Showing papers in "Computer-aided Civil and Infrastructure Engineering in 2015"

Vision‐Based Automated Crack Detection for Bridge Inspection

Abstract: The visual inspection of bridges demands long inspection time and also makes it difficult to access all areas of the bridge. This paper presents a visual-based crack detection technique for the automatic inspection of bridges. The technique collects images from an aerial camera to identify the presence of damage to the structure. The images are captured without controlling angles or positioning of cameras so there is no need for calibration. This allows the extracting of images of damage sensitive areas from different angles to increase detection of damage and decrease false-positive errors. The images can detect cracks regardless of the size or the possibility of not being visible. The effectiveness of this technique can be used to successfully detect cracks near bolts.

Structural Damage Detection Using Modal Strain Energy and Hybrid Multiobjective Optimization

Abstract: Modal strain energy (MSE) is a sensitive physical property that can be utilized as a damage index in structural health monitoring. Inverse problem solving-based approaches using single-objective optimization algorithms are also a promising damage identification method. However, the research into the integration of these methods is currently limited; only partial success in the detection of structural damage with high errors has been reported. The majority of previous research was focused on detecting damage in simply supported beams or plain structures. In this study, a novel damage detection approach using hybrid multiobjective optimization algorithms based on MSE is proposed to detect damages in various three-dimensional (3-D) steel structures. Minor damages have little effect on the difference of the modal properties of the structure, and thus such damages with multiple locations in a structure are difficult to detect using traditional damage detection methods based on modal properties. Various minor damage scenarios are created for the 3-D structures to investigate the newly proposed multiobjective approach. The proposed hybrid multiobjective genetic algorithm detects the exact locations and extents of the induced minor damages in the structure. Even though it uses incomplete mode shapes, which do not have any measured information at the damaged element, the proposed approach detects damage well. The robustness of the proposed method is investigated by adding 5% Gaussian random white noise as a noise effect to mode shapes, which are used in the calculation ofMSE.

Multi-scale geometric-semantic modeling of shield tunnels for GIS and BIM applications

Abstract: The planning of large infrastructure facilities such as inner-city subway tracks requires the consideration of widely differing scales, ranging from the kilometer scale for the general routing of the track down to the centimeter scale for detailed design of connection points. On the one hand this implies the utilization of both, Geographic Information Systems (GIS) as well as Building Information Modeling (BIM) tools, for performing the required analysis, modeling, and visualization tasks. On the other hand, a sound foundation of handling multi-scale representations is required. Although multi-scale modeling is already well established in the GIS field, there are no corresponding approaches in Infrastructure BIM so far. However, multi-scale concepts are also much needed in the BIM context, as the planning process typically provides only rough information in the early stages and increasingly detailed and fine-grained information in later stages. To meet this demand, this article presents a comprehensive concept for incorporating multi-scale representations with building information models, with a particular focus on the geometric-semantic modeling of shield tunnels. Based on a detailed analysis of the data modeling methods used in CityGML and the requirements present in the context of infrastructure planning projects, we discuss potential extensions to the BIM data model Industry Foundation Classes (IFC) for incorporating multi-scale representations of shield tunnels. Particular emphasis is put on providing means for preserving the consistency of the representation across the different Levels-of-Detail (LoDs), while taking into account both semantics and geometry. For realizing consistency preservation mechanisms, we propose to apply a procedural geometry description which makes it possible to define explicit dependencies between geometric entities on different LoDs. The modification of an object on a coarse level consequently results in an automated update of all dependent objects on the finer levels. Finally, we discuss the transformation of the IFC-based multi-scale tunnel model into a CityGML compliant tunnel representation.

