Showing papers in "Computer-aided Civil and Infrastructure Engineering in 2006"

Structural Model Updating and Health Monitoring with Incomplete Modal Data Using Gibbs Sampler

Abstract: A new Bayesian model updating approach is presented for linear structural models. It is based on the Gibbs sampler, a stochastic simulation method that decomposes the uncertain model parameters into three groups, so that the direct sampling from any one group is possible when conditional on the other groups and the incomplete modal data. This means that even if the number of uncertain parameters is large, the effective dimension for the Gibbs sampler is always three and so high-dimensional parameter spaces that are fatal to most sampling techniques are handled by the method, making it more practical for health monitoring of real structures. The approach also inherits the advantages of Bayesian techniques: it not only updates the optimal estimate of the structural parameters but also updates the associated uncertainties. The approach is illustrated by applying it to two examples of structural health monitoring problems, in which the goal is to detect and quantify any damage using incomplete modal data obtained from small-amplitude vibrations measured before and after a severe loading event, such as an earthquake or explosion.

Structural Health Monitoring via Measured Ritz Vectors Utilizing Artificial Neural Networks

Abstract: A pattern recognition approach for structural health monitoring (SHM) is presented that uses damage-induced changes in Ritz vectors as the features to characterize the damage patterns defined by the corresponding locations and severity of damage. Unlike most other pattern recognition methods, an artificial neural network (ANN) technique is employed as a tool for systematically identifying the damage pattern corresponding to an observed feature. An important aspect of using an ANN is its design but this is usually skipped in the literature on ANN-based SHM. The design of an ANN has significant effects on both the training and performance of the ANN. As the multi-layer perceptron ANN model is adopted in this work, ANN design refers to the selection of the number of hidden layers and the number of neurons in each hidden layer. A design method based on a Bayesian probabilistic approach for model selection is proposed. The combination of the pattern recognition method and the Bayesian ANN design method forms a practical SHM methodology. A truss model is employed to demonstrate the proposed methodology.

Structural Health Monitoring System for the Shandong Binzhou Yellow River Highway Bridge

Abstract: The Binzhou Yellow River Highway Bridge is a cable-stayed bridge in China. A structural health monitoring system was implemented on this bridge during its construction for monitoring its structural health status and assessing its safety for long-term service. This paper describes the design, implementation and functions of this system, and presents the measured responses of the bridge subjected to moving vehicle loads. The system includes a sensor module, a data acquisition module, a wired and wireless data transmit module, a structural analysis module, a database module, and a warning module. It is integrated by using LabVIEW software and can be remotely operated via Internet. After two years of service, the system operates well and confirms the current reliability of the bridge.

Bi-level Programming Formulation and Heuristic Solution Approach for Dynamic Traffic Signal Optimization

Abstract: : Although dynamic traffic control and traffic assignment are intimately connected in the framework of Intelligent Transportation Systems (ITS), they have been developed independent of one another by most existing research. Conventional methods of signal timing optimization assume given traffic flow pattern, whereas traffic assignment is performed with the assumption of fixed signal timing. This study develops a bi-level programming formulation and heuristic solution approach (HSA) for dynamic traffic signal optimization in networks with time-dependent demand and stochastic route choice. In the bi-level programming model, the upper level problem represents the decision-making behavior (signal control) of the system manager, while the user travel behavior is represented at the lower level. The HSA consists of a Genetic Algorithm (GA) and a Cell Transmission Simulation (CTS) based Incremental Logit Assignment (ILA) procedure. GA is used to seek the upper level signal control variables. ILA is developed to find user optimal flow pattern at the lower level, and CTS is implemented to propagate traffic and collect real-time traffic information. The performance of the HSA is investigated in numerical applications in a sample network. These applications compare the efficiency and quality of the global optima achieved by Elitist GA and Micro GA. Furthermore, the impact of different frequencies of updating information and different population sizes of GA on system performance is analyzed.

