are obtane such that they are consstent wth the case that the values of functon loss n the equvalent cost analyss are $, an $,, respectvely. The utlty hazar curves for selecte combnatons of the alternatve systems are shown n Fgure 6 showng the expecte utltes corresponng to fferent earthquake levels. Ths analyss suggests that none of the rehabltaton actons are justfe, as n the equvalent cost analyss, unless the relatve mportance of the functon loss s very hgh. Wth a scalng factor for functon loss (k ) of.75, scheme T s preferre. It shoul be note that although T4 omnates n both plots n Fgure 6,T4 s less preferre than ether T or T overall because T an T are preferre (because of low ntal costs) over T4 when there s no earthquake, whch s hghly lkely. Table 7 Expecte tltes of the Combnatons of the Sesmc Alternatve Schemes (wth rskseekng utlty functons) Scheme CM CL RML Expecte tlty k =.6 k =.75 T No Acton No Acton No Acton T No Acton No Acton Rehab LS T3 No Acton Rehab LS No Acton T4 No Acton Rehab LS Rehab LS T5 Rehab LS No Acton No Acton.9.96 T6 Rehab LS No Acton Rehab LS T7 Rehab LS Rehab LS No Acton T8 Rehab LS Rehab LS Rehab LS tlty Hazar Curve tlty Hazar Curve Expecte tlty T T. T3 T4.% 5.%.% 5.% Probablty of Exceeance Expecte tlty T T T3 T4.% 5.%.% 5.% Probablty of Exceeance (a) wth k =.6 (b) wth k =.75 Fgure 6 tlty Hazar Curves for Selecte Combnaton Schemes

11 To nvestgate the effect of fferent rsk atttues, the analyss s performe wth the rsk-averse utlty functons are shown n Fgure 7. Note that the same set of scalng factors s use for the analyss. The analyss s performe n the same manner as escrbe above. Overall expecte utltes of the fferent rehabltaton schemes are shown n Table 8. In contrast to the results wth rsk-seekng utlty functons, the analyss ncates that T5~T8 are preferre over T~T4 for both sets of scalng values. Conserng the fact that CM type structures consttute the majorty of the system of nterest (at least n terms of square footage), the analyss shows, as one woul expect, that rehabltaton actons are generally recommene when ecson makers are rsk averse y = -(/) 3 x y = -(/5) 3 x Cost (Mllon $)..8 (a) Monetary Cost Loss of Functon..8 (b) Loss of Functon.6.4. y = -(/3) 3 x y = -(/55) 3 x Death (c) Death Injury () Injury Fgure 7 tlty Functons (Rsk-Averse) for Attrbutes Table 8 Expecte utltes for alternatve rehabltaton schemes (wth rsk-averse utlty functons) Scheme CM CL RML Expecte tlty k =.6 k =.75 T No Acton No Acton No Acton T No Acton No Acton Rehab LS T3 No Acton Rehab LS No Acton T4 No Acton Rehab LS Rehab LS T5 Rehab LS No Acton No Acton T6 Rehab LS No Acton Rehab LS T7 Rehab LS Rehab LS No Acton T8 Rehab LS Rehab LS Rehab LS JOINT PROBABILITY DECISION MAKING Bante [4] evelope Jont Probablstc Decson Makng (JPDM) as a tool for mult-objectve optmzaton an prouct selecton problems n aerospace system esgn. In ths metho, a jont probablty strbuton functon for multple objectves can be obtane ether mathematcally or from

12 emprcal strbuton functons. sng jont probablty strbuton functons, a unque value calle Probablty of Success (POS), whch ncates the probablty of satsfyng specfc ecson makng objectves, can be calculate to prove a barometer wth whch the ecson can be mae. The POS can be mathematcally expresse as follows. POS = P{ ( z = zmax zmn mn... z z max ) ( z mn z z zn max f Z Z Z ( z, z,... z N ) z z N zn mn max z )... ( z N Nmn z N z Nmax ) } (8) where, z s the crteron value, f Z (,,... ) Z... Z z z z N N s the jont probablty functon of the crtera, an z mn an z max are the mnmum an maxmum range of the objectve crteron value, respectvely. In JPDM the alternatve wth maxmum probable postve consequences s preferre. Note that because JPDM requres specfyng specfc threshols for success (ecson crtera values), the preferre ecson n JPDM may not be same as that resultng from an expecte value (or expecte utlty) approach. Value nformaton (.e., success) n JPDM s expresse n terms of the crtera values. The consequental fference between ECA an MAT s the ncorporaton of rsk atttues. JPDM s a categorcal approach, n that the ecson maker specfes at the outset what values of each ecson attrbute of nterest (.e., crteron values) wll be consere success (or acceptable ). JPDM s esgne to help the ecson maker maxmze the jont probablty of attanng success (.e., acceptablty) on all attrbutes of nterest. Table 9 shows the crteron values assume for the JPDM analyss n ths example. These ncate the range of the consequences the ecson maker consers successful (.e., acceptable). The Monte Carlo Smulaton s use to calculate the probabltes of success (POS) of the alternatve schemes an fferent earthquake levels. Table shows the overall expecte values of POS for alternatve rehabltaton schemes. In ths analyss, JPDM gves hgh prorty to T6 an T8. Table 9 Crteron Values for JPDM Attrbutes Mnmum Crteron Value Maxmum Crteron Value Monetary Cost ($M) Functon Loss ( ays, ft ) Death Injury Table POSs of the Combnatons of the Sesmc Alternatve Schemes Scheme CM CL RML POS T No Acton No Acton No Acton.93 T No Acton No Acton Rehab LS.94 T3 No Acton Rehab LS No Acton.9334 T4 No Acton Rehab LS Rehab LS.944 T5 Rehab LS No Acton No Acton.954 T6 Rehab LS No Acton Rehab LS.9644 T7 Rehab LS Rehab LS No Acton.9548 T8 Rehab LS Rehab LS Rehab LS.9694

