A MODULAR, WIRELESS NETWORK PLATFORM FOR MONITORING STRUCTURES

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1 A MODULAR, WIRELESS NETWORK PLATFORM FOR MONITORING STRUCTURES E. G. Straser 1, A.S. Kiremidjian 1, T.H. Meng 2, L. Redlefsen 2 1 Ph.D. Candidate and Professor/Director Blume Earthquake Engineering Center Stanford University Stanford CA, Associate Professor and Graduate Student Department of Electrical Engineering Stanford University Stanford, CA ABSTRACT Continuous monitoring of structures has recently become an area of great activity in both the research community and commercial sector. The vast majority of published work has focused on developing algorithms to advance the detection and diagnosis of structures. An equally important task is the establishment of a flexible hardware platform capable of real-time data acquisition. Many of the existing monitoring strategies assume a sophisticated hardware infrastructure. Such an assumption ignores the issues of up-front cost, relative total cost/information benefit, system installation, and life-time maintenance. A multidisciplinary research program at Stanford University, between the Electrical, Mechanical, and Civil Engineering Departments, has developed a hardware platform based on embedded systems and wireless networks to realize the needs of the monitoring community. Our approach has been to determine the desired qualities of a structural monitoring system and design both the hardware to acquire and manage such data and the software to facilitate damage detection diagnosis. A monitoring platform based on embedded systems and wireless packet-switching networks allows for a relatively low-cost, unobtrusive, and maintainable network of Hsensor units.h The embedded system approach pushes intelligence forward in the monitoring system, relieving the data acquisition and computational burden of a central computer and enabling local decision analysis and data processing. A wireless packet-switched network eliminates cabling installation, cable maintenance, and signal degradation over long distances. A functional network of Hsensor unitsh is demonstrated. The hardware and software co-design issues are addressed. The implications for practical deployment using such a network of modular units are discussed. NOMENCLATURE xi(t) = Displacement of floor i at timet IDRi(t) = Inter-story drift ratio for story i at timet x(tt) = Acceleration of floor i at timet Ai(t) = Cumulative Normalized Arias Intensity of floor i at timet 1.0 INTRODUCTION Recent natural disasters such as the earthquakes of January 17, 1994, in Northridge, California, and January 17, 1995, in Kobe, Japan, and hurricanes Andrew and lniki of 1989, have demonstrated the need for near real-time damage assessment of civil structures. Several months after the Northridge event, engineers were still trying to establish the health of steel structures in the greater Los Angeles area. Inspection of damaged buildings and bridges is often time consuming and costly because critical members and connections are difficult to access or are concealed under architectural surface coverings. In addition to earthquakes, service loading and extreme climatic conditions have caused extensive deterioration of the United States' aging infrastructure. Extending the life cycle of our built environment in light of economic constraints will become critical as we enter the next millennium. Long term monitoring systems for deterioration that can be permanently installed or used periodically by roving teams are needed. This research addresses the development of a near real-time damage monitoring system that can evaluate both the extreme event and long term health of civil structures. A cross-disciplinary team of researchers 450

2 from the Civil, Electrical, and Mechanical Engineering Departments at Stanford has been assembled to take a fundamental look at both the hardware and software issues and design novel solutions. 2.0 SOCIETAL BENEFITS The societal benefits of such a system are wide and demonstrable. For natural hazards, such as earthquakes, dissemination of information to emergency response officials on major collapses of structures within minutes can result in lives saved and prudent resource allocation. Quick, accurate estimates of the level of damage to a structure may also be used to indicate loss of function. Structure specific loss of function estimates could provide significant savings to large manufacturing operations. Often such information is delayed due to weather conditions, lack of daylight, inappropriate survey equipment, or lack of access due to terrain obstacles. A long term monitoring system will have the following benefits to structural inventory managers: (1) provide a numerical benchmark to improve the fidelity of the subjective visual inspection, (2) reduce inspection costs by focusing inspection efforts on regions where damage may be located, and (3) increase the manual inspection period by conducting inspections only when needed. 