Multimode project scheduling based on particle swarm optimization

Abstract: This paper introduces a methodology for solving the multimode resource-constrained project scheduling problem (MRCPSP) based on particle swarm optimization (PSO) The MRCPSP considers both renewable and nonrenewable resources that have not been addressed efficiently in the construction field The framework of the PSO-based methodology is developed with the objective of minimizing project duration A particle representation formulation is proposed to represent the potential solution to the MRCPSP in terms of priority combination and mode combination for activities Each particle-represented solution should be checked against the nonrenewable resource infeasibility and will be handled by adjusting the mode combination The feasible particle-represented solution is transformed to a schedule through a serial generation scheme Experimental analyses are presented to investigate the performance of the proposed methodology Comparisons with other methods show that the PSO method is equally efficient at solving the MRCPSP

Comparison of Two Evolutionary Algorithms for Optimization of Bridge Deck Repairs

Abstract: Most bridge management systems have been developed to support either network- or project-level decisions. Network-level decisions include the selection of bridges for repair while repair strategies are considered project-level decisions. This article introduces an integrated model for bridge deck repairs with detailed life cycle costs of both network-level and project-level decisions. Two evolutionary-based optimization techniques that are capable of handling large-size problems, namely genetic algorithms and shuffled frog leaping, are then applied on the model to optimize maintenance and repair decisions. Ten trial runs with different numbers of bridges were used to compare the results of both techniques. The results indicate that both techniques can be equally suitable, and that the key issue is determining the set of parameters that optimize performance. The best optimization strategy for this type of problem appears to be a year-by-year strategy coupled with the use of a preprocessing function to allocate repair funds first to critical bridges.

A comparative study of modal parameter identification based on wavelet and Hilbert-Huang transforms

Abstract: Modal parameter identification is an important topic in vibration-based structural health monitoring. This paper presents a comparative study of the modal parameter identification of structures based on the continuous wavelet transform (WT) using the modified complex Morlet wavelet function and the improved Hilbert–Huang transform (HHT). Special attention is given to some implementation issues, such as the modal separation and end effect in the WT, the optimal parameter selection of the wavelet function, the new stopping criterion for the empirical mode decomposition and the end effect in the HHT. The capabilities of these two techniques are compared and assessed by using three examples: a numerical simulation for a damped system with two very close modes; an impact test on an experimental model with three well-separated modes; and an ambient vibration test on the Z24-bridge benchmark problem. The results demonstrate that both methods are applicable for the system with well-separated modes when the time-frequency resolutions are sufficiently taken into account. For the system with very close modes, the WT method seems to be more effective than HHT. One reason is that the frequency separation of HHT is partially dependent on the decomposition performance of the preprocess tool. Therefore, if the adjacent frequency components are very close, it is difficult to design appropriate parameters for the filters to separate them clearly.

Substructure Identification and Health Monitoring Using Noisy Response Measurements Only

Abstract: A probabilistic substructure identification and health monitoring methodology for linear systems is presented using measured response time histories only. A very large number of uncertain parameters have to be identified if one considers the updating of the entire structure. For identifiability, one then would require a very large number of sensors. Furthermore, even when such a large number of sensors are available, process- ing of vast amount of the corresponding data raises com- putational difficulties. In this article a substructuring ap- proach is proposed, which allows for the identification and monitoring of some critical substructures only. The proposed method does not require any interface measure- ments and/or excitation measurements. No information regarding the stochastic model of the input is required. Specifically, the method does not require the response to be stationary and does not assume any knowledge of the parametric form of the spectral density of the input. There- fore, the method has very wide applicability. The proposed approach allows one to obtain not only the most probable values of the updated model parameters but also their as- sociated uncertainties using only one set of response data. The probability of damage can be computed directly using data from the undamaged and possibly damaged struc- ture. A hundred-story building model is used to illustrate the proposed method.