13 CONCLSIONS Ths paper outlnes a ecson framework that ncorporates state of the art earthquake engneerng nformaton an ecson maker preferences nto a flexble tool to support earthquake rsk mtgaton ecsons. Three ecson moels are use to prove nsght nto the value of system nterventons to reuce earthquake rsks: ) an equvalent cost moel, ) mult-attrbute utlty theory an 3) jont probablty ecson makng. To llustrate the kns of nsghts the framework can prove, t s apple to a set of hosptals n Memphs, Tennessee, to assess the relatve value of structural rehabltaton optons. Wth the assume baselne values of scount rate, value of functon loss, value of eath an njury, an tme pero, an the assume set of utlty functons, no rehabltaton acton s justfe n ether the equvalent cost analyss or the utlty analyss. However, senstvty analyss suggests that the RML structure n the hosptal system shoul be rehabltate to lfe safety level f the relatve mportance of hosptal functon loss s hgh. If the ecson maker s rsk-averse, the analyss ncates rehabltaton actons are generally justfe. Wth JPDM, two rehabltaton schemes are preferre. The results llustrate the kns of nsghts the system coul prove to ecson makers, recognzng that any such analyses requre sgnfcant assumptons, whch shoul be probe wth approprate techncal support. ACKNOWLEDGMENT Ths research was sponsore n part by the M-Amerca Earthquake Center through Natonal Scence Founaton Grant EEC However, all results, conclusons an fnngs are solely those of the authors an o not necessarly represent those of the sponsors. REFERENCES. Abrams, D. P., Elnasha, A. E., an Beavers, J. E. (), A New Engneerng Paragm: Consequence-Base Engneerng, M-Amerca Earthquake Center. Benthen, M. an von Wnterfelt, D. (), A Decson Analyss Framework for Improvng the Sesmc Safety of Apartment Bulngs wth Tuckuner Parkng Structures, workng paper, School of Polcy, Plannng an Development, nversty of Southern Calforna,.S.A. 3. Thel, C. C. an Hagen, S, H. (998), Economc Analyss of Earthquake Retroft Optons: an Applcaton to Wele Steel Moment Frames, the Structural Desgn of Tall Bulngs, v7, pp Methost Healthcare System (3), Methost Healthcare, < 5. HAZS (999), Techncal Manual, Feeral Emergency Management Agency, Washngton, D.C. 6. Cornell, C. A., Jalayer, J., Hamburger, R. O., an Foutch, D. A. (), Probablstc Bass for SAC Feeral Emergency Management Agency Steel Moment Frame Guelnes, Journal of Structural Engneerng, v8, n4, pp Yun, S.Y., Hamburger, O. O., Cornell, C. A., an Foutch, D. A. (), Sesmc Performance Evaluaton for Steel Moment Frames, Journal of Structural Engneerng, v8, n4, pp SAC Jont Venture (), Recommene Sesmc Desgn Crtera for New Steel Moment Frame Bulngs, Report No. FEMA-35, Feeral Emergency Management Agency, Washngton, D.C. 9. Feeral Emergency Management Agency FEMA (999), Example Applcatons of the NEHRP Guelnes for the Sesmc Rehabltaton of Bulngs, Report 76, Washngton, D.C.,.S.A.. Feeral Emergency Management Agency FEMA (99), A Beneft-Cost Moel for the Sesmc Rehabltaton of Bulngs, Report 7, Washngton, D.C.,.S.A.. Feeral Emergency Management Agency FEMA (995), Typcal Costs for Sesmc Rehabltaton of Exstng Bulngs, Vol. Summary, Report 56, Washngton, D.C.,.S.A.

14 . Keeney, R. L. an Raffa, H. (993), Decsons wth Multple Objectves: Preferences an Value Traeoffs, Cambrge nversty Press 3. Kahneman, D. an Tverssky, A. (979), Prospect Theory: An Analyss of Decson uner Rsk, Econometrca, v47, n3, pp Bante, O. (), A Probablstc Mult-Crtera Decson Makng Technque for Conceptual an Prelmnary Aerospace Systems Desgn, Ph.D Thess, Georga Insttute of Technology

Fragility Based Rehabilitation Decision Analysis

Fragility Based Rehabilitation Decision Analysis .171. Fraglty Based Rehabltaton Decson Analyss Cagdas Kafal Graduate Student, School of Cvl and Envronmental Engneerng, Cornell Unversty Research Supervsor: rcea Grgoru, Professor Summary A method s presented

More information

DEGREES OF EQUIVALENCE IN A KEY COMPARISON 1 Thang H. L., Nguyen D. D. Vietnam Metrology Institute, Address: 8 Hoang Quoc Viet, Hanoi, Vietnam

DEGREES OF EQUIVALENCE IN A KEY COMPARISON 1 Thang H. L., Nguyen D. D. Vietnam Metrology Institute, Address: 8 Hoang Quoc Viet, Hanoi, Vietnam DEGREES OF EQUIVALECE I A EY COMPARISO Thang H. L., guyen D. D. Vetnam Metrology Insttute, Aress: 8 Hoang Quoc Vet, Hano, Vetnam Abstract: In an nterlaboratory key comparson, a ata analyss proceure for

More information

EXAMPLE PROBLEMS SOLVED USING THE SHARP EL-733A CALCULATOR

EXAMPLE PROBLEMS SOLVED USING THE SHARP EL-733A CALCULATOR EXAMPLE PROBLEMS SOLVED USING THE SHARP EL-733A CALCULATOR 8S CHAPTER 8 EXAMPLES EXAMPLE 8.4A THE INVESTMENT NEEDED TO REACH A PARTICULAR FUTURE VALUE What amount must you nvest now at 4% compoune monthly

More information

benefit is 2, paid if the policyholder dies within the year, and probability of death within the year is ).

benefit is 2, paid if the policyholder dies within the year, and probability of death within the year is ). REVIEW OF RISK MANAGEMENT CONCEPTS LOSS DISTRIBUTIONS AND INSURANCE Loss and nsurance: When someone s subject to the rsk of ncurrng a fnancal loss, the loss s generally modeled usng a random varable or