3.0 SYSTEM DESIGN A new approach to the monitoring problem is necessary as conventional test and measurement systems create substantial problems on large civil structures. High per channel costs, extensive cabling, signal deterioration over large distances, maintenance costs, and environmental exposure are common problems. In addition, the velocity of technical innovation in the fields of wireless communication, embedded computation, and sensing is overwhelming conventional systems. A modular damage monitoring system incorporating embedded microprocessors and wireless communications is designed as an effective, inexpensive platform for the measurement and analysis of critical civil structures. Key design factors for our system include: ease of installation, low per unit cost, portability, and broad functionality. These design constraints are manifested in our hardware component choices: embedded microprocessor, radio modem, battery, and data acquisition including sensors. Tight integration of these hardware components and their software complements give birth to our "sensor unit". The "sensor units" form a wireless network that operates in a client-server relationship with the "site master" PC. The site master is the controller of system communication, data storage, and global structural analysis. Data reduction and synthesis is necessary to provide managers and owners with a simple indicator for emergency response. [1] Figure 1. Overview of the Problem Monitoring strategies for extreme events and periodic monitoring have been developed. For extreme events, the sensor units await a significant earthquake and self-actuate. They record data from their sensors until a minimum signal threshold is reached. A system synchronization occurs to align all time series signals. Finally, the recorded data is requested by the site master and transmitted over the radio modem for processing and analysis at the site master. For periodic monitoring, the sensor units are programmed to awake on specified intervals. The sensor units receive instructions from the site master and acquire the sensor data. Time synchronization and data transfer complete the process. The system then returns to sleep mode awaiting an extreme event. Periodic Momtormg Figure 2. Monitoring Strategies Flowchart 4.0 SENSOR UNIT DESIGN The enabling component in this research is the "sensor unit." The operation of each unit is a synthesis of various subsystems. Figure 3 highlights the functional 451

3 relationships and interfaces for the subsystems of the "sensor unit." Development of the sensor unit has led to the filing of a provisional patent through Stanford University. Our sensor unit, comprised of an embedded microprocessor, radio modem, sensors and peripherals, and a battery pack, has been designed. A demonstration network of 5 units has been built for proof of concept testing. The demonstration ntwork communicates with our site master (PC) and d1splays the recorded sensor time histories and signal characteristics. The individual components and design considerations of the sensor unit are described in the following sections. 4.1 SENSOR UNIT HARDWARE DESIGN The microprocessor was chosen based on its wide industrial use, availability of high-level programming environments, wealth of peripherals, and its performance per unit cost. The microprocessor is manufactured by Motorola and is integrated into a single board computer. The microprocessor operates in two power saving modes making it ideal for our monitoring strategies. Commercially available radio modems are used for wireless networking. The radio modems are a direct sequence spread spectrum product that operates in the Industrial, Scientific, and Medical (ISM) band between 902 and 928 MHz. Therefore, they require no FCC license to operate, have decent signal penetration through civil engineering materials, and are greatly immune to interference. They operate at a raw speed of 242 Kbps with each unit running at 19.2 Kpbs, much like a standard modem. This allows roughly 12 units to run at full 19.2 Kpbs speed before saturating the channel. Three independent channels are available. Range testing has indicated that for difficult indoor situations characterized by concrete walls, steel plates, and concrete slabs, that we can expect communication distances of approximately 50 meters. This translates roughly into 10 to 12 stories for most buildings. For line-of-sight settings and most outdoor configurations, we have found that communication distances of 300 meters are common. One obstacle in design was how to synchronize the sensor units. The radio modems do not allow access to the physical media and force communication through a Media Access Control (MAC) layer in firmware. The result is that you cannot precisely control when data is transmitted; you merely ask the modem's firmware to transmit the data and it responds with a success or failure. The solution was to implement a second AM type radio to perform synchronization. In this scenario, each unit is informed, via the radio modem, of an upcoming synchronization "ping" on the AM radio and awaits its arrival to synchronize with other units in the network. Using a second serial port on the site master and an input capture interrupt on the sensor unit's microprocessor, synchronization at the 0.05 millisecond level is achievable. For our frequency spectrum of interest, namely 1-50 Hz, 0.05 millisecond represents a maximum phase delay of less than one degree. Presently, the physical parameter