Construction workspace planning : Assignment and analysis utilizing 4D visualization technologies

Abstract: This article describes a construction activity execution space analysis approach and a decision sup- port tool for resolving execution space interference and conflict between work-face construction activities. Lack of execution pace planning interrupt and badly affect the progress of construction activities. Also, in real situations, spatial congestion can severely reduce the productivity of workers sharing the same workspace, and may cause health and safety hazard issues. The aim of this article is to present a critical space-time analysis (CSA) approach that was developed to model and quantify workspace conges- tion and was encapsulated in a computerized tool dubbed PECASO (patterns execution and critical analysis of site- space organization) that was developed to assist site man- agers in the assignment and identification of workspace conflicts. A new concept of "visualizing workspace com- petition" between the construction activities is presented based on a unique representation of the dynamic nature of activities within the execution workspace, in 3D space and time. PECASO embraces 4D visualization and highlights the critical space control aspect to formulate an innova- tive 4D space planning and visualization tool. The CSA methodology and PECASO were validated using a real case study and the article concludes that the PECASO system has the potential to reduce the number of compet- ing workspaces, as well as the conflicting volume/space between occupied workspaces. This in turn produces bet- ter assessment of the execution strategy for a given project schedule. Additionally, the PECASO system has intro- duced a new way of communicating the program of work in a high level of detail for space planning purposes. ∗ To whom correspondence should be addressed. E-mail: n.n.

Segmentation of Pipe Images for Crack Detection in Buried Sewers

Abstract: : Assessing the condition of underground pipelines such as water lines, sewer pipes, and telecommunication conduits in an automated and reliable manner is vital to the safety and maintenance of buried public infrastructure. To fully automate condition assessment, it is necessary to develop robust data analysis and interpretation systems for defects in buried pipes. This article presents the development of an automated data analysis system for detecting defects in sanitary sewer pipelines. We propose a three-step method to identify and extract cracks from contrast enhanced pipe images. This method is based on mathematical morphology and curvature evaluation that detects crack-like patterns in a noisy pipe camera scanned image. As cracks are the most common defects in pipes and are indicative of the residual structural strength of the pipe, they are the focus of this study. This article discusses its implementation on 225 pipe images taken from different cities in North America and shows that the system performs very well under a variety of pipe conditions.

Performance of Buildings under Earthquakes in Barcelona, Spain

Abstract: : The seismic risk of the buildings of Barcelona, Spain, is analyzed by using a method based on the capacity spectrum. The seismic hazard in the area of the city is described by means of the reduced 5% damped elastic response spectrum. Obtaining fragility curves for the most important building types of an urban center requires an important amount of information about the structures and the use of nonlinear structural analysis tools. The information on the buildings of Barcelona was obtained by collecting, arranging, improving, and completing the database of the housing and current buildings. The buildings existing in Barcelona are mainly of two types: unreinforced masonry structures and reinforced concrete buildings with waffled slab floors. In addition, the Arc-View software was used to create a GIS tool for managing the collected information to develop seismic risk scenarios. This study shows that the vulnerability of the buildings is significant and therefore, in spite of the medium to low seismic hazard in the area of the city, the expected seismic risk is considerable.

Computational aspects of risk-based inspection planning

Abstract: The significant computational efforts required to compute risk-based inspection plans founded on the Bayesian decision theory has hindered their application in the past. In this article, a computationally efficient method for the calculation of risk-based inspection (RBI) plans is presented, which overcomes the problem through the use of a generic approach. After an introduction in RBI planning, focus is set on the computational aspects of the methodology. The derivation of inspection plans through interpolation in databases with predefined generic inspection plans is demonstrated and the accuracy of the methodology is investigated. Finally, an overview is given on some recent applications of the generic approach in practice, including the implementation in efficient software tools.

Fuzzy Control of Base‐Isolation System Using Multi‐Objective Genetic Algorithm

Abstract: Smart base-isolation strategies are being widely investigated as a way to reduce structural damage caused by severe loads. This study uses a friction pendulum system (FPS) as the isolator and a magnetorheological (MR) damper as the supplemental damping device of a smart base-isolation system. Neuro-fuzzy models are used to represent dynamic behavior of the MR damper and FPS. A fuzzy logic controller (FLC) is used to modulate the MR damper so as to minimize structural acceleration while maintaining acceptable base displacement levels. To this end, a multi-objective optimization scheme that uses a nondominated multi-objective genetic algorithm (NSGA-II) is used to optimize parameters of membership functions and find appropriate fuzzy rules. To demonstrate the effectiveness of the proposed multi-objective genetic algorithm for FLC, a numerical study of a smart base-isolation system is conducted using several historical earthquakes. The findings show that the proposed method can find optimal fuzzy rules and that the NSGA-II-optimized FLC outperformed a passive control strategy, a conventional semiactive control algorithm and a human-designed FLC.