More information

Risk-based Fatigue Estimate of Deep Water Risers -- Course Project for EM388F: Fracture Mechanics, Spring 2008

Risk-based Fatigue Estimate of Deep Water Risers -- Course Project for EM388F: Fracture Mechanics, Spring 2008 Rsk-based Fatgue Estmate of Deep Water Rsers -- Course Project for EM388F: Fracture Mechancs, Sprng 2008 Chen Sh Department of Cvl, Archtectural, and Envronmental Engneerng The Unversty of Texas at Austn

More information

Number of Levels Cumulative Annual operating Income per year construction costs costs ($) ($) ($) 1 600,000 35,000 100,000 2 2,200,000 60,000 350,000

Number of Levels Cumulative Annual operating Income per year construction costs costs ($) ($) ($) 1 600,000 35,000 100,000 2 2,200,000 60,000 350,000 Problem Set 5 Solutons 1 MIT s consderng buldng a new car park near Kendall Square. o unversty funds are avalable (overhead rates are under pressure and the new faclty would have to pay for tself from

More information

Can Auto Liability Insurance Purchases Signal Risk Attitude?

Can Auto Liability Insurance Purchases Signal Risk Attitude? Internatonal Journal of Busness and Economcs, 2011, Vol. 10, No. 2, 159-164 Can Auto Lablty Insurance Purchases Sgnal Rsk Atttude? Chu-Shu L Department of Internatonal Busness, Asa Unversty, Tawan Sheng-Chang

More information

The OC Curve of Attribute Acceptance Plans

The OC Curve of Attribute Acceptance Plans The OC Curve of Attrbute Acceptance Plans The Operatng Characterstc (OC) curve descrbes the probablty of acceptng a lot as a functon of the lot s qualty. Fgure 1 shows a typcal OC Curve. 10 8 6 4 1 3 4

More information

An Alternative Way to Measure Private Equity Performance

An Alternative Way to Measure Private Equity Performance An Alternatve Way to Measure Prvate Equty Performance Peter Todd Parlux Investment Technology LLC Summary Internal Rate of Return (IRR) s probably the most common way to measure the performance of prvate

More information

On the Optimal Marginal Rate of Income Tax

On the Optimal Marginal Rate of Income Tax On the Optmal Margnal Rate of Income Tax Gareth D Myles Insttute for Fscal Stues an Unversty of Exeter June 999 Abstract: The paper shows that n the quas-lnear moel of ncome taxaton, the optmal margnal

More information

ANALYZING THE RELATIONSHIPS BETWEEN QUALITY, TIME, AND COST IN PROJECT MANAGEMENT DECISION MAKING

ANALYZING THE RELATIONSHIPS BETWEEN QUALITY, TIME, AND COST IN PROJECT MANAGEMENT DECISION MAKING ANALYZING THE RELATIONSHIPS BETWEEN QUALITY, TIME, AND COST IN PROJECT MANAGEMENT DECISION MAKING Matthew J. Lberatore, Department of Management and Operatons, Vllanova Unversty, Vllanova, PA 19085, 610-519-4390,

More information

Present Values and Accumulations

Present Values and Accumulations Present Values an Accumulatons ANGUS S. MACDONALD Volume 3, pp. 1331 1336 In Encyclopea Of Actuaral Scence (ISBN -47-84676-3) Ete by Jozef L. Teugels an Bjørn Sunt John Wley & Sons, Lt, Chchester, 24 Present

More information

Answer: A). There is a flatter IS curve in the high MPC economy. Original LM LM after increase in M. IS curve for low MPC economy

Answer: A). There is a flatter IS curve in the high MPC economy. Original LM LM after increase in M. IS curve for low MPC economy 4.02 Quz Solutons Fall 2004 Multple-Choce Questons (30/00 ponts) Please, crcle the correct answer for each of the followng 0 multple-choce questons. For each queston, only one of the answers s correct.

More information

A DYNAMIC CRASHING METHOD FOR PROJECT MANAGEMENT USING SIMULATION-BASED OPTIMIZATION. Michael E. Kuhl Radhamés A. Tolentino-Peña

A DYNAMIC CRASHING METHOD FOR PROJECT MANAGEMENT USING SIMULATION-BASED OPTIMIZATION. Michael E. Kuhl Radhamés A. Tolentino-Peña Proceedngs of the 2008 Wnter Smulaton Conference S. J. Mason, R. R. Hll, L. Mönch, O. Rose, T. Jefferson, J. W. Fowler eds. A DYNAMIC CRASHING METHOD FOR PROJECT MANAGEMENT USING SIMULATION-BASED OPTIMIZATION

More information

Risk Model of Long-Term Production Scheduling in Open Pit Gold Mining

Risk Model of Long-Term Production Scheduling in Open Pit Gold Mining Rsk Model of Long-Term Producton Schedulng n Open Pt Gold Mnng R Halatchev 1 and P Lever 2 ABSTRACT Open pt gold mnng s an mportant sector of the Australan mnng ndustry. It uses large amounts of nvestments,

More information

LIFETIME INCOME OPTIONS

LIFETIME INCOME OPTIONS LIFETIME INCOME OPTIONS May 2011 by: Marca S. Wagner, Esq. The Wagner Law Group A Professonal Corporaton 99 Summer Street, 13 th Floor Boston, MA 02110 Tel: (617) 357-5200 Fax: (617) 357-5250 www.ersa-lawyers.com

More information

PSYCHOLOGICAL RESEARCH (PYC 304-C) Lecture 12

PSYCHOLOGICAL RESEARCH (PYC 304-C) Lecture 12 14 The Ch-squared dstrbuton PSYCHOLOGICAL RESEARCH (PYC 304-C) Lecture 1 If a normal varable X, havng mean µ and varance σ, s standardsed, the new varable Z has a mean 0 and varance 1. When ths standardsed

More information

APPLICATION OF PROBE DATA COLLECTED VIA INFRARED BEACONS TO TRAFFIC MANEGEMENT

APPLICATION OF PROBE DATA COLLECTED VIA INFRARED BEACONS TO TRAFFIC MANEGEMENT APPLICATION OF PROBE DATA COLLECTED VIA INFRARED BEACONS TO TRAFFIC MANEGEMENT Toshhko Oda (1), Kochro Iwaoka (2) (1), (2) Infrastructure Systems Busness Unt, Panasonc System Networks Co., Ltd. Saedo-cho

More information

Module 2 LOSSLESS IMAGE COMPRESSION SYSTEMS. Version 2 ECE IIT, Kharagpur

Module 2 LOSSLESS IMAGE COMPRESSION SYSTEMS. Version 2 ECE IIT, Kharagpur Module LOSSLESS IMAGE COMPRESSION SYSTEMS Lesson 3 Lossless Compresson: Huffman Codng Instructonal Objectves At the end of ths lesson, the students should be able to:. Defne and measure source entropy..