measured is acceleration as it lends itself to the great majority of the existing ambient and forced excitation analysis methods. We have investigated the use of MEMs type capacitive accelerometers by vendors IC Sensors, Kistler, and others. These capacitive accelerometers are attractive in that they have small form factors, adequate dynamic ranges from 0.1 milli-g to 2 or 5 g, and resolutions consistent with extreme event monitoring. Some of the inertial grade accelerometers are being moved down the product chain, by companies like Endevco. Some of these products may be applicable for ambient excitation methods. For our purposes, the accelerometer used is the Kistler 8352A2. It is based on an open loop capacitance measurement made between the proof mass and two plate electrodes on either side. The frequency response is flat from DC to 150 Hz and the device has a resolution of 0.03 milli-g over 0-10 Hz. A significant feature of the Kistler products is their response to DC, allowing these devices to provide information about permanent rotations that may occur during or after an earthquake. Also, the devices are signal conditioned on chip. This allows for great simplification in our data acquisition; we merely supply an unregulated power source and receive a frequency and thermal signal conditioned output between 0-5 volts. With data integrity as our highest concern, we have developed a custom data acquisition sub-system. It consists of a 4-layer printed circuit board with on-board power regulation and conditioning. Data acquisition is performed by a microcontroller based sigma-delta converter manufactured by Harris Semiconductor. The Hl7188 is a 16 bit, 8 channel analog to digital converter that samples all 8 channels up to 240 Hz for line noise immunity and supports the Motorola serial peripheral interface. The on-chip multiplexer, microsequencer, and data buffer relieve the microprocessor from much of the data acquisition burden. The data is brought into the microprocessor on a 1 Mbps synchronous serial interface. The synchronous serial interface will run at a maximum of 5 Mbps, leaving room for expansion to 452

4 more channels per sensor unit. The acquired data is stored for subsequent transmission over the radio modem. I Other Units Or I Site Master Ral Time I Sync Serial Custom AID T1mer Interface Board, 16 bit 1Mbps 8 channel, 240 Hz BUS Wake-Up Wake-Up Interrupt Interrupt Antenna 1 CPU Input Capture AM Motorola Interrupt Sync 6811A8 Radio Radio Modem BUSl BUS DB-91 64KB Direct Async IMBRAM Memory Serial Port Data Storage RAM+ EPROM 19.2 Kbps Memory EEPROM I Proxlink MSU I BUS Figure 3. Sensor Unit Subsystems and Interfaces The most difficult design constraint is electrical power. Battery technology has proven to be the limiting constraint on products from toys to laptops. With few significant advances in battery technology recently, it is difficult to design a system that can survive in permanent installations for several years. The largest concern is wireless communication, which consumes 1 0 to 1 00 times the power of all of the rest of the components. Therefore, our design keeps the radios dormant most of the time. While it would be possible to use AC line power to charge batteries prior to the event, the introduction of any wired connections defeats the wireless contribution. Hence, the real deployment of such a system will likely be limited by efficiencies in wireless communication and batteries. Other sensing and acquisition schemes are currently under review. While acceleration sensing will remain the main input to our system, displacement based sensing is not well suited to monitoring crack and weld fracture. For weld and crack detection, we are investigating ultrasonic and eddy current solutions. Such methods show promise in identifying cracks in welds and other structural connections and lend themselves to battery powered intermittent operation. The sensing methods we implement must be very low in power consumption due to our battery constraints. This restricts the use of non-destructive techniques that require constant monitoring such as acoustic emission. Further, sensing methods that require bulky, expensive interpretive hardware, such as many fiber optic solutions, are not presently effective from a packaging or cost standpoint. 4.2 SITE MASTER HARDWARE The site master is the controller of the networking system at the structure and is the main warehouse of data and system information. It is a commercial personal computer (PC) with a SCSI drive interface to allow for massive data storage. Most of the better equipped Pentium models available today would be sufficient for our design. In a real deployed setting, we anticipate the site master being located in a basement, abutment, or storage locker adjacent to the structure. The site master has two components attached to serial ports: 1) a radio modem for communication with other sensor units and 2) the AM transmitter for sensor unit synchronization. 