Showing papers in "Computer-aided Civil and Infrastructure Engineering in 2011"

Automated Object Identification Using Optical Video Cameras on Construction Sites

Abstract: : Visual recording devices such as video cameras, CCTVs, or webcams have been broadly used to facilitate work progress or safety monitoring on construction sites. Without human intervention, however, both real-time reasoning about captured scenes and interpretation of recorded images are challenging tasks. This article presents an exploratory method for automated object identification using standard video cameras on construction sites. The proposed method supports real-time detection and classification of mobile heavy equipment and workers. The background subtraction algorithm extracts motion pixels from an image sequence, the pixels are then grouped into regions to represent moving objects, and finally the regions are identified as a certain object using classifiers. For evaluating the method, the formulated computer-aided process was implemented on actual construction sites, and promising results were obtained. This article is expected to contribute to future applications of automated monitoring systems of work zone safety or productivity.

Finite Element Model Updating Using Evolutionary Strategy for Damage Detection

Abstract: : Structural health monitoring through the use of finite element model updating techniques for dispersed civil infrastructures usually deals with minimizing a complex, nonlinear, nonconvex, high-dimensional cost function with several local minima. Hence, stochastic optimization algorithms with promising performance in solving global optimization problems have received considerable attention for finite element model updating purposes in recent years. In this study, the performance of an evolutionary strategy in the finite element model updating approach was investigated for damage detection in a quarter-scale two-span reinforced concrete bridge system which was tested experimentally at the University of Nevada, Reno. The damage sequence in the structure was induced by a range of progressively increasing excitations in the transverse direction of the specimen. Intermediate nondestructive white noise excitations and response measurements were used for system identification and damage detection purposes. It is shown that, when evaluated together with the strain gauge measurements and visual inspection results, the applied finite element model updating algorithm of this article could accurately detect, localize, and quantify the damage in the tested bridge columns throughout the different phases of the experiment.

Influence of the Autoregressive Model Order on Damage Detection

Abstract: : An important step for using time-series autoregressive (AR) models for structural health monitoring is the estimation of the appropriate model order. To obtain an optimal AR model order for such processes, this article presents and discusses four techniques based on Akaike information criterion, partial autocorrelation function, root mean squared error, and singular value decomposition. A unique contribution of this work is to provide a comparative study with three different AR models that is carried out to understand the influence of the model order on the damage detection process in the presence of simulated operational and environmental variability. A three-story base-excited frame structure was used as a test bed in a laboratory setting, and data sets were measured for several structural state conditions. Damage was introduced by a bumper mechanism that induces a repetitive impact-type nonlinearity. The operational and environmental effects were simulated by adding mass and by changing the stiffness properties of the columns. It was found that these four techniques do not converge to a unique solution, rather all require somewhat qualitative interpretation to define the optimal model order. The comparative study carried out on these data sets shows that the AR model order range defined by the four techniques provides robust damage detection in the presence of simulated operational and environmental variability.

A Synchronized Wireless Sensor Network for Experimental Modal Analysis in Structural Health Monitoring

Abstract: Structural health monitoring aims to provide an accurate diagnosis of the condition of civil infrastruc- tures during their life span using data acquired by sen- sors Wireless sensor networks represent a suitable mon- itoring technology to collect reliable information about the structure's condition, replacing visual inspections, and reducing installation and maintenance time and costs This article introduces a time synchronized and configurable wireless sensor network for structural health monitoring enabling a highly accurate identification of the modal properties of the monitored structure The wireless sensor nodes forming the network are equipped with a 3-axis digital accelerometer and a temperature and humidity sensor The implemented Medium Access Con- trol layer time synchronization protocol (μ-Sync) en- sures a highly accurate synchronicity among the samples collected by the nodes, the absolute error being constantly below 10 μs, also when high sampling frequency (up to 1 kHz) and extended sampling periods (up to 10 min- utes) are applied The experimental results obtained on a wooden model bridge, compared with those derived from acceleration signals acquired by high-quality wired sensors, show that the so synchronized wireless sensor nodes allow a precise identification of the natural fre- quencies of vibration of the monitored structure (1% maximum relative difference)

Modified Genetic Algorithm for the Dynamic Identification of Structural Systems Using Incomplete Measurements