More information

IMPACT ANALYSIS OF A CELLULAR PHONE

IMPACT ANALYSIS OF A CELLULAR PHONE 4 th ASA & μeta Internatonal Conference IMPACT AALYSIS OF A CELLULAR PHOE We Lu, 2 Hongy L Bejng FEAonlne Engneerng Co.,Ltd. Bejng, Chna ABSTRACT Drop test smulaton plays an mportant role n nvestgatng

More information

iavenue iavenue i i i iavenue iavenue iavenue

iavenue iavenue i i i iavenue iavenue iavenue Saratoga Systems' enterprse-wde Avenue CRM system s a comprehensve web-enabled software soluton. Ths next generaton system enables you to effectvely manage and enhance your customer relatonshps n both

More information

The Development of Web Log Mining Based on Improve-K-Means Clustering Analysis

The Development of Web Log Mining Based on Improve-K-Means Clustering Analysis The Development of Web Log Mnng Based on Improve-K-Means Clusterng Analyss TngZhong Wang * College of Informaton Technology, Luoyang Normal Unversty, Luoyang, 471022, Chna [email protected] Abstract.

More information

Cluster Analysis. Cluster Analysis

Cluster Analysis. Cluster Analysis Cluster Analyss Cluster Analyss What s Cluster Analyss? Types of Data n Cluster Analyss A Categorzaton of Maor Clusterng Methos Parttonng Methos Herarchcal Methos Densty-Base Methos Gr-Base Methos Moel-Base

More information

ECONOMICS OF PLANT ENERGY SAVINGS PROJECTS IN A CHANGING MARKET Douglas C White Emerson Process Management

ECONOMICS OF PLANT ENERGY SAVINGS PROJECTS IN A CHANGING MARKET Douglas C White Emerson Process Management ECONOMICS OF PLANT ENERGY SAVINGS PROJECTS IN A CHANGING MARKET Douglas C Whte Emerson Process Management Abstract Energy prces have exhbted sgnfcant volatlty n recent years. For example, natural gas prces

More information

On the computation of the capital multiplier in the Fortis Credit Economic Capital model

On the computation of the capital multiplier in the Fortis Credit Economic Capital model On the computaton of the captal multpler n the Forts Cret Economc Captal moel Jan Dhaene 1, Steven Vuffel 2, Marc Goovaerts 1, Ruben Oleslagers 3 Robert Koch 3 Abstract One of the key parameters n the

More information

An Efficient Recovery Algorithm for Coverage Hole in WSNs

An Efficient Recovery Algorithm for Coverage Hole in WSNs An Effcent Recover Algorthm for Coverage Hole n WSNs Song Ja 1,*, Wang Balng 1, Peng Xuan 1 School of Informaton an Electrcal Engneerng Harbn Insttute of Technolog at Weha, Shanong, Chna Automatc Test

More information

SPECIALIZED DAY TRADING - A NEW VIEW ON AN OLD GAME

SPECIALIZED DAY TRADING - A NEW VIEW ON AN OLD GAME August 7 - August 12, 2006 n Baden-Baden, Germany SPECIALIZED DAY TRADING - A NEW VIEW ON AN OLD GAME Vladmr Šmovć 1, and Vladmr Šmovć 2, PhD 1 Faculty of Electrcal Engneerng and Computng, Unska 3, 10000

More information

INVESTIGATION OF VEHICULAR USERS FAIRNESS IN CDMA-HDR NETWORKS

INVESTIGATION OF VEHICULAR USERS FAIRNESS IN CDMA-HDR NETWORKS 21 22 September 2007, BULGARIA 119 Proceedngs of the Internatonal Conference on Informaton Technologes (InfoTech-2007) 21 st 22 nd September 2007, Bulgara vol. 2 INVESTIGATION OF VEHICULAR USERS FAIRNESS

More information

APPLICATION OF BINARY DIVISION ALGORITHM FOR IMAGE ANALYSIS AND CHANGE DETECTION TO IDENTIFY THE HOTSPOTS IN MODIS IMAGES

APPLICATION OF BINARY DIVISION ALGORITHM FOR IMAGE ANALYSIS AND CHANGE DETECTION TO IDENTIFY THE HOTSPOTS IN MODIS IMAGES APPLICATION OF BINARY DIVISION ALGORITHM FOR IMAGE ANALYSIS AND CHANGE DETECTION TO IDENTIFY THE HOTSPOTS IN MODIS IMAGES Harsh Kumar G R * an Dharmenra Sngh ([email protected], [email protected]) Department

More information

SQUARE Project: Cost/Benefit Analysis Framework for Information Security Improvement Projects in Small Companies

SQUARE Project: Cost/Benefit Analysis Framework for Information Security Improvement Projects in Small Companies SQUARE Project: Cost/Beneft Analyss Framework for Informaton Securty Improvement Projects n Small Companes System Qualty Requrements Engneerng (SQUARE) Team Nck (Nng) Xe Nancy R. Mead, Advsor Contrbutors:

More information

Course outline. Financial Time Series Analysis. Overview. Data analysis. Predictive signal. Trading strategy

Course outline. Financial Time Series Analysis. Overview. Data analysis. Predictive signal. Trading strategy Fnancal Tme Seres Analyss Patrck McSharry [email protected] www.mcsharry.net Trnty Term 2014 Mathematcal Insttute Unversty of Oxford Course outlne 1. Data analyss, probablty, correlatons, vsualsaton