Queue Profile Estimation in Congested Urban Networks with Probe Data

Abstract: Queues at signalized intersections are the main cause of traffic delays and travel time variability in urban networks. In this article, we propose a method to estimate queue profiles that are traffic shockwave polygons in the time-space plane describing the spatiotemporal formation and dissipation of queues. The method integrates the collective effect of dispersed probe vehicle data with traffic flow shockwave analysis and data mining techniques. The proposed queue profile estimation method requires position and velocity data of probe vehicles; however, any explicit information of signal settings and arrival distribution is indispensable. Moreover, the method captures interdependencies in queue evolutions of successive intersections. The significance of the proposed method is that it is applicable in oversaturated conditions and includes queue spillover identification. Numerical results of simulation experiments and tests on NGSIM field data, with various penetration rates and sampling intervals, reveal the promising and robust performance of the proposed method compared with a uniform arrival queue estimation procedure. The method provides a thorough understanding of urban traffic flow dynamics and has direct applications for delay analysis, queue length estimation, signal settings estimation, and vehicle trajectory reconstruction.

Probabilistic Assessment of Civil Infrastructure Resilience to Earthquakes

Abstract: The large losses occurred in the past due to earthquakes, even in highly developed countries, as well as the ensuing prolonged inactivity of the stricken societies, imparted momentum to research into regional seismic impact and community resilience to earthquakes. Need for comprehensive and consistent modeling is apparent, and this work presents a contribution in this direction. The extension of a recently developed civil infrastructure simulation framework to the evaluation of resilience, as well as the introduction of a new infrastructure network-based resilience metric represent the novelties of the article and allow one to explore the effect that sources of uncertainty and key vulnerability factors have on the probability distribution of resilience.

Particle Swarm Optimization with Sequential Niche Technique for Dynamic Finite Element Model Updating

Abstract: Due to uncertainties associated with material properties, structural geometry, boundary conditions, and connectivity of structural parts as well as inherent simplifying assumptions in the development of finite element (FE) models, actual behavior of structures often differs from model predictions FE model updating comprises a multitude of techniques that systematically calibrate FE models in order to match experimental results Updating of structural models can be posed as an optimization problem where model parameters that minimize the errors between the responses of the model and actual structure are sought However, due to limited number of experimental responses and measurement errors, the optimization problem may have multiple admissible solutions in the search domain Global optimization algorithms (GOAs) are useful and efficient tools in such situations as they try to find the globally optimal solution out of many possible local minima, but are not totally immune to missing the right minimum in complex problems such as those encountered in updating A methodology based on particle swarm optimization (PSO), a GOA, with sequential niche technique (SNT) for FE model updating is proposed and explored in this article The combination of PSO and SNT enables a systematic search for multiple minima and considerably increases the confidence in finding the global minimum The method is applied to FE model updating of a pedestrian cable-stayed bridge using modal data from full-scale dynamic testing

Real-Time System Identification: An Algorithm for Simultaneous Model Class Selection and Parametric Identification

Abstract: In this article, a novel Bayesian real-time system identification algorithm using response measurement is proposed for dynamical systems. In contrast to most existing structural identification methods which focus solely on parametric identification, the proposed algorithm emphasizes also model class selection. By embedding the novel model class selection component into the extended Kalman filter, the proposed algorithm is applicable to simultaneous model class selection and parametric identification in the real-time manner. Furthermore, parametric identification using the proposed algorithm is based on multiple model classes. Examples are presented with application to damage detection for degrading structures using noisy dynamic response measurement.

Smart Artificial Firefly Colony Algorithm-Based Support Vector Regression for Enhanced Forecasting in Civil Engineering

Abstract: Advanced data mining techniques are potential tools for solving civil engineering (CE) problems. This study proposes a novel smart artificial firefly colony algorithm-based support vector regression (SAFCA-SVR) system that integrates firefly algorithm (FA), chaotic maps, adaptive inertia weight, Levy flight, and least squares support vector regression (LS-SVR). First, adaptive approach and randomization methods are incorporated in FA to construct a novel and highly effective metaheuristic algorithm for global optimization. The enhanced FA is then used to optimize parameters in LS-SVR model. The proposed system is validated by comparing its performance with those of empirical methods and previous works via cross-validation algorithm and hypothesis test through the real-world engineering cases. Specifically, high-performance concrete, resilient modulus of subgrade soils, and building cooling load are used as case studies. The SAFCA-SVR achieved 8.8%–91.3% better error rates than those of previous works. Analytical results confirm that using the proposed hybrid system significantly improves the accuracy in solving CE problems.