Abstract: : A modified real-coded genetic algorithm to identify the parameters of large structural systems subject to the dynamic loads is presented in this article. The proposed algorithm utilizes several subpopulations and a migration operator with a ring topology is periodically performed to allow the interaction between them. For each subpopulation, a specialized medley of recent genetic operators (crossover and mutation) has been adopted and is briefly discussed. The final algorithm includes a novel operator based on the auto-adaptive asexual reproduction of the best individual in the current subpopulation. This latter is introduced to avoid a long stagnation at the start of the evolutionary process due to insufficient exploration as well as to attempt an improved local exploration around the current best solution at the end of the search. Moreover, a search space reduction technique is performed to improve, both convergence speed and final accuracy, allowing a genetic-based search within a reduced region of the initial feasible domain. This numerical technique has been used to identify two shear-type mechanical systems with 10 and 30 degrees-of-freedom, assuming as unknown parameters the mass, the stiffness, and the damping coefficients. The identification will be conducted starting from some noisy acceleration signals to verify, both the computational effectiveness and the accuracy of the proposed optimizer in presence of high noise-to-signal ratio. A critical and detailed analysis of the results is presented to investigate the inner work of the optimizer. Finally, its performances are examined and compared to the most recent results documented in the current literature to demonstrate the numerical competitiveness of the proposed strategy.

Reconstructing the Traffic State by Fusion of Heterogeneous Data

Abstract: This paper presents an advanced interpolation method for estimating smooth spatiotemporal profiles for local highway traffic variables such as flow, speed and density. The method is based on the “adaptive smoothing method” which takes as input stationary detector data as typically collected by traffic control centers. The authors generalize this method to allow for fusion with floating car data or other traffic information. The resulting profiles display transitions between free and congested traffic in great detail, as well as fine structures such as stop-and-go waves. The authors establish the accuracy and robustness of the method and demonstrate three potential applications: (1) compensation for gaps in data caused by detector failure; (2) separation of noise from dynamic traffic information; and (3) the fusion of floating car data with stationary detector data.

Fusing Loop Detector and Probe Vehicle Data to Estimate Travel Time Statistics on Signalized Urban Networks

Abstract: This article presents a methodology that integrates cumulative plots with probe vehicle data for estimation of travel time statistics (average, quartile) on urban networks. The integration reduces relative deviation among the cumulative plots so that the classical analytical procedure of defining the area between the plots as the total travel time can be applied. For quartile estimation, a slicing technique is proposed. The methodology is validated with real data from Lucerne, Switzerland and it is concluded that the travel time estimates from the proposed methodology are statistically equivalent to the observed values.

A Heuristic Approach to the Railroad Track Maintenance Scheduling Problem

Abstract: : Every year, billions of dollars are spent on rail track maintenance to keep the serviceability of the railroad network. These maintenance projects (of different types) must be performed by suitable maintenance teams within a planning horizon. This article presents a time-space network model to solve the track maintenance scheduling problem (TMSP). The objective is to minimize the total travel costs of the maintenance teams as well as the impact of maintenance projects on railroad operation, which are formulated by three types of side constraints: mutually exclusive, time window, and precedence constraints. An iterative heuristic solution approach is proposed to solve the large-scale TMSP model with a large number of side constraints. The proposed model and solution approach are applied to a large-scale real-world problem. Compared to the current industry practice the model outcome eliminated all hard side-constraint violations and reduced the total objective value (travel costs and soft side-constraint violation penalties) by 66.8%.

Numerical Evaluation of Vibration-Based Methods for Damage Assessment of Cable-Stayed Bridges

Abstract: : This study investigated a number of different damage detection algorithms for structural health monitoring of a typical cable-stayed bridge. The Bayview Bridge, a cable-stayed bridge in Quincy, Illinois, was selected for the study. The focus was in comparing the viability of simplified techniques for practical applications. Accordingly, the numerical analysis involved development of a precise linear elastic finite element model (FEM) to simulate various structural health monitoring test scenarios with accelerometers. The Effective Independence Method was employed to locate the best distribution of the accelerometers along the length of the bridge. The simulated accelerometer data based on the FEM analysis was employed for the evaluation of the four damage identification methods investigated here. These methods included the Enhanced Coordinate Modal Assurance Criterion, Damage Index Method, Mode Shape Curvature Method, and Modal Flexibility Index Method. Some of these methods had been previously applied only to a number of specific bridges. However, the investigation here provides the relative merits and shortcomings of the damage detection methods in long-span cable-stayed bridges.