More information

An MILP model for planning of batch plants operating in a campaign-mode

An MILP model for planning of batch plants operating in a campaign-mode An MILP model for plannng of batch plants operatng n a campagn-mode Yanna Fumero Insttuto de Desarrollo y Dseño CONICET UTN [email protected] Gabrela Corsano Insttuto de Desarrollo y Dseño

More information

Overview of monitoring and evaluation

Overview of monitoring and evaluation 540 Toolkt to Combat Traffckng n Persons Tool 10.1 Overvew of montorng and evaluaton Overvew Ths tool brefly descrbes both montorng and evaluaton, and the dstncton between the two. What s montorng? Montorng

More information

A Stigmergy Approach for Open Source Software Developer Community Simulation

A Stigmergy Approach for Open Source Software Developer Community Simulation A Stgmergy Approach for Open Source Software Developer Communty Smulaton Xaohu Cu, Justn Beaver, Jm Treawell an Thomas Potok Oak Rge Natonal Laboratory Oak Rge, TN 37831 Laura Pullum Lockhee Martn Corporaton

More information

INTEGRATED DATA FLOW AND RISK AGGREGATION FOR CONSEQUENCE-BASED RISK MANAGEMENT OF SEISMIC REGIONAL LOSSES

INTEGRATED DATA FLOW AND RISK AGGREGATION FOR CONSEQUENCE-BASED RISK MANAGEMENT OF SEISMIC REGIONAL LOSSES INTEGRATED DATA FLOW AND RISK AGGREGATION FOR CONSEQUENCE-BASED RISK MANAGEMENT OF SEISMIC REGIONAL LOSSES Joshua Steelman, Junho Song, and Jerome F. Hajjar A Report of the 1241 Newmark Cvl Engneerng Laboratory

More information

IDENTIFICATION AND CORRECTION OF A COMMON ERROR IN GENERAL ANNUITY CALCULATIONS

IDENTIFICATION AND CORRECTION OF A COMMON ERROR IN GENERAL ANNUITY CALCULATIONS IDENTIFICATION AND CORRECTION OF A COMMON ERROR IN GENERAL ANNUITY CALCULATIONS Chrs Deeley* Last revsed: September 22, 200 * Chrs Deeley s a Senor Lecturer n the School of Accountng, Charles Sturt Unversty,

More information

SPEE Recommended Evaluation Practice #6 Definition of Decline Curve Parameters Background:

SPEE Recommended Evaluation Practice #6 Definition of Decline Curve Parameters Background: SPEE Recommended Evaluaton Practce #6 efnton of eclne Curve Parameters Background: The producton hstores of ol and gas wells can be analyzed to estmate reserves and future ol and gas producton rates and

More information

Forecasting the Demand of Emergency Supplies: Based on the CBR Theory and BP Neural Network

Forecasting the Demand of Emergency Supplies: Based on the CBR Theory and BP Neural Network 700 Proceedngs of the 8th Internatonal Conference on Innovaton & Management Forecastng the Demand of Emergency Supples: Based on the CBR Theory and BP Neural Network Fu Deqang, Lu Yun, L Changbng School

More information

Robust Design of Public Storage Warehouses. Yeming (Yale) Gong EMLYON Business School

Robust Design of Public Storage Warehouses. Yeming (Yale) Gong EMLYON Business School Robust Desgn of Publc Storage Warehouses Yemng (Yale) Gong EMLYON Busness School Rene de Koster Rotterdam school of management, Erasmus Unversty Abstract We apply robust optmzaton and revenue management

More information

CS 2750 Machine Learning. Lecture 3. Density estimation. CS 2750 Machine Learning. Announcements

CS 2750 Machine Learning. Lecture 3. Density estimation. CS 2750 Machine Learning. Announcements Lecture 3 Densty estmaton Mlos Hauskrecht [email protected] 5329 Sennott Square Next lecture: Matlab tutoral Announcements Rules for attendng the class: Regstered for credt Regstered for audt (only f there

More information

DEFINING %COMPLETE IN MICROSOFT PROJECT

DEFINING %COMPLETE IN MICROSOFT PROJECT CelersSystems DEFINING %COMPLETE IN MICROSOFT PROJECT PREPARED BY James E Aksel, PMP, PMI-SP, MVP For Addtonal Informaton about Earned Value Management Systems and reportng, please contact: CelersSystems,

More information

Activity Scheduling for Cost-Time Investment Optimization in Project Management

Activity Scheduling for Cost-Time Investment Optimization in Project Management PROJECT MANAGEMENT 4 th Internatonal Conference on Industral Engneerng and Industral Management XIV Congreso de Ingenería de Organzacón Donosta- San Sebastán, September 8 th -10 th 010 Actvty Schedulng

More information

Damage detection in composite laminates using coin-tap method

Damage detection in composite laminates using coin-tap method Damage detecton n composte lamnates usng con-tap method S.J. Km Korea Aerospace Research Insttute, 45 Eoeun-Dong, Youseong-Gu, 35-333 Daejeon, Republc of Korea [email protected] 45 The con-tap test has the

More information

Kiel Institute for World Economics Duesternbrooker Weg 120 24105 Kiel (Germany) Kiel Working Paper No. 1120

Kiel Institute for World Economics Duesternbrooker Weg 120 24105 Kiel (Germany) Kiel Working Paper No. 1120 Kel Insttute for World Economcs Duesternbrooker Weg 45 Kel (Germany) Kel Workng Paper No. Path Dependences n enture Captal Markets by Andrea Schertler July The responsblty for the contents of the workng

More information

ERP Software Selection Using The Rough Set And TPOSIS Methods

ERP Software Selection Using The Rough Set And TPOSIS Methods ERP Software Selecton Usng The Rough Set And TPOSIS Methods Under Fuzzy Envronment Informaton Management Department, Hunan Unversty of Fnance and Economcs, No. 139, Fengln 2nd Road, Changsha, 410205, Chna