Systemic seismic risk assessment of road networks considering interactions with the built environment

Abstract: This article presents an integrated approach for the probabilistic systemic risk analysis of a road network considering spatial seismic hazard with correlation of ground motion intensities, vulnerability of the network components, and the effect of interactions within the network, as well as, between roadway components and built environment to the network functionality. The system performance is evaluated at the system level through a global connectivity performance indicator, which depends on both physical damages to its components and induced functionality losses due to interactions with other systems. An object-oriented modeling paradigm is used, where the complex problem of several interacting systems is decomposed in a number of interacting objects, accounting for intra- and interdependencies between and within systems. Each system is specified with its components, solving algorithms, performance indicators and interactions with other systems. The proposed approach is implemented for the analysis of the road network in the city of Thessaloniki (Greece) to demonstrate its applicability. In particular, the risk for the road network in the area is calculated, specifically focusing on the short-term impact of seismic events (just after the earthquake). The potential of road blockages due to collapses of adjacent buildings and overpass bridges is analyzed, trying to individuate possible criticalities related to specific components/subsystems. The application can be extended based on the proposed approach, to account for other interactions such as failure of pipelines beneath the road segments, collapse of adjacent electric poles, or malfunction of lighting and signaling systems due to damage in the electric power network.

A Hybrid Optimization Algorithm with Bayesian Inference for Probabilistic Model Updating

Abstract: A hybrid optimization methodology is presented for the probabilistic finite element model up- dating of structural systems. The model updating pro- cess is formulated as an inverse problem, analyzed by Bayesian inference, and solved using a hybrid optimiza- tion algorithm. The proposed hybrid approach is a com- bination of a modified artificial bee colony (MABC) algorithm and the Broyden-Fletcher-Goldfarb-Shanno (BFGS) method. The MABC includes four modifica- tions compared to the standard ABC algorithm, which basically improve the global convergence of ABC in the solution phases of initialization, updating, selection, and rebirth. The BFGS is inserted to improve the finer solu- tion search ability aiming at a higher solution accuracy. In brief, a probabilistic framework based on Bayesian in- ference is first derived so to get a regularized objective function for optimization. Then the proposed MABC- BFGS algorithm is applied to determine the unknown system parameters by minimizing the newly defined objective function. System parameters as well as the pre- diction error covariance are updated iteratively in the optimization process. Posterior distributions of the iden- tified system parameters are determined using a weighted sum of Gaussian distributions. Finally, the effectiveness of the proposed approach is illustrated by the numer- ical data sets of the Phase I IASC-ASCE benchmark model and the experimental data sets of a three-storey frame structure (from the Los Alamos National Labo- ratory (LANL), New Mexico, United States).

Effect of Pulse-and-Glide Strategy on Traffic Flow for a Platoon of Mixed Automated and Manually Driven Vehicles

Abstract: The fuel consumption of ground vehicles is significantly affected by how they are driven. The fuel-optimized vehicular automation technique can improve fuel economy for the host vehicle, but their effectiveness on a platoon of vehicles is still unknown. This article studies the performance of a well-known fuel-optimized vehicle automation strategy, i.e., Pulse-and-Glide (PnG) operation, on traffic smoothness and fuel economy in a mixed traffic flow. The mixed traffic flow is assumed to be a single-lane highway on flat road consisting of both driverless and manually driven vehicles. The driverless vehicles are equipped with fuel economy-oriented automated controller using the PnG strategy. The manually driven vehicles are simulated using the Intelligent Driver Models (IDM) to mimic the average car-following behavior of human drivers in naturalistic traffics. A series of simulations are conducted with three scenarios, i.e., a single car, a car section, and a car platoon. The simulation results show that the PnG strategy can significantly improve the fuel economy of individual vehicles. For traffic flows, the fuel economy and traffic smoothness vary significantly under the PnG strategy.