Modeling Framework and Architecture of Hybrid System Dynamics and Discrete Event Simulation for Construction

Abstract: : Construction system modeling aims to improve construction work performance by tracking the dynamic behaviors of construction systems. More accurate system modeling can be achieved by considering the mutual effects of construction operations on the context level of the system. Hybrid models of discrete event simulation (DES) and system dynamics aim to capture these mutual effects to provide model developers with more precise system analysis. Although system dynamics models are used to capture the behavior of the system at the context level, DES models are utilized to capture construction operations. The potential benefits of utilizing hybrid models for complex systems have been argued and established, but there are still limited studies that have utilized this modeling approach for real construction systems. In this research, we have attempted to identify the challenging issues that have caused this problem and to confront this problem by proposing a hybrid framework and architecture, which address these challenges. To verify the effectiveness of the new model, the performance of the proposed hybrid modeling framework and architecture has been tested by applying the proposed model in a real-scale construction-related system.

A Cell-Based Model for Multi-class Doubly Stochastic Dynamic Traffic Assignment

Abstract: This article proposes a cell-based multi-class dynamic traffic assignment problem that considers the random evolution of traffic states. Travelers are assumed to select routes based on perceived effective travel time, where effective travel time is the sum of mean travel time and safety margin. The proposed problem is formulated as a fixed point problem, which includes a Monte-Carlo-based stochastic cell transmission model to capture the effect of physical queues and the random evolution of traffic states during flow propagation. The fixed point problem is solved by the self-regulated averaging method. The results illustrate the properties of the problem and the effectiveness of the solution method. The key findings include the following: (1) Reducing perception errors on traffic conditions may not be able to reduce the uncertainty of estimating system performance, (2) Using the self-regulated averaging method can give a much faster rate of convergence in most test cases compared with using the method of successive averages, (3) The combination of the values of the step size parameters highly affects the speed of convergence, (4) A higher demand, a better information quality, or a higher degree of the risk aversion of drivers can lead to a higher computation time, (5) More driver classes do not necessarily result in a longer computation time, and (6) Computation time can be significantly reduced by using small sample sizes in the early stage of solution processes.

Reliability-based transit assignment for congested stochastic transit networks

Abstract: This article proposes a nonlinear complementarity problem (NCP) formulation for the risk-aversive stochastic transit assignment problem in which in-vehicle travel time, waiting time, capacity, and the effect of congestion are considered as stochastic variables simultaneously and both their means and variances are incorporated into the formulation. A new congestion model is developed and captured in the proposed NCP formulation to account for different effects of on-board passengers and passengers waiting at stops. A reliability-based user equilibrium condition is also defined based on the proposed generalized concept of travel time budget referred to as effective travel cost, and is captured in the formulation. A column generation based algorithm is proposed to solve the NCP formulation. A survey was conducted to validate that the degree of risk aversion of transit passengers affects their route choices. Numerical studies were performed to demonstrate the problem and the effectiveness of the proposed algorithm. The results obtained show that underestimating the congestion effect and ignoring the risk aversion behavior can overestimate the patronage of transit service, which have profound implications on the profit of the operators involved and the development of transit network design models.

Fractal Dimension‐Based Damage Detection Method for Beams with a Uniform Cross‐Section

Abstract: : For civil structures, damage usually occurs in localized areas. As fractal dimension (FD) analysis can provide insight to local complexity in geometry, a damage detection approach based on Katz's estimation of the FD measure of displacement mode shape for homogeneous, uniform cross-sectional beam structures is proposed in this study. An FD-based index for damage localization (FDIDL) is developed utilizing the difference of angles of sliding windows between two successive points, which is expressed in FD. To improve robustness against noise, FDIDL is calculated using multisliding windows. The influence of the spatial sampling interval length and the number of 2-sampling sliding windows on sensitivity to damage and robustness against noise is investigated. The relationship between the angle expressed in FD and the modal strain energy is established and thereby an FD-based index for the estimation of damage extent (FDIDE) is presented. The two damage indices are applied to a simply supported beam to detect the simulated damage in the beam. The results indicate that the proposed FDIDL can locate the single or multiple damages, and FDIDE can reliably quantify the damage extent. The optimal spatial sampling interval and the number of sliding windows are investigated. Furthermore, the simulation with measurement noise is carried out to demonstrate the effectiveness and robustness of the two defined FD-based damage indices. Finally, experiments are conducted on simply supported steel beams damaged at different locations. It is demonstrated that the proposed approach can locate the damages to a satisfactory precision.