4.3 SENSOR UNIT SOFTWARE The sensor unit software is divided into several modules: data acquisition, memory management, and communication. The data acquisition module provides routines to manage the AID board allows for configuration (number of channels, channel gains) on the fly. In order to provide a large data logging capability, a memory management module coordinates the transfer of data samples from CPU on-board resources to the memory expansion boards. The communications module controls the radio modem and synchronization radio and performs all packet protocol and error checking. Network presence and coordination is also the responsibility of the communications module. At present, the network is a typical client-server configuration. The site master is the "smart" client that queries each of the sensor units. The sensor units serve file transfer, network ping, and synchronization information to the site master. Events and actions in the sensor module are triggered by a mixture of polling and software and hardware interrupts. In an extreme event scenario, such as an earthquake, the sensor unit is in sleep mode. In sleep mode, the CPU operates under extremely low power and periodically checks its hardware interrupt to determine if there are external events which require attention. Input to the interrupt is provided by always keeping one accelerometer in each unit active and monitoring its voltage level through simple triggering circuits. If the hardware interrupt is triggered, the CPU comes out of sleep mode and begins data acquisition. In a long-term health monitoring scenario, system activation can occur through self-timers on a hardware interrupt when in sleep mode or through radio communication in normal mode. The communications module is triggered primarily by software interrupt. When the units are actively acqumng data, communications requests from the site master or other units are disabled to focus on data acquisition. 453

5 4.4 SITE MASTER SOFTWARE There are two main applications running on our site master PC: network communications software and Matlab. Our network communications software manages the radio network and initiates all traffic on the network. When a data transfer is requested, the site master opens an incoming data file with the unique radio serial number and writes data bytes to the file. After all data is received for all of the sensor units, the communications module schedules the next wake up time and puts all of the sensor modules into sleep mode where they can be activated by significant earthquake excitation. The commercial mathematics package, Matlab, is our primary computational tool. It was chosen mainly for its ease of use, software libraries, and cross-platform compatibility. With all of the data identified by sensor unit radio serial number, and hence spatial location, the data can then be loaded into Matlab for analysis. Section 6 describes analysis software written for Matlab. 5.0 EXPERIMENTAL TESTING Experimental efforts have focused on practical implementation issues. In particular, an experimental test of the Alamosa Canyon Bridge in New Mexico was conducted, in conjunction with Los Alamos National Laboratory (LANL), to determine the effects of environmental factors, such as temperature, on the modal properties of a six span steel girder bridge. The results of the study indicate that temperature effects, and specifically differential heating, cause the bridge to undergo a 7% change in first three modal frequencies over the course of one day. [2] 7.&0! _ j 7.30, First Model Frequency Versus Time of Doy, I 0.5 Ul.0.2 ' o.e o ; <f l 1:15 11:30 13:12 15:13 17:52 20:01 21:20 23:21 1:21 3:11 5:11 7:03 1:22 TIMe of Day - Mean+ 21ig Mun Frequency Value - Mean-2slg "' limp: Top W11tOutdoor Top EutOukloor Figure 4. Natural Variation of Modal Parameters Additionally, heating of the sensor cables created significant routing and measurement problems in the field. Clearly the issue of natural variation of modal parameters, the typical input to damage identification software, needs to be addressed before confidence can be placed in the results of monitoring efforts based on modal properties. 6.0 ANALYSIS SOFTWARE In the event of an earthquake, a report of the gross state of the structure must be completed in a matter of minutes. For this purpose, the use of simple response estimators are employed. Inter-story drift ratios and Arias Intensity are ideal candidates as they have been or can be benchmarked to damage for various structural systems. The inter-story drift ratio is a non-dimensional parameter and has been widely used in the earthquake engineering field as a quick damage estimator. Several studies have used inter-story drift as measure for damage estimation across various structural classes. [3, 4] Inter-story drift ratio is the difference at any time t of the displacement response between two adjacent floors divided by the story height, Of interest is the maximum inter-story drift ratio over the entire time history. The downside of inter-story drift is that it does not take into consideration the energy absorbed by the structure as a result of repeated large amplitude cycles. The Cumulative Normalized Arias Intensity is employed as a measure of the total kinetic energy of each floor. It is normalized to 1 at the end of each time history for each story, Ai(t) = t :L<xitt)} 2 1=0!=end L(xjtt)} 2 t=o Then, Cumulative Normalized Arias Intensity is plotted as a function of time. Initial analysis using these two measures on buildings subjected to the Northridge earthquake shows promising results. When several floors are plotted on the same chart, large deviations between neighboring measurements indicate changes in the kinetic energy and hence, the energy balance for that area of the building. The conclusion drawn is that the energy must have been absorbed as damage incurred by the structure. (1) (2) 454