Statistical Regularization for Identification of Structural Parameters and External Loadings Using State Space Models

Abstract: A novel numerical approach is presented, in the time domain, to simultaneously identify structural parameters and unmeasured input loadings using incomplete output measurement only. The identification problem is formulated as an optimization process, wherein the objective function is defined as the discrepancy between the measured and the predicted data, and is solved by a damped Gauss-Newton method. Because the proposed algorithm is a time domain technique, forward analyses are required to obtain predicted system responses so as to compute the discrepancy. Therefore, we propose an input force estimation scheme in the identification process to complete the task of input-output forward analyses, for the case of output-only measurement. The relationship between the unknown input loadings and the output measurement is established through a state space model, which basically formulates an ill-posed least squares problem. A statistical Bayesian inference-based regularization technique is presented to solve such a least squares problem. Finally, the proposed approach is illustrated by both numerical and experimental examples using output-only measurements of either acceleration or strain time histories. The results clearly show the robustness and the applicability of the proposed algorithm to simultaneously identify structural parameters and unmeasured input loadings with a high accuracy.

Simulation-Based Seismic Risk Assessment of Gas Distribution Networks

Abstract: The basic function of a gas distribution system, essentially composed of buried pipelines, reduction stations, and demand nodes, is to deliver gas from sources to end users. The objective of the article is to discuss the evaluation of seismic risk of gas networks in compliance with the performance-based earthquake engineering framework adapted to spatially distributed systems. In particular, three issues are addressed: (1) spatial seismic hazard characterization in terms of ground shaking and permanent ground deformation; (2) analysis of system's vulnerability via fragility curves; (3) seismic performance evaluation via computer-aided simulation. As an application, the seismic risk analysis of L'Aquila (central Italy) gas distribution network, a 621-km mid- and low-pressure pipeline system was considered. The analyses were performed with reference to the mid-pressure part of the network, through an object-oriented software, specific for risk assessment of lifelines, developed by the authors. Results in terms of connectivity-based performance indicators are presented and discussed, along with a performance disaggregation analysis carried out to evaluate the contribution of the components of the system to the risk.

A Multiwavelet Neural Network-Based Response Surface Method for Structural Reliability Analysis

Abstract: A new multiwavelet neural network-based response surface method is proposed for efficient structural reliability assessment. Although multiwavelet network can be used for approximating nonlinear functions, its application has been limited to small dimension problems due to computational cost. The new method expands the application of multiwavelet network to moderate dimension by introducing a series of intermediate nodes, and the number of these intermediate nodes is determined by the multiwavelet theory. Thus, a multidimensional function learning problem is transformed into a one-dimensional function learning problem. Four examples involving one stochastic finite element-based reliability problem illustrate the effectiveness of the proposed method, which indicate that the new method is more efficient up to 10 random variables than the classical multilayer perceptron-based response surface method.

Global Sensitivity-Based Model Updating for Heritage Structures

Abstract: Sensitivity-based approaches to model updat- Q2 ing have become widely used because of their capability to calibrate the model by taking into account the influence of updating parameters associated to different structural elements. Global sensitivity analysis (SA) allows model updating to be carried out even in the case of elevated un- certainty about the material characteristics. Architectural heritage structures deserve specific attention on account of their intrinsic geometrical complexity and heterogene- ity. In this article, the concept of global SA is applied for the first time to complex monumental structures, and a comparative view is offered on more classical local sen- sitivity approaches. Different finite element (FE) calibra- tion techniques—via global and local SA—were applied to the intriguing case of the church of S. Maria del Suf- fragio in L'Aquila (Italy), severely damaged by the 2009 earthquake. The FE updating was based on experimental data acquired by a dynamic monitoring system. Finally, calibration strategies were assessed through time history analyses by comparing the responses to the recorded seismic event.

Bayesian Modeling of External Corrosion in Underground Pipelines Based on the Integration of Markov Chain Monte Carlo Techniques and Clustered Inspection Data

Abstract: In this study, a model is developed to assess external corrosion in buried pipelines based on the uni- fication of Bayesian inferential structure derived from Markov chain Monte Carlo techniques using clustered inspection data. This proposed stochastic model com- bines clustering algorithms that can ascertain the simi- larity of corrosion defects and Monte Carlo simulation that can give an accurate probability density function es- timation of the corrosion rate. The metal loss rate is cho- sen as the indicator of corrosion damage propagation, obeying a generalized extreme value (GEV) distribution. Bayesian theory was employed to update the probability distribution of metal loss rate as well as the GEV param- eters in order to account for the model uncertainty. The proposed model was validated with direct and indirect inspection data extracted from a 110-km buried pipeline system.