Toward an Integrated Smart Sensing System and Data Interpretation Techniques for Pavement Fatigue Monitoring

Abstract: : Currently, pavement instrumentation for condition monitoring is done on a localized and short-term basis. Existing technology does not allow for continuous long-term monitoring and network level deployment. Long-term monitoring of mechanical loading for pavement structures could reduce maintenance costs, improve longevity, and enhance safety. In this article, on-going research to develop and validate a smart pavement monitoring system is described. The system mainly consists of a novel self-powered wireless sensor based on the integration of piezoelectric transduction with floating-gate injection capable of detecting, storing, and transmitting strain history for long-term monitoring and a novel passive temperature gauge. A technique for estimating full-field strain distributions using measured data from a limited number of implemented sensors is also described. The ultimate purpose is to incorporate the traffic wander effect in the fatigue prediction algorithms. Preliminary results are shown and limitations are discussed.

Durability Monitoring for Improved Service Life Predictions of Concrete Bridge Decks in Corrosive Environments

Abstract: The development of an effective strategy for the inspection and monitoring of the nation's critical bridges has become necessary due to aging, increased traffic loads, changing environmental conditions, and advanced deterioration. This article presents the development of a probabilistic mechanistic modeling approach supported by durability monitoring to obtain improved predictions of service life of concrete bridge decks exposed to chlorides. The application and benefits of this approach are illustrated on a case study of a reinforced concrete barrier wall of a highway bridge monitored over 10 years. It is demonstrated that service life predictions using probabilistic models calibrated with selected monitored field data can provide more reliable assessments of the probabilities of reinforcement corrosion and corrosion-induced damage compared to using deterministic models based on standard data from the literature. Such calibrated probabilistic models can help decision makers optimize intervention strategies as to how and when to repair or rehabilitate a given structure, thus improving its life cycle performance, extending its service life and reducing its life cycle cost.

Stress Development of a Supertall Structure during Construction: Field Monitoring and Numerical Analysis

Abstract: : The safety of the 610 m Guangzhou New Television Tower, due to become China's tallest structure on completion, is an issue that concerns many parties because of the lack of sufficient experience, official design codes, and construction guidelines for such a skyscraper. This article investigates the strain/stress development of this supertall structure through the integration of finite element analysis and field monitoring during the construction stage with a particular focus on the following issues: (1) the shrinkage and creep properties of high-strength low-shrinkage concrete; (2) the strain response of the structure to extreme events including typhoons, a major earthquake, and unfavorable construction loads; and (3) the stress evolution of the supertall structure as construction activity progresses. Field monitoring results demonstrate that the strain responses of the structure to natural hazards are within safe ranges. Finite element model predictions made at different stages of construction are in good agreement with measurement data. The monitoring exercise described in this article provides the designer, the contractor, and the client valuable data regarding the safety of the tower. It verifies the effectiveness and importance of monitoring during the construction phase for such a complex supertall structure.

Wireless Monitoring of a Multispan Bridge Superstructure for Diagnostic Load Testing and System Identification

Abstract: This article focuses on the deployment of a wireless sensor system (WSS) developed at Clarkson University for structural monitoring purposes. The WSS is designed specifically for diagnostic bridge monitoring, providing independent conditioning for accelerometers, strain transducers, and temperature sensors in addition to high-rate wireless data transmission and is capable of supporting large-scale sensor arrays. A three-span simply supported structure was subjected to diagnostic load testing as well as ambient vibration monitoring. A total of 90 wireless and several wired sensors, including accelerometers and strain transducers were used in the deployment. Strain measurements provided capacity and demand characteristics of the structure in the form of neutral axis locations, load distributions, and dynamic allowances which ultimately produced an inventory and operating load rating for the structure. Additionally, modal characteristics of the structure, including natural frequencies and mode shapes, were derived from measured accelerations and discussed briefly.