GPS-GIS-Based Procedure for Tracking Vehicle Path on Horizontal Alignments

Abstract: : This article presents a global positioning system–geographic information system (GPS–GIS)-based procedure for the deduction of the horizontal alignment of a road based on the path of a control vehicle. Using differential GPS surveying, field data were collected at a 0.1-second interval, under different speed conditions on a 25-km section of a two-lane rural highway in eastern Ontario. The raw GPS data were post-processed to filter out the possible errors and then imported into a GIS environment for analysis and interpretation of the results. An extension for ArcView was written to determine the geometric features of the highway horizontal alignment, including the tangents, spirals, and circular curves. Values were obtained for the radius and length of nine circular curves, length of spirals, and the lateral position of the vehicle path along the straight and curved segments. These values were compared with the same features of the actual highway alignment. The results showed that the developed procedure and ArcView extension could produce the horizontal alignment of a road quickly, accurately, and for a relatively low cost. In addition to the extraction of the horizontal alignment of a road, the procedure can be used to track the actual vehicle path under normal driving conditions and compare it with the horizontal alignment of a road in an investigation concerning driver behavior.

Active Control of Structures Using Energy‐Based LQR Method

Abstract: : In this article, a new energy-based technique is proposed to eliminate trial and error in finding appropriate gain matrices in linear quadratic regulator (LQR) controllers used in active control of structures. The gain matrix is obtained by considering the energy of the structure. To compare the efficiency of the proposed method, a three-storey building with two active tendons in the first and third floors is considered. The proposed gain selection and other techniques reported in the literature for LQR controllers are used to compare the response of the structure for three accelograms. A comparison of the displacement and control forces illustrates the superiority of the proposed method.

Improving Tower Crane Productivity Using Wireless Technology

Abstract: : A tower crane is one of the major items of equipment used in the construction of high-rise buildings. However, crane operators do not receive adequate information, such as the target space conditions and the material being lifted, to control the crane. This limitation degrades productivity and safety. This study describes a prototype of an advanced tower crane (ATC) equipped with wireless video control and radio frequency identification (RFID) technology. With these advanced technologies, the ATC can provide the crane operator with an enhanced view of the work space and various other functions providing up-to-date material status. The ATC can also provide faster information flow with greater accuracy and improved driving efficiency. The ATC was used on a pilot construction site as a case study to test its performance. The results confirmed considerable improvement in operational speed and significantly enhanced performance in workplace safety and communication efficiency.

Determination of Gradient and Curvature Constrained Optimal Paths

Abstract: In this paper, the impact of gradient and curvature constraints on optimal path form and length are analyzed, with particular reference to selecting road, rail, and pipeline routes. The authors first examine the case of a single (global) gradient constraint and a planar surface, with or without boundaries and obstacles. This leads to a consideration of surface representation using rectangular lattices and procedures for determining shortest gradient-constrained paths across such surfaces. Gradient-constrained distance transforms are introduced as a new procedure to enable such optimal paths to be computed. Examples are provided for a range of landform profiles and gradients. Horizontal and vertical curvature constraints are then analyzed and incorporated into final solution paths at subsequent stages of the optimization process. Such paths may then be used as pre-analyzed input to detailed cost and engineering models to speed up and improve the quality and cost-effectiveness of route selection. Initial tests show that iteration of the scanning algorithm results in small changes to the optimal paths selecting, suggesting that some iteration may be desirable to obtain alternative solutions, which may have preferable profiles.