An Advanced Stochastic Time-Cost Tradeoff Analysis Based on a CPM-Guided Genetic Algorithm

Abstract: This article presents an advanced stochastic time-cost tradeoff (ASTCT) method that performs time-cost tradeoff analysis by identifying optimal set(s) of construction methods for activities, hence reducing the project completion time and cost simultaneously. ASTCT involves a stochastic time-cost tradeoff analysis method based on a critical path method (CPM)-guided genetic algorithm (GA). It makes use of CPM schedule data exported from a project management software, and alternative construction methods obtained from estimators (i.e., normal and accelerated durations and costs) for each activity. It simulates schedule networks, identifies an optimal set of GA parameters (i.e., population size, crossover rate, mutation rate, and stopping rule), implements several GA cycles, and computes near-optimal solution(s) exhaustively. This study is of value to practitioners because ASTCT improves the computation time, reliability, and usability of existing GA-based time-cost tradeoff methods. The study is also of relevance to researchers because it facilitates experiments using different GA parameters expeditiously. Two test cases verify the usability and validity of the computational methods.

A Sparsity-Inducing Optimization-Based Algorithm for Planar Patches Extraction from Noisy Point-Cloud Data

Abstract: Currently, much of the manual labor needed to generate as-built building information models (BIMs) of existing facilities is spent converting raw point cloud data sets (PCDs) to BIM descriptions. Automating the PCD conversion process can drastically reduce the cost of generating as-built BIMs. Due to the widespread existence of planar structures in civil infrastructures, detecting and extracting planar patches from raw PCDs is a fundamental step in the conversion pipeline from PCDs to BIMs. However, existing methods cannot effectively address both automatically detecting and extracting planar patches from infrastructure PCDs. The existing methods cannot resolve the problem due to the large scale and model complexity of civil infrastructure, or due to the requirements of extra constraints or known information. To address the problem, this article presents a novel framework for automatically detecting and extracting planar patches from large-scale and noisy raw PCDs. The proposed method automatically detects planar structures, estimates the parametric plane models, and determines the boundaries of the planar patches. The first step recovers existing linear dependence relationships amongst points in the PCD by solving a group-sparsity inducing optimization problem. Next, a spectral clustering procedure based on the recovered linear dependence relationships segments the PCD. Then, for each segmented group, model parameters of the extracted planes are estimated via singular value decomposition (SVD) and maximum likelihood estimation sample consensus (MLESAC). Finally, the α-shape algorithm detects the boundaries of planar structures based on a projection of the data to the planar model. The proposed approach is evaluated comprehensively by experiments on two types of PCDs from real-world infrastructures, one captured directly by laser scanners and the other reconstructed from video using structure-from-motion techniques. To evaluate the performance comprehensively, five evaluation metrics are proposed which measure different aspects of performance. Experimental results reveal that the proposed method outperforms the existing methods, in the sense that the method automatically and accurately extracts planar patches from large-scaled raw PCDs without any extra constraints nor user assistance.

A Test Method for Damage Diagnosis of Suspension Bridge Suspender Cables

Abstract: This article proposes a model-free test method for damage diagnosis of suspender cables because these cables are one of the most vulnerable components of the bridge. Many previous damage detection methods require an accurate finite element model but this method includes two procedures: the mean normalized curvature difference procedure and the curvature difference probability procedure. The test method does not eliminate the need for manual inspection, but changes it from observation to a more quantified method. Numerical results for single and multiple damage cases indicate that: (1) both procedures can be effective for damage diagnosis of suspender cables; (2) small damage can be more easily diagnosed in long suspender cables than short ones; and (3) noise is generally not a problem because the signal-to-noise ratio can be improved by increasing the pulse excitation magnitude for a suspender cable. The potential of the method for practical applications is increased with these points.