Passing Rates to Measure Relaxation and Impact of Lane-Changing in Congestion

Abstract: : Passing rate measurements of backward-moving kinematic waves in congestion are applied to quantify two traffic features; a relaxation phenomenon of vehicle lane-changing and impact of lane-changing in traffic streams after the relaxation process is complete. The relaxation phenomenon occurs when either a lane-changer or its immediate follower accepts a short spacing upon insertion and gradually resumes a larger spacing. A simple existing model describes this process with few observable parameters. In this study, the existing model is reformulated to estimate its parameter using passing rate measurements. Calibration results based on vehicle trajectories from two freeway locations indicate that the revised relaxation model matches the observation well. The results also indicate that the relaxation occurs in about 15 seconds and that the shoulder lane exhibits a longer relaxation duration. The passing rate measurements were also employed to quantify the postrelaxation impact of multiple lane-changing maneuvers within a platoon of 10 or more vehicles in queued traffic stream. The analysis of the same data sets shows that lane-changing activities do not induce a long-term change in traffic states; traffic streams are perturbed temporarily by lane-changing maneuvers but return to the initial states after relaxations.

Modeling and Implementation of Adaptive Transit Signal Priority on Actuated Control Systems

Abstract: This article describes the development and implementation of adaptive transit signal priority (TSP) on an actuated dual-ring traffic signal control system After providing an overview of architecture design of the adaptive TSP system, the article presents an adaptive TSP optimization model that optimizes green splits for 3 consecutive cycles to minimize the weighted sum of transit vehicle delay and other traffic delay, considering the safety and other operational constraints under the dual-ring structure of signal control The model is illustrated using a numerical example under medium and heavily congested situations The findings from a field operational test are also reported to validate and demonstrate the developed TSP system At a congested intersection, it is found that the average bus delay and average traffic delay along the bus movement direction were reduced by approximately 43% and 16%, respectively Moreover, the average delay of cross-street traffic was increased by about 12%

Vibration-Based Damage Localization in Flexural Structures Using Normalized Modal Macrostrain Techniques from Limited Measurements

Abstract: : This article presents damage locating indices based on normalized modal macrostrain (MMS) as improvement on the typical curvature-dependent methods. Vulnerability to noise and the use of numerical differentiation procedures are the key factors for the poor performance of many curvature-dependent methods using displacement mode shapes. Whereas dynamic distributed strain measurement data from long-gauge FBG sensors have significantly improved the performance of many damage identification methods, the sensitivity to local damage diminishes as the gauge length increases. The proposed model-free damage identification techniques based on normalized MMS vectors are successfully implemented to locate damage in beam-like structures through numerical simulations and experimental verifications. The unique advantages of the techniques are their simplicity, robustness to noise, ability to precisely identify small damage extents, and localize single and multiple damage states using limited measurable modes from few sensors.

Estimation and Warning of Fatigue Damage of FRP Stay Cables Based on Acoustic Emission Techniques and Fractal Theory

Abstract: A damage assessment and warning method for stay cables based on the acoustic emission (AE) technique and fractal theory was developed. First, the AE signal features of Higuchi's fractal dimension (FD) were analyzed at various scales. The analytical results indicated that the FD was associated with the frequency response. Meanwhile, it was found that the curve length of the original signal reflected the fluctuation of the AE signal in the time domain. Both the FD and the curve length of the original signal were related to damage evolution. Based on the above analysis, a damage index, namely, the FD-based damage assessment index, was defined by the fractal features of AE signals generated by damaged structures, including the curve length of the original signal and its FD. Fatigue tests of glass fiber-reinforced polymer (GFRP) and carbon fiber-reinforced polymer (CFRP) cables with AE sensors were performed to validate the proposed approach. The time-history responses and frequency responses of the AE signals and the corresponding damage modes were analyzed during the entire cyclic loading process. The FD-based damage indices for all the FRP cables were obtained through analysis of the AE signals. The relationships of both the time-history responses and the frequency responses with the FD-based damage index were investigated. The results indicated that the FD-based damage index increased little with the number of loading cycles at the early loading stage but increased dramatically at the final stage of the fatigue test. The results of this article demonstrate that an FD-based damage index can quantify the evolutionary process of fatigue damage in FRP stay cables.