Damage Detection in a Girder Bridge by Artificial Neural Network Technique

Abstract: : Vibration-based damage identification (VBDI) techniques rely on the fact that damage in a structure reduces its stiffness and alters its global vibration characteristics. Measurement of changes in the vibration characteristics can therefore be used to determine the damage in the structure. Although VBDI offers several advantages, most of the available damage identification algorithms fail when applied to practical structures due to the effect of measurement errors, need to use incomplete mode shapes, mode truncation, and the nonunique nature of the solutions. This article presents a new robust two-step algorithm that uses the modal energy-based damage index to locate the damage and an artificial neural network technique to determine the magnitude of damage. The proposed algorithm is applied to detect simulated damage in a finite element model of a girder and a similar model of a real bridge named Crowchild Bridge located in Alberta, Canada. The results show that the proposed algorithm is quite effective in identifying the location and magnitude of damage, even in the presence of measurement errors in the input data.

Health Monitoring of Rehabilitated Concrete Bridges Using Distributed Optical Fiber Sensing

Abstract: : It is evident that a health monitoring system (HMS) holds a great deal of potential to reduce the inspection and maintenance cost of existing highway bridges by identifying the structural deficiencies at an early stage, as well as verifying the efficacy of repair procedures. As newly developed techniques, distributed optical fiber sensing (DOFS) have gradually played a prominent role in structural health monitoring for the last decade. This article focuses on the employment of two types of DOFS, namely fiber Bragg grating (FBG) and Brillouin optical time domain reflectometry (BOTDR), into an integrated HMS for rehabilitated RC girder bridges by means of a series of static and dynamic loading tests to a simply supported RC T-beam strengthened by externally post-tensioned aramid fiber reinforced polymer (AFRP) tendons. Before the loading tests, a calibration test for FBG and another one for BOTDR were implemented to, respectively, obtain good linearity for both of them. Monitoring data were collected in real time during the process of external strengthening, static loading, and dynamic loading, respectively, all of which well identified the relevant structural state. The beam was finally vibrated for 2 million cycles and then loaded monotonously to failure. Based on the bending strength of externally prestressed members, ultimate values for the test specimen were numerically computed via a newly developed simplified model, which satisfactorily predicted the ultimate structural state of the beam. And then the alert values were adopted to compare with the monitoring results for safety alarm. The investigation results show a great deal of applicability for the integrated SHM by using both DOFS in rehabilitated concrete bridges strengthened by external prestressing.

Fuzzy Expert System to Assess Corrosion of Cast/Ductile Iron Pipes from Backfill Properties

Abstract: : Several factors may contribute to the structural failure of cast and ductile iron water mains, the most important of which is considered to be corrosion The ANSI/AWWA C105/A215-99 10-point scoring (10-P) method is commonly used to predict the corrosivity potential of a given soil sample using certain soil properties The 10-P and other scoring methods use binary logic to classify the soil as either corrosive or noncorrosive Fuzzy logic extends binary logic in this context as it recognizes the real world phenomena using a certain degree of membership between 0 and 1 This article presents a fuzzy logic expert system capable of predicting the deterioration of cast and ductile iron water mains based on surrounding soil properties The proposed model consists of two modules: a knowledge base and an inference mechanism The knowledge base provides information for better decision making and is developed in a two-tier fuzzy modeling process First in direct approach, the expert knowledge generates a subjective model to describe the characteristics of the system using fuzzy linguistic variables Later in system identification, the field data are used to develop an objective model, which is eventually used in conjunction with the subjective model to provide a more reliable knowledge base for the expert system The inference mechanism uses fuzzy approximate reasoning methods to process the encoded information of the knowledge base

Structural Damage Detection Method Using Uncertain Frequency Response Functions

Abstract: : A structural damage detection method using uncertain frequency response functions (FRFs) is presented in this article. Structural damage is detected from the changes in FRFs from the original intact state. The measurements are always contaminated by noise, and sufficient data are often difficult to obtain; making it difficult to detect damage with a finite number of data. To surmount this, we introduce hypothesis testing based on the bootstrap method to statistically prevent detection errors due to measurement noise. The proposed method iteratively zooms in on the damaged elements by excluding the elements which were assessed as undamaged from among the damage candidates, step by step. The proposed approach was applied to numerical simulations using a 2D frame structure and its efficiency was confirmed.