More information

Study on Model of Risks Assessment of Standard Operation in Rural Power Network

Study on Model of Risks Assessment of Standard Operation in Rural Power Network Study on Model of Rsks Assessment of Standard Operaton n Rural Power Network Qngj L 1, Tao Yang 2 1 Qngj L, College of Informaton and Electrcal Engneerng, Shenyang Agrculture Unversty, Shenyang 110866,

More information

An Integrated Approach of AHP-GP and Visualization for Software Architecture Optimization: A case-study for selection of architecture style

An Integrated Approach of AHP-GP and Visualization for Software Architecture Optimization: A case-study for selection of architecture style Internatonal Journal of Scentfc & Engneerng Research Volume 2, Issue 7, July-20 An Integrated Approach of AHP-GP and Vsualzaton for Software Archtecture Optmzaton: A case-study for selecton of archtecture

More information

How Sets of Coherent Probabilities May Serve as Models for Degrees of Incoherence

How Sets of Coherent Probabilities May Serve as Models for Degrees of Incoherence 1 st Internatonal Symposum on Imprecse Probabltes and Ther Applcatons, Ghent, Belgum, 29 June 2 July 1999 How Sets of Coherent Probabltes May Serve as Models for Degrees of Incoherence Mar J. Schervsh

More information

Exact GP Schema Theory for Headless Chicken Crossover and Subtree Mutation

Exact GP Schema Theory for Headless Chicken Crossover and Subtree Mutation Exact GP Schema Theory for Healess Chcken Crossover an Subtree Mutaton Rccaro Pol School of Computer Scence The Unversty of Brmngham Brmngham, B5 TT, UK [email protected] Ncholas F. McPhee Dvson of Scence

More information

Stock Profit Patterns

Stock Profit Patterns Stock Proft Patterns Suppose a share of Farsta Shppng stock n January 004 s prce n the market to 56. Assume that a September call opton at exercse prce 50 costs 8. A September put opton at exercse prce

More information

An Empirical Study of Search Engine Advertising Effectiveness

An Empirical Study of Search Engine Advertising Effectiveness An Emprcal Study of Search Engne Advertsng Effectveness Sanjog Msra, Smon School of Busness Unversty of Rochester Edeal Pnker, Smon School of Busness Unversty of Rochester Alan Rmm-Kaufman, Rmm-Kaufman

More information

The Greedy Method. Introduction. 0/1 Knapsack Problem

The Greedy Method. Introduction. 0/1 Knapsack Problem The Greedy Method Introducton We have completed data structures. We now are gong to look at algorthm desgn methods. Often we are lookng at optmzaton problems whose performance s exponental. For an optmzaton

More information

Multiple-Period Attribution: Residuals and Compounding

Multiple-Period Attribution: Residuals and Compounding Multple-Perod Attrbuton: Resduals and Compoundng Our revewer gave these authors full marks for dealng wth an ssue that performance measurers and vendors often regard as propretary nformaton. In 1994, Dens

More information

= i δ δ s n and PV = a n = 1 v n = 1 e nδ

= i δ δ s n and PV = a n = 1 v n = 1 e nδ Exam 2 s Th March 19 You are allowe 7 sheets of notes an a calculator 41) An mportant fact about smple nterest s that for smple nterest A(t) = K[1+t], the amount of nterest earne each year s constant =

More information

Efficient Project Portfolio as a tool for Enterprise Risk Management

Efficient Project Portfolio as a tool for Enterprise Risk Management Effcent Proect Portfolo as a tool for Enterprse Rsk Management Valentn O. Nkonov Ural State Techncal Unversty Growth Traectory Consultng Company January 5, 27 Effcent Proect Portfolo as a tool for Enterprse

More information

Causal, Explanatory Forecasting. Analysis. Regression Analysis. Simple Linear Regression. Which is Independent? Forecasting

Causal, Explanatory Forecasting. Analysis. Regression Analysis. Simple Linear Regression. Which is Independent? Forecasting Causal, Explanatory Forecastng Assumes cause-and-effect relatonshp between system nputs and ts output Forecastng wth Regresson Analyss Rchard S. Barr Inputs System Cause + Effect Relatonshp The job of

More information

NEURO-FUZZY INFERENCE SYSTEM FOR E-COMMERCE WEBSITE EVALUATION

NEURO-FUZZY INFERENCE SYSTEM FOR E-COMMERCE WEBSITE EVALUATION NEURO-FUZZY INFERENE SYSTEM FOR E-OMMERE WEBSITE EVALUATION Huan Lu, School of Software, Harbn Unversty of Scence and Technology, Harbn, hna Faculty of Appled Mathematcs and omputer Scence, Belarusan State

More information

A Novel Methodology of Working Capital Management for Large. Public Constructions by Using Fuzzy S-curve Regression

A Novel Methodology of Working Capital Management for Large. Public Constructions by Using Fuzzy S-curve Regression Novel Methodology of Workng Captal Management for Large Publc Constructons by Usng Fuzzy S-curve Regresson Cheng-Wu Chen, Morrs H. L. Wang and Tng-Ya Hseh Department of Cvl Engneerng, Natonal Central Unversty,

More information

Selecting Best Employee of the Year Using Analytical Hierarchy Process

Selecting Best Employee of the Year Using Analytical Hierarchy Process J. Basc. Appl. Sc. Res., 5(11)72-76, 2015 2015, TextRoad Publcaton ISSN 2090-4304 Journal of Basc and Appled Scentfc Research www.textroad.com Selectng Best Employee of the Year Usng Analytcal Herarchy

More information

Calculation of Sampling Weights

Calculation of Sampling Weights Perre Foy Statstcs Canada 4 Calculaton of Samplng Weghts 4.1 OVERVIEW The basc sample desgn used n TIMSS Populatons 1 and 2 was a two-stage stratfed cluster desgn. 1 The frst stage conssted of a sample