Hierarchical Refinement of Latin Hypercube Samples

Abstract: In this article, a novel method for the extension of sample size in Latin Hypercube Sampling LHS is suggested. The method can be applied when an initial LH design is employed for the analysis of functions g of a random vector. The article explains how the statistical, sensitivity and reliability analyses of g can be divided into a hierarchical sequence of simulations with subsets of samples of a random vector in such a way that i the favorable properties of LHS are retained the low number of simulations needed for statistically significant estimations of statistical parameters of function g with low estimation variability; ii the simulation process can be halted, for example, when the estimations reach a certain prescribed statistical significance. An important aspect of the method is that it efficiently simulates subsets of samples of random vectors while focusing on their correlation structure or any other objective function such as some measure of dependence, spatial distribution uniformity, discrepancy, etc. This is achieved by employing a robust algorithm based on combinatorial optimization of the mutual ordering of samples. The method is primarily intended to serve as a tool for computationally intensive evaluations of g where there is a need for pilot numerical studies, preliminary and subsequently refined estimations of statistical parameters, optimization of the progressive learning of neural networks, or during experimental design.

Coupling Response Surface and Differential Evolution for Parameter Identification Problems

Abstract: In the present article, a new surrogate-assisted evolutionary algorithm for dynamic identification problems with unknown parameters is presented. It is based on the combination of the response surface (RS) approach (the surrogate model) with differential evolution algorithm for global search. Differential evolution (DE) is an evolutionary algorithm where N different vectors collecting the parameters of the system are chosen randomly or by adding weighted differences between vectors obtained from two populations. In the proposed algorithm (called DE-Q), the RS is introduced in the mutation operation. The new parameter vector is defined as the one minimizing the second-order polynomial function (RS), approximating the objective function. The performances in terms of speed rate are improved by introducing the second-order approximation; nevertheless, robustness of DE algorithm for global minimum search of objective function is preserved, because multiple search points are used simultaneously. Numerical examples are presented, concerning: search of the global minimum of analytical benchmark functions; parameter identification of a damaged beam; parameter identification of mechanical properties (masses and member stiffnesses) of a truss-girder steel bridge starting from frequencies and eigenvectors obtained from an experimental field test.

Synthetic Population Generation with Multilevel Controls: A Fitness‐Based Synthesis Approach and Validations

Abstract: The application of disaggregate models for predictions and policy evaluations requires as inputs detailed information on the socioeconomic characteristics of the population. The early procedure developed for population synthesis involved the generation of a joint multiway distribution of all attributes of interest using iterative proportional fitting (IPF). Recognizing its limitations, including the inability to deal with multilevel controls, several alternate methods have been proposed in the last few years. This article presents a methodology called the fitness-based synthesis (FBS) that directly generates a list of households to match several multilevel controls without the need for determining a joint multiway distribution. The application and validation results demonstrate both the feasibility of the approach and its improved performance relative to the IPF and methods using fewer control tables. This article also presents a comprehensive validation of the synthetic populations against the true populations and thereby demonstrates the ability of the FBS method to generate the multidimensional correlations among the attributes. The number of iterations to terminations is found to be between one and three times the number of households to be synthesized. In sum, the FBS is an efficient and scalable methodology that is easy to implement and as such is a valuable tool for generating the detailed socioeconomic characteristics need for applying disaggregate travel-demand forecasting models.

Physical Parameter Identification of Structural Systems with Hysteretic Pinching

Abstract: This research investigates the physical parameter identification of a nonlinear hysteretic structure with pinching behavior for real-time or rapid structural health monitoring (SHM) after a major seismic event. The identification procedure is based on the overall least squares linear regression and hypothesis testing. It is applied to a general, nonlinear slip-lock (SL) pinching model. In particular, the hysteresis loop is reconstructed using data available from current sensor technologies. The path dependent hysteresis response is first divided into different loading and unloading subhalf cycles with a single valued function. These subhalf cycles are then assumed to be piecewise linear, and the number of segments for each subhalf cycle is identified using the sup F type test. The overall least squares linear regression is finally applied to the identified subhalf cycles to compute the regression coefficients and breakpoints that yield the elastic stiffness, plastic stiffness, and cumulative plastic deformation. The performance and robustness of the proposed method is illustrated using a single degree of freedom shear-type reinforced concrete structure with 10% added root mean square noise and variable pinching behavior. The proposed method is shown to be computationally efficient and accurate in identifying the damage parameters within 10% of true values. These results indicate that the system is able to capture nonlinear behavior and structural parameters, such as preyielding stiffness, postyielding stiffness and cumulative plastic deformation, directly relevant to damage and performance using a computationally efficient and simple method. Finally, the method requires no user input and could thus be automated and performed in real time for each half cycle, with results available effectively immediately after an event, as well as during an event, if required.