6 ;; j i :; E 0.2 u " -Roof -8thFIOOf - 3rd Floor 2ndAoor =--=G= = Time (seconds) model correlation, and structural damage identification. DIAMOND provides statistical analysis allowing the user to incorporate multiple data sets and quantify modal parameter distributions. [5] Figure 5. Plot of Normalized Arias Intensity A typical plot is shown in Figure 5 for a 7 story moment resisting concrete frame building. There appears to be no significant difference in the Cumulative Normalized Arias Intensity. However, Figure 6 for a 13 story moment resisting frame, clearly indicates some discrepancy in the Normalized Arias Intensity between the second and six floors which coincides with observed damage. Discrimination of that difference in software and synthesis of the information from interstory drift and other spectral indicators should lead to initial damage indicators for the extreme event monitoring scenario. Algorithms are implemented in Matlab to leverage the periodic monitoring software efforts. ;; j = :; E o.2 u " Sherman Oaks, 13 story MRCF; Longitudinal Direction 1----==--==== Time (oeconds} Figure 6. Plot of Normalized Arias Intensity For periodic monitoring, we are collaborating with researchers at LANL on the development of a combined modal analysis, model correlation, and damage detection application called DIAMOND. DIAMOND is a Matlab toolbox with graphical user interface which can be used for all aspects of structural damage monitoring: modal analysis, finite element Figure 7. DIAMOND Splash Screen 7.0 FUTURE IMPROVEMENTS While the efforts of this project have demonstrated a proof of concept for our system, the road toward deployment is long indeed. A careful redesign of the sensor unit is necessary since most of the components have been improved in the time of this research. Significant upgrades could be made to many of the components. Current price points in late 1997 for a sensor unit are less than $1 K USD. Industrial partners would likely be involved in future versions. CURRENT FUTURE Motorola 6811 CPU PC Proxim Proxlink Modem Proxim RangeLAN2 Data Logging Memory SanDisk Flash Drive Tri-Axial Accelerometers 6-DOF Solid State Alkaline or Lithium lon Super Cap. or Solar Table 1. Future Improvements to Sensor Unit It is important to assess how this technology can gain adoption. For the long term scenario, the concept of a roving team that instruments bridges periodically leverages the cost of the equipment over many bridges. Penetrating the substitute cost of the current biennial inspection will be difficult. Use of monitoring systems as a supplement to visual inspections is likely. For the earthquake scenario, structural monitoring will likely see adoption first in lifelines and manufacturing where quick, accurate estimations of system damage have emergency response and loss of function implications, respectively. 455

7 7.0 CONCLUSIONS AND FUTURE DIRECTIONS The deliverables of this research are a prototype network of "sensor units" and the "site master" computer that can perform extreme event and periodic monitoring. Overall, the field of damage monitoring is still in its infancy. Unresolved issues include the sensor placement, new sensors, and damage algorithms. Current hardware systems are ill-suited to this application and damage algorithms have shown limited success with field data. This research is a step toward the type of instrumentation system required for monitoring civil structures, especially those subjected to extreme events. 8.0 ACKNOWLEDGEMENTS The financial support of this research by Grant CMS from the National Science Foundation is acknowledged. The efforts of Chuck Farrar and Scott Doebling of LANL are acknowledged for the Alamosa Canyon Test and DIAMOND. The Arias Intensity work of Mark Audigier and Hjortur Thrainsson of Stanford University is acknowledged. 9.0 REFERENCES [1] Straser, E.G. and Kiremidjian, A.S., "A Modular, Visual Approach to Damage Monitoring for Civil Structures," Proceedings of the 2nd International Workshop on Structural Control, Hong Kong, December [2] Farrar, C.R., Doebling, S.W., Cornwell, P.J., Straser, E.G., "Variability of Modal Parameters Measured on the Alamosa Canyon Bridge," Proceedings of the 1Sh International Modal Analysis Conference, Orlando, Florida, January [3] ATC-13. Earthquake Damage Evaluation Data for California. Report A TC-13. Applied Technology Council, Redwood City, California, 1985 [4] Risk Management Solutions. Development of a Standardized Earthquake Loss Estimation Methodology. Vol. 2, Technical Manual (prepared for National Institute of Building Sciences), Menlo Park, California, [5] Farrar, C.R. and Doebling, S.W. " A Computer Toolbox for Damage Identification Based on Changes in Vibration Characteristics," Proceedings of International Workshop on Structural Health Monitoring, Palo Alto, CA, Sept

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