More information

Forecasting the Direction and Strength of Stock Market Movement

Forecasting the Direction and Strength of Stock Market Movement Forecastng the Drecton and Strength of Stock Market Movement Jngwe Chen Mng Chen Nan Ye [email protected] [email protected] [email protected] Abstract - Stock market s one of the most complcated systems

More information

Statistical Methods to Develop Rating Models

Statistical Methods to Develop Rating Models Statstcal Methods to Develop Ratng Models [Evelyn Hayden and Danel Porath, Österrechsche Natonalbank and Unversty of Appled Scences at Manz] Source: The Basel II Rsk Parameters Estmaton, Valdaton, and

More information

Calculating the high frequency transmission line parameters of power cables

Calculating the high frequency transmission line parameters of power cables < ' Calculatng the hgh frequency transmsson lne parameters of power cables Authors: Dr. John Dcknson, Laboratory Servces Manager, N 0 RW E B Communcatons Mr. Peter J. Ncholson, Project Assgnment Manager,

More information

Abstract. 260 Business Intelligence Journal July IDENTIFICATION OF DEMAND THROUGH STATISTICAL DISTRIBUTION MODELING FOR IMPROVED DEMAND FORECASTING

Abstract. 260 Business Intelligence Journal July IDENTIFICATION OF DEMAND THROUGH STATISTICAL DISTRIBUTION MODELING FOR IMPROVED DEMAND FORECASTING 260 Busness Intellgence Journal July IDENTIFICATION OF DEMAND THROUGH STATISTICAL DISTRIBUTION MODELING FOR IMPROVED DEMAND FORECASTING Murphy Choy Mchelle L.F. Cheong School of Informaton Systems, Sngapore

More information

An Interest-Oriented Network Evolution Mechanism for Online Communities

An Interest-Oriented Network Evolution Mechanism for Online Communities An Interest-Orented Network Evoluton Mechansm for Onlne Communtes Cahong Sun and Xaopng Yang School of Informaton, Renmn Unversty of Chna, Bejng 100872, P.R. Chna {chsun,yang}@ruc.edu.cn Abstract. Onlne

More information

High Performance Latent Dirichlet Allocation for Text Mining

High Performance Latent Dirichlet Allocation for Text Mining Hgh Performance Latent Drchlet Allocaton for Text Mnng A thess submtte for Degree of Doctor of Phlosophy By Department of Electronc an Computer Engneerng School of Engneerng an Desgn Brunel Unversty September

More information

Criminal Justice System on Crime *

Criminal Justice System on Crime * On the Impact of the NSW Crmnal Justce System on Crme * Dr Vasls Sarafds, Dscplne of Operatons Management and Econometrcs Unversty of Sydney * Ths presentaton s based on jont work wth Rchard Kelaher 1

More information

Effective Network Defense Strategies against Malicious Attacks with Various Defense Mechanisms under Quality of Service Constraints

Effective Network Defense Strategies against Malicious Attacks with Various Defense Mechanisms under Quality of Service Constraints Effectve Network Defense Strateges aganst Malcous Attacks wth Varous Defense Mechansms under Qualty of Servce Constrants Frank Yeong-Sung Ln Department of Informaton Natonal Tawan Unversty Tape, Tawan,

More information

SIMULATION OPTIMIZATION: APPLICATIONS IN RISK MANAGEMENT

SIMULATION OPTIMIZATION: APPLICATIONS IN RISK MANAGEMENT Internatonal Journal of Informaton Technology & Decson Makng Vol. 7, No. 4 (2008) 571 587 c World Scentfc Publshng Company SIMULATION OPTIMIZATION: APPLICATIONS IN RISK MANAGEMENT MARCO BETTER and FRED

More information

Brigid Mullany, Ph.D University of North Carolina, Charlotte

Brigid Mullany, Ph.D University of North Carolina, Charlotte Evaluaton And Comparson Of The Dfferent Standards Used To Defne The Postonal Accuracy And Repeatablty Of Numercally Controlled Machnng Center Axes Brgd Mullany, Ph.D Unversty of North Carolna, Charlotte

More information

14.74 Lecture 5: Health (2)

14.74 Lecture 5: Health (2) 14.74 Lecture 5: Health (2) Esther Duflo February 17, 2004 1 Possble Interventons Last tme we dscussed possble nterventons. Let s take one: provdng ron supplements to people, for example. From the data,

More information

MODELING DYNAMICS OF POST-DISASTER RECOVERY. Technology, Texas Tech University, Box 43107, Lubbock, Texas 79409-3107, Email: ali.nejat@ttu.

MODELING DYNAMICS OF POST-DISASTER RECOVERY. Technology, Texas Tech University, Box 43107, Lubbock, Texas 79409-3107, Email: ali.nejat@ttu. 2200 MODELING DYNAMICS OF POST-DISASTER RECOVERY Al NEJAT 1 and Ivan DAMNJANOVIC 2 1 Assstant Professor, Department of Constructon Engneerng and Engneerng Technology, Texas Tech Unversty, Box 43107, Lubbock,

More information

On the Optimal Control of a Cascade of Hydro-Electric Power Stations

On the Optimal Control of a Cascade of Hydro-Electric Power Stations On the Optmal Control of a Cascade of Hydro-Electrc Power Statons M.C.M. Guedes a, A.F. Rbero a, G.V. Smrnov b and S. Vlela c a Department of Mathematcs, School of Scences, Unversty of Porto, Portugal;

More information

Distributed Strategic Learning with Application to Network Security

Distributed Strategic Learning with Application to Network Security Amercan Control Conference on O'Farrell Street San Francsco CA USA June 9 - July Dstrbute Strategc Learnng wth Applcaton to Network Securty Quanyan Zhu Hamou Tembne an Tamer Başar Abstract We conser n

More information

1 De nitions and Censoring

1 De nitions and Censoring De ntons and Censorng. Survval Analyss We begn by consderng smple analyses but we wll lead up to and take a look at regresson on explanatory factors., as n lnear regresson part A. The mportant d erence

More information

Fuzzy TOPSIS Method in the Selection of Investment Boards by Incorporating Operational Risks