GA-Based Multi-Objective Optimizationźfor Retrofit Design on a Multi-Core PC Cluster

Abstract: This article presents a distributed nondominated sorting genetic algorithm II NSGA-II for optimal seismic retrofit design using buckling restrained braces BRBs on a cluster of multi-core PCs. In the formulation, two conflicting objective functions of the initial BRB installation cost required for seismic retrofitting and damage cost that can be incurred by earthquakes expected during the life cycle of the structure were minimized. Because time-consuming nonlinear structural analyses are required for fitness evaluations of individuals in every generation, parallelism at candidate design level or individual level is exploited by assigning fitness evaluations for individuals to slave core processors evenly. The distributed algorithm is applied to seismic retrofit design of 2D steel frame structure and 3D irregular reinforced concrete structure. The performance of the distributed NSGA-II was assessed based on three criteria: convergence of the distributed algorithm, efficiency of distributed computing, and quality of optimal solutions. Implementation of the distributed algorithm on the multi-core cluster consisting of up to 64 core processors resulted in relatively high speedups or efficiencies of the distributed optimization without deteriorating the quality of the optimal solutions.

Fuzzy Cellular Automata Model for Signalized Intersections

Abstract: At signalized intersections, the decision-making process of each individual driver is a very complex process that involves many factors. In this article, a fuzzy cellular automata FCA model, which incorporates traditional cellular automata CA and fuzzy logic FL, is developed to simulate the decision-making process and estimate the effect of driving behavior on traffic performance. Different from existing models and applications, the proposed FCA model utilizes fuzzy interface systems FISs and membership functions to simulate the cognition system of individual drivers. Four FISs are defined for each decision-making process: car-following, lane-changing, amber-running, and right-turn filtering. A field observation study is conducted to calibrate membership functions of input factors, model parameters, and to validate the proposed FCA model. Simulation experiments of a two-lane system show that the proposed FCA model is able to replicate decision-making processes and estimate the effect on overall traffic performance.

Emission Mitigation via Longitudinal Control of Intelligent Vehicles in a Congested Platoon

Abstract: The effectiveness of vehicle control on emission mitigation and traffic stabilization increases with the percentage (penetration rate) of intelligent vehicles in the congested vehicle platoon. A nonlinear model predictive control (MPC) approach for emission mitigation via longitudinal control of intelligent vehicles in a congested platoon is proposed in this article. Car-following behavior of drivers contributes to oscillations and significant emissions in congested highway traffic. The emergence of intelligent vehicles and modern communication technologies provides an opportunity to reduce adverse impacts of human factors and dynamically control car-following vehicles. To relieve the real-time optimization burden, the authors propose an instantaneous control model which is essentially a simplified MPC approach with a short and identical prediction and control horizon. The proposed vehicle control strategies are tested using a series of simulations and results verify that localized and instantaneous control of a few intelligent vehicles could reduce emissions of a platoon of vehicles. The models are also applied to field trajectory data and the results show that the instantaneous emission optimization model significantly reduces emissions without increasing travel time.