Reliability Assessment of Lifeline Systems with Radial Topology

Abstract: The increased susceptibility of lifeline systems to failure due to aging and external hazards requires efficient methods to quantify their reliability and related uncertainty. Monte Carlo simulation techniques for network-level reliability and uncertainty assessment usually require large computational experiments. Also, available analytical approaches apply mainly to simple network topologies, and are limited to providing average values, low order moments, or confidence bounds of reliability metrics. This study introduces a closed form technique to obtain the entire probability distribution of a reliability metric of customer service availability (CSA) for generic radial lifeline systems. A special case of this general formulation reduces to a simple sum of products equation, for which a recursive algorithm that exploits its structure is presented. This special-case algorithm computes the probability mass function (PMF) of CSA for systems with M elements in O(M 3 ) operations, relative to conventional O(2 M ) operations, and opens the possibility of finding recursive algorithms for the general radial case. Parametric models that approximate the CSA metric are also explored and their errors quantified. The proposed radial topology reliability assessment tools and resulting probability distributions provide infrastructure owners with critical insights for informed operation and maintenance decision making under uncertainty.

A Fuzzy Expert System for Prioritizing Rehabilitation of Sewer Networks

Abstract: : Rehabilitation of sewer networks is a huge and very costly global problem that has often been treated on a crisis-based approach. The development of a rehabilitation program requires models and tools for assessing the condition and performance of sewers. The original contribution of this study is the development of a ranking scheme for sewer rehabilitation priorities. A fuzzy expert system was applied with inputs from a combined assessment of hydraulic, structural performance and potential failure consequences. The fuzzy structural system computes the global structural performance index for each pipe using internal condition, surrounding condition, and site vulnerability (SV) as inputs. The fuzzy hydraulic system uses hydraulic performance index (HPI), hydraulic performance impact, and SV to compute the global HPI. Finally, the fuzzy global system uses all these factors to compute the global performance index for each pipe. This methodology was successfully applied to the sewer system of the City of Laval in Canada. The results show how the fuzzy inference system may be used to establish rehabilitation priorities for each pipe section. The fuzzy expert system provides more realistic results than the intuitive approaches that use structural and hydraulic performance maximum and mean.

Time-varying lane-based capacity reversibility for traffic management

Abstract: Many metropolitan areas have adopted various traffic management techniques to maintain an efficient traffic flow. This article proposes a new bi-level formulation for the time-varying lane-based capacity reversibility problem for traffic management. The problem is formulated as a bi-level program where the lower level is the cell-transmission-based user-optimal dynamic traffic assignment (UODTA). Due to its Non-deterministic Polynomial-time hard (NP-hard) complexity, the genetic algorithm (GA) with the simulation-based UODTA is adopted to solve multi-origin multi-destination problems. Four GA variations are proposed. GA1 is a simple GA. GA2, GA3, and GA4, with a jam-density factor parameter (JDF), employ time-dependent congestion measures in their decoding procedures. The four algorithms are empirically tested on a grid network and compared based on solution quality, convergence speed, and central processing unit (CPU) time. GA3 with JDF of 0.6 appears to be the best on the three criteria. On the Sioux Falls network, GA3 with JDF of 0.7 performs the best. The GA with the appropriate inclusion of problem-specific knowledge and parameter calibration indeed provides excellent results when compared with the simple GA.

An Effective Texture Mapping Approach for 3D Models Obtained from Laser Scanner Data to Building Documentation

Abstract: : Obtaining virtual models from real buildings, terrains, or building works is a matter of increased interest in construction. The application of such models ranges from technical use in architecture and civil engineering, to multimedia presentation, or remote visits through the web. This is becoming possible thanks to recent advances in laser scanning technology and related 3D processing algorithms. Moreover, real texture mapped onto 3D models is often required for communication, cataloguing, or digital documentation projects. In this article, an effective methodology to obtain digital building documentation based on 3D textured models is presented. First of all, a brief presentation of laser scanners is given as their data are used. An approach for mapping photographic images onto 3D models is also presented. The proposed approach, based on a camera registration method, offers high flexibility as it is based on hand-held cameras and can be implemented in a computing-effective way. A method for automatic image selection in overlapped areas is also presented. Finally, some hints are given concerning the automatic extraction of sections, orthophotos, and feature lines from the models. Experimental results focused on heritage buildings are shown, which demonstrate the suitability of the proposed techniques.