Mobile Model-Based Bridge Lifecycle Management System

Abstract: Bridge lifecycle management systems are designed to assist in the performance of management functionalities related to bridges from the conceptual stage to the end of service life. This paper discusses the requirements for developing a mobile model-based bridge lifecycle management system (MMBLMS). This new system would link all the information about the lifecycle stages of a bridge (e.g., design, construction, inspection, and maintenance) to a four-dimensional model of the bridge, incorporating different scales of space and time to record events throughout the lifecycle with suitable levels of details (LoDs). In addition, the MMBLMS should support distributed databases and mobile location-based computing by providing user interfaces that can be used on mobile computers. A framework for a MMBLMS is described and the basic computational issues for realizing it are discussed, including the navigation modes, the picking behavior and the LoDs for representing bridge elements and defects. A prototype system developed in Java language is used to demonstrate the feasibility of the proposed methodology for realizing this system. A case study of a bridge in Montreal is also demonstrated. The prototype system received positive evaluations from bridge management engineers, and preliminary testing of the system and its user interface showed good results.

Modeling of Traffic Excitation for System Identification of Bridge Structures

Abstract: System input (traffic excitation) is difficult to measure, so system identification is often performed based only on the system output (bridge vibration responses) in long-term health monitoring of bridge structures. Traffic excitation is commonly modeled as spatially uncorrelated white noise to facilitate the identification of the bridge properties. A physical model of a stationary stream of vehicles (moving loads) arriving in accordance with a Poisson process, traversing an elastic beam, shows that the traffic excitation is spatially correlated. Employing the dynamic nodal loading approach, this spatial correlation results in a frequency-dependent excitation spectrum density matrix, and shifts the response spectra obtained from those excited by spatially uncorrelated white noise. It is shown that the application of system identification techniques based on the conventional excitation model may result in misleading structural properties. Hence, this study further proposes an output-only gray-box identification technique for bridge structures, in which knowledge about the nature of the traffic excitation, such as its spatial correlation, is implanted into an autoregressive-moving-average (ARMA) model. The identifiability of the ARMA model so constructed is assured and the feasibility of the proposed identification technique is demonstrated by a numerical example. With the proposed physics-based excitation model and the gray-box identification technique, information collected about traffic will directly contribute to improving the identification of structural structural properties.

Wavelet‐Based Structural Health Monitoring of Earthquake Excited Structures

Abstract: : The article presents a wavelet-based structural health monitoring technique for structures subjected to an earthquake excitation utilizing the instantaneous modal information. The instantaneous mode shape information is first extracted from the vibration response data collected during an earthquake event by using a wavelet packet sifting process. A confidence index (CI) is proposed to validate the results obtained. The identified normalized instantaneous mode shapes in conjunction with the corresponding CIs can be effectively used to monitor damage development in the structure. The effectiveness of the proposed approach is illustrated for two damage scenarios, sudden stiffness loss and progressive stiffness degradation, and different base excitations including three real earthquake signals and a random signal. Consistently good results were obtained in all cases. Issues related to robustness of the method in the presence of a measurement noise and sensitivity to damage severity are discussed.

3D Reconstruction of Real World Scenes Using a Low‐Cost 3D Range Scanner

Abstract: : This article presents a 3D reconstruction technique for real world environments based on a traditional 2D laser range finder modified to implement a 3D laser scanner. The article describes the mechanical and control issues addressed to physically achieve the 3D sensor used to acquire the data. It also presents the techniques used to process and merge range and intensity data to create textured polygonal models and illustrates the potential of such a unit. The result is a promising system for 3D modeling of real world scenes at a commercial price 10 or 20 times lower than current commercial 3D laser scanners. The use of such a system can simplify measurements of existing buildings and produce easily 3D models and ortophotos of existing structures with minimum effort and at an affordable price.