Online Simultaneous Reconstruction of Wind Load and Structural Responses—Theory and Application to Canton Tower

Abstract: The actual wind load information is helpful in evaluating the health status of high-rise structures. However, as a type of distributed load, the wind load is very difficult to be measured directly. A possible solution is to reconstruct it from the structural response measurements. This is often an ill-posed inverse problem. In this article, such ill posedness is solved by using a stable input estimator. With the help of the proposed application-oriented algorithm selection guidance, a type of state and input estimator is formulated. This type of estimator is designed based on the Kalman filter scheme, and is capable of estimating the unknown inputs and the system states within one sampling time. This actually facilitates the online simultaneous reconstruction of the wind load and the structural responses. The 600 m tall Canton Tower is situated in a typhoon active area, and a structural health monitoring system has already been integrated onto this tower. These two points make the Canton Tower an ideal test bed for validating the above illustrated online reconstruction strategy for the wind load and the structural responses. An operational modal analysis (OMA) is first performed to identify the modal properties of the Canton Tower under the Typhoon Nanmadol in 2011. Then a reduced-order finite element model (FEM) of the Canton Tower is updated according the OMA results. Finally, the equivalent fluctuating lateral loads and moments, which act on the nodes of the reduced-order FEM are reconstructed using the acceleration measurements recorded during Tyhoon Kai-tak in 2012. The reconstruction results are validated by comparing the simultaneously reconstructed structural acceleration with the corresponding sensor measurements. The mean component of the loads and moments are calculated using the real-time wind speed measurements and the available aerodynamic force coefficients. It is noted here that the focus of this article is not to develop a totally new theory, but rather to explore the application of a state and input estimator in the foreground to a practical complex structure.

Semiautomatic Extraction of Road Horizontal Alignment from a Mobile LiDAR System

Abstract: This article proposes a method to semiautomatically extract the road axis through a mobile LiDAR system, a recent popular technology for transportation-related applications, road estimation and even to enhance driver safety. In particular, the approach developed has two components: (1) the feature extraction from LiDAR data to model the road axis, and (2) the estimation of the horizontal alignment that meets the requirements and practice for a transportation authority. Given the massive and complex character of the data captured by the system, a hierarchical (coarse-to-fine) and robust strategy based on segmentation, parameterization and filtering, which determine the road centerline together with the geometric elements that compose its horizontal alignment, such as straight lines, circular arcs, and clothoids, has been developed and implemented. Test results using a simulated and a real data are discussed and validated. The experimental results obtained with real cases guarantying relative accuracies under 2%, being a useful approach to produce accurate estimations of the horizontal geometric features of the road alignment.

Model Updating Technique Based on Modal Participation Factors for Beam Structures

Abstract: This article proposes a model updating technique based on modal participation factors for a beam structure. In this model updating technique, the error functions of the dynamic characteristic differences between measurement and model are generated as the number of modes under consideration and minimized using the multiobjective optimization techniques. A modal influence factor defined by modal participation factors for each mode is presented for the selection of the best solution from among Pareto solutions. The selection rule represented in this article makes it possible to reflect the contributions of each mode on the behavior of a structure. The model is updated using natural frequencies measured in an impact hammer test of a beam structure and the validity of the updated model is confirmed by the strain responses measured from the test. It is found that the bending stiffness of the beam structure as the parameter for model updating can be identified by the proposed techniques. Furthermore, through comparing the models updated by the simple sum model updating and the technique in this research, it is verified that the proposed technique is more appropriate for the model updating.

Performance-Based Probabilistic Capacity Models and Fragility Estimates for RC Columns Subject to Vehicle Collision

Abstract: This article developed performance-based probabilistic models for the dynamic shear force capacity of reinforced concrete (RC) columns in bridges and buildings. The infrastructure and transportation facilities have rapidly grown over the years because of increasing population and demand and this has been associated with an increasing number of vehicle collisions with structures. In RC structures, columns are usually the most vulnerable members exposed to collisions. This type of collision might lead to damage and sometimes collapse of the structures. However, the existing design guidelines and provisions for protection of these members against collision of vehicles are not adequate. In particular, the desired behavior and performance levels of a RC structure during a vehicle collision are not defined. Therefore, there is a need to assess the vulnerability of structures against such collisions. This article developed a framework to estimate the fragility of the RC columns that were subjected to vehicle collision. The developed probabilistic dynamic shear force capacity models can be used for a performance-based design of structures such as buildings and bridges and the developed framework can also be used to estimate the adequacy of a structure to sustain a collision event and make recommendations for repair and retrofit. The approach presented in the article can also be used to study collisions and develop models for ship collisions with bridge piers and projectile collisions of vehicles into concrete walls.