Fuzzy TOPSIS Method in the Selection of Investment Boards by Incorporating Operational Risks , July 6-8, 2011, London, U.K. Fuzzy TOPSIS Method n the Selecton of Investment Boards by Incorporatng Operatonal Rsks Elssa Nada Mad, and Abu Osman Md Tap Abstract Mult Crtera Decson Makng (MCDM) nvolves

More information

BERNSTEIN POLYNOMIALS

BERNSTEIN POLYNOMIALS On-Lne Geometrc Modelng Notes BERNSTEIN POLYNOMIALS Kenneth I. Joy Vsualzaton and Graphcs Research Group Department of Computer Scence Unversty of Calforna, Davs Overvew Polynomals are ncredbly useful

More information

PRIVATE SCHOOL CHOICE: THE EFFECTS OF RELIGIOUS AFFILIATION AND PARTICIPATION

PRIVATE SCHOOL CHOICE: THE EFFECTS OF RELIGIOUS AFFILIATION AND PARTICIPATION PRIVATE SCHOOL CHOICE: THE EFFECTS OF RELIIOUS AFFILIATION AND PARTICIPATION Danny Cohen-Zada Department of Economcs, Ben-uron Unversty, Beer-Sheva 84105, Israel Wllam Sander Department of Economcs, DePaul

More information

Power-of-Two Policies for Single- Warehouse Multi-Retailer Inventory Systems with Order Frequency Discounts

Power-of-Two Policies for Single- Warehouse Multi-Retailer Inventory Systems with Order Frequency Discounts Power-of-wo Polces for Sngle- Warehouse Mult-Retaler Inventory Systems wth Order Frequency Dscounts José A. Ventura Pennsylvana State Unversty (USA) Yale. Herer echnon Israel Insttute of echnology (Israel)

More information

Trust Network and Trust Community Clustering based on Shortest Path Analysis for E-commerce

Trust Network and Trust Community Clustering based on Shortest Path Analysis for E-commerce Internatonal Journal of u- an e- Serce, Scence an Technology Trust Network an Trust Communty Clusterng base on Shortest Path Analyss for E-commerce Shaozhong Zhang 1, Jungan Chen 1, Haong Zhong 2, Zhaox

More information

THE LOAD PLANNING PROBLEM FOR LESS-THAN-TRUCKLOAD MOTOR CARRIERS AND A SOLUTION APPROACH. Professor Naoto Katayama* and Professor Shigeru Yurimoto*

THE LOAD PLANNING PROBLEM FOR LESS-THAN-TRUCKLOAD MOTOR CARRIERS AND A SOLUTION APPROACH. Professor Naoto Katayama* and Professor Shigeru Yurimoto* 7th Internatonal Symposum on Logstcs THE LOAD PLAIG PROBLEM FOR LESS-THA-TRUCKLOAD MOTOR CARRIERS AD A SOLUTIO APPROACH Professor aoto Katayama* an Professor Shgeru Yurmoto* * Faculty of Dstrbuton an Logstcs

More information

THE DISTRIBUTION OF LOAN PORTFOLIO VALUE * Oldrich Alfons Vasicek

THE DISTRIBUTION OF LOAN PORTFOLIO VALUE * Oldrich Alfons Vasicek HE DISRIBUION OF LOAN PORFOLIO VALUE * Oldrch Alfons Vascek he amount of captal necessary to support a portfolo of debt securtes depends on the probablty dstrbuton of the portfolo loss. Consder a portfolo

More information

A Secure Password-Authenticated Key Agreement Using Smart Cards

A Secure Password-Authenticated Key Agreement Using Smart Cards A Secure Password-Authentcated Key Agreement Usng Smart Cards Ka Chan 1, Wen-Chung Kuo 2 and Jn-Chou Cheng 3 1 Department of Computer and Informaton Scence, R.O.C. Mltary Academy, Kaohsung 83059, Tawan,

More information

The Use of Analytics for Claim Fraud Detection Roosevelt C. Mosley, Jr., FCAS, MAAA Nick Kucera Pinnacle Actuarial Resources Inc.

The Use of Analytics for Claim Fraud Detection Roosevelt C. Mosley, Jr., FCAS, MAAA Nick Kucera Pinnacle Actuarial Resources Inc. Paper 1837-2014 The Use of Analytcs for Clam Fraud Detecton Roosevelt C. Mosley, Jr., FCAS, MAAA Nck Kucera Pnnacle Actuaral Resources Inc., Bloomngton, IL ABSTRACT As t has been wdely reported n the nsurance

More information

行 政 院 國 家 科 學 委 員 會 補 助 專 題 研 究 計 畫 成 果 報 告 期 中 進 度 報 告

行 政 院 國 家 科 學 委 員 會 補 助 專 題 研 究 計 畫 成 果 報 告 期 中 進 度 報 告 行 政 院 國 家 科 學 委 員 會 補 助 專 題 研 究 計 畫 成 果 報 告 期 中 進 度 報 告 畫 類 別 : 個 別 型 計 畫 半 導 體 產 業 大 型 廠 房 之 設 施 規 劃 計 畫 編 號 :NSC 96-2628-E-009-026-MY3 執 行 期 間 : 2007 年 8 月 1 日 至 2010 年 7 月 31 日 計 畫 主 持 人 : 巫 木 誠 共 同

More information

A Practical Study of Regenerating Codes for Peer-to-Peer Backup Systems

A Practical Study of Regenerating Codes for Peer-to-Peer Backup Systems A Practcal Stuy of Regeneratng Coes for Peer-to-Peer Backup Systems Alessanro Dumnuco an Ernst Bersack EURECOM Sopha Antpols, France {umnuco,bersack}@eurecom.fr Abstract In strbute storage systems, erasure

More information

The Safety Board recommends that the Penn Central Transportation. Company and the American Railway Engineering Association revise

The Safety Board recommends that the Penn Central Transportation. Company and the American Railway Engineering Association revise V. RECOWNDATONS 4.! The Safety Board recommends that the Penn Central Transportaton Company and the Amercan Ralway Engneerng Assocaton revse ther track nspecton and mantenance standards or recommended

More information