A Computational Framework for Risk Assessment of RC Structures Using Indicators

Abstract: The present article starts out by proposing a framework for risk assessment of RC structures utilizing condition indicators. Thereafter, the various building stones of the suggested framework are described. This description includes a summary of the basis for the probabilistic modeling of the initiation phases of chloride-induced corrosion of concrete structures. Furthermore, a probabilistic modeling of condition indicators regarding the condition state of concrete structures is proposed whereby information available at the design stage of concrete structures as well as information obtained through in-service inspections may be utilized for the purpose of reliability updating. Finally, it is described how the probability of localized and spatially distributed degradation of different degrees can be assessed and examples are given on how the various indicators may be used for the purpose of updating the statistical characteristics of the future degradation of RC structures. The presented framework forms a consistent basis for risk assessment of concrete structures subject to chloride-induced corrosion. It can easily be adopted to other degradation phenomena such as carbonation-induced corrosion and it forms a good basis for the development of efficient approaches to Asset Integrity Management of RC structures.

Web-based structural health monitoring of an FRP composite bridge

Abstract: This paper describes how wireless technology was used in a structural health monitoring scheme to monitor the long-term performance of fiber reinforced polymer (FRP) composite bridge structures. The scheme was implemented on the Kings Stormwater Channel Bridge, located on a major state highway in California. The bridge was constructed using FRP composite girders and deck panels. The data collected by a comprehensive array of sensors are transmitted wirelessly, and processed in real-time remotely. Computer-based automated analysis algorithms process the incoming data to provide an assessment of structural response. Effects, due to time-based deterioration, and irregularities are determined using modal parameters, in terms of damage localization indices and an estimated damage severity. The results, made available via a web-based interface, enable appropriate action to be authorized for preliminary maintenance or emergency response prior to actual on-site inspection. This approach shows promise for monitoring and assessment of structural systems.

A Linear Model for the Continuous Network Design Problem

Abstract: This paper introduces a dynamic network design problem model that can be used to compute continuous network improvements. The model assumes system optimum traffic flow conditions and time-dependent demand. A linear programming formulation is introduced based on a dynamic traffic assignment (DTA) model that propagates traffic according to the cell transmission model. The introduced approach is limited to continuous link improvements and does not provide for new link additions. This paper is one of the first attempts to provide an analytical formulation for network design that accounts for DTA conditions. A single destination example network, resembling a freeway corridor, is used to test the model under various congestion levels, loading patterns and budget sizes. Findings from the example demonstrate that the model is simple and effective at capturing the dynamic behavior of traffic and the dependencies among adjacent links.

Comparison of Neural Networks and Gravity Models in Trip Distribution

Abstract: Transportation engineers are commonly faced with the question of how to extract information from expensive and scarce field data. Modeling the distribution of trips between zones is complex and dependent on the quality and availability of field data. This research explores the performance of neural networks in trip distribution modeling and compares the results with commonly used doubly constrained gravity models. The approach differs from other research in several respects; the study is based on both synthetic data, varying in complexity, as well as real-world data. Furthermore, neural networks and gravity models are calibrated using different percentages of hold out data. Extensive statistical analyses are conducted to obtain necessary sample sizes for significant results. The results show that neural networks outperform gravity models when data are scarce in both synthesized as well as real-world cases. Sample size for statistically significant results is forty times lower for neural networks.

Minimizing the Consequences of Intentional Attack on Water Infrastructure

Abstract: Since September 11, 2001, protecting the na-tion’s water infrastructure and improving water networkresiliency have become priorities in the water industry.In this work, we develop methods to mitigate the con-sequences of water shortage resulting from destructionof facilities in water networks. These methods integratesearch techniques, such as branch-and-bound and geneticalgorithms, with a hydraulic solver to check demand fea-sibilities across a residual water network. The objective isto identify a feasible customer demand pattern that min-imizes the consequences of water shortage in the down-graded network. We present a mathematical model of theproblem addressed along with an exact solution method-ology and several heuristics. We apply these methodolo-gies to three water networks ranging in size from approx-imately 10–700 nodes and compare the solution qualityand computational efficiency. ∗ To whom correspondence should be addressed. E-mail: malawley@ecn.purdue.edu . 1 INTRODUCTION