Gage‐Free Stress Estimation of a Beam‐like Structure Based on Terrestrial Laser Scanning

Abstract: This article will discuss how terrestrial laser scanning (TLS) is used in the structural health monitoring of structures. TLS is a technique that remotely obtains the three-dimensional (3D) coordinates of an object using laser pulses. It is advantageous when used to obtain the 3D coordinates of the overall shape as well as any particular area or point of a target object. In addition, using TLS for the stress monitoring of structures will not require the installation of a sensor on the target structure whose structural response will be assessed. Therefore, TLS can resolve the limitations of conventional sensors based on strain monitoring. This article presents a computational model for the automatic estimation of the stresses of beam structures using TLS in association with a finite element method. The method is experimentally applied to the stress estimation of a simply supported steel beam subjected to a concentrated load.

Polynomial Chaos Representation for Identification of Mechanical Characteristics of Instrumented Structures

Abstract: The modeling of in-service behavior is of first importance when reassessing complex structures like harbor structures and when performing risk analysis. To this aim, the monitoring of structures allows assessment of the level of loading and to provide more realistic models for mechanical behavior or input values for their parameters. Moreover, for complex structures and due to building hazards, a stochastic modeling is needed to represent the large scatter of measured quantities. In this article, a step-by-step procedure for structural identification is presented. A decomposition of random variables on Polynomial Chaos is selected and it is shown to represent better the basic variables in comparison to preselected distribution functions, when considering maximum likelihood estimate. The decomposed variables are used for a stochastic analysis to be further updated with available monitoring data. The model can be used to follow the structure behavior during in-service or extreme conditions and to perform a reliability analysis. The proposed procedure will be carried out by using available data from the monitoring of a pile-supported wharf in the Port of Nantes, in France, but it can be generalized to similar monitored structures.

An Expert System for Auditing Quality Management Systems in Construction

Abstract: : This article introduces a system called Construction Quality Management Audit (CQMA) Expert that assesses the performance of a quality management system (QMS) implemented in a construction firm. CQMA Expert is programmed by using MATLAB's GUI components and its Fuzzy Logic Toolbox. CQMA Expert's rule base is constructed using information obtained from auditors of QMSs. CQMA Expert imports the quality requirements relative to the many quality management processes specified in ISO 9000, processes audit inputs, and generates consistent decisions relative to conformance to standards. It provides an interactive user interface for recording evidence collected during the audit and clearly states the reasons for the conclusions. It contributes to continuous quality improvement because (1) it enhances the maintenance of a QMS by quantifying its performance, (2) it assists with and facilitates the implementation of the duties of auditors in charge of assessing the performance of a QMS, (3) it simplifies the burdensome process involved in keeping track of the audit results of the many quality management processes investigated, and (4) it reduces the impact of the variability caused by the use of different auditors assessing different quality management processes. Case studies based on Section 4.11 of the ISO 9000 standard entitled “Control of inspection, measuring and test equipment” are used to illustrate the system and verify its usability and validity.

Active Control of Daylighting Features in Buildings

Abstract: : Due to increasing awareness of the importance of energy efficiency, daylighting features such as light shelves are becoming more and more popular. A light shelf is a horizontal or inclined projection with a high reflectivity meant to increase the depth of daylight penetration into a room. Currently, a light shelf is treated as a passive design element. It is designed to maximize the average distribution of daylight during the operating hours of a building and its geometry is not adapted to the changing conditions during the day. This article discusses a methodology for the active control of light shelves. A light shelf system whose geometry can be adapted is presented. The control of this system is treated as a global optimization problem. Geometrical parameters of light shelves are computed in real time to minimize the energy required for artificial lighting. An example of an office building is taken to illustrate the hourly energy savings possible through active control. It is demonstrated that building automation and control have considerable potential for energy savings.

High-Order Computational Scheme for a Dynamic Continuum Model for Bi-Directional Pedestrian Flows

Abstract: In this article, the authors present a high-order weighted essentially non-oscillatory (WENO) scheme, coupled with a high-order fast sweeping method, for solving a dynamic continuum model for bi-directional pedestrian flows. The dynamic continuum model for bi-directional pedestrian flows is reviewed first, which is composed of a coupled system of a conservation law and an Eikonal equation. Next, the authors present the first-order Lax–Friedrichs difference scheme with first-order Euler forward time discretization, the third-order WENO scheme with third-order total variation diminishing (TVD) Runge–Kutta time discretization, and the fast sweeping method, and demonstrate how to apply them to the model under study. A comparison of the numerical results of the model from the first-order and high-order methods is provided, and it is concluded that the high-order method is more efficient than the first-order one, and they both converge to the same solution of the physical model.