Mobile bridges have been used for a broad range of applications including military transportation or disaster restoration. Because mobile bridges are rapidly deployed under a wide variety of conditions, often remaining in place for just minutes to hours, and have irregular usage patterns, a detailed record of usage history is important for ensuring structural safety. To facilitate usage data collection in mobile bridges, a new acceleration-based vehicle classification technique is proposed to automatically identify the class of each vehicle. Herein we present a new technique that is based on the premise that each class of vehicles produces distinctive dynamic patterns while crossing this mobile bridge, and those patterns can be extracted from the system's acceleration responses. Measured acceleration signals are converted to time–frequency images to extract two-dimensional patterns. The Viola–Jones object detection algorithm is applied here to extract and classify those patterns. The effectiveness of the technique is investigated and demonstrated using laboratory and full-scale mobile bridges by simulating realistic scenarios.

Heavy traffic volume coupled with insufficient capacity due to limited space cause most of traffic congestion at urban signalized intersections. This article presents an innovative design to increase the capacity of heavily congested intersections by using the special width approach lane (SWAL), which consists of two narrow approach lanes that are dynamically utilized by either two passenger cars or a heavy vehicle (e.g., buses or trucks) depending on the composition of traffic. The impact of the SWAL on the saturation flow rate is quantified and validated, followed by an optimization model for best geometric layout and signal timing design with the presence of the SWAL. The optimization model is formulated as a mixed-integer-linear-program for intersection capacity maximization which can be efficiently solved by the standard branch-and-bound technique. Results of extensive numerical analyses and case studies show the effectiveness of SWAL to increase intersection capacity, indicating its promising application at intersections with very limited space that prevents the addition of separate lanes.

This article aims to automatically obtain the geometrical inventory of road cross-sections (i.e., number of lanes, width of the roadway, width of the shoulders, width of the lanes, superelevation) using a mobile laser scanning (MLS) system. Because of the large amount of data captured by the MLS, we have developed a methodology that is based on a process of segmentation, classification, and extraction, which allows us to determine the geometrical cross-section parameters. The process was validated with real data from motorways, and satisfactory results were obtained in the analyzed scenarios.

In ground motion prediction, the key is to develop a suitable and reliable GMPE (ground motion prediction equation) characterizing the ground motion pattern of the target seismic region. There are two critical goals encountered in GMPE development. Proposing a suitable predictive formula applicable to target seismic region has attracted much of the attention in previous studies. On the other hand, dependence between prediction–error variance and ground motion data has been observed and the study on this kind of heterogeneous relation becomes an important task yet to be explored. In this article, a novel HEteRogeneous BAyesian Learning (HERBAL) approach is proposed for achieving these two goals simultaneously. The homogeneity assumption on error in the traditional learning approach is relaxed, so the proposed approach is applicable for more general heterogeneous cases. With the generalization made on the traditional Bayesian learning by embedding the derived closed form expression for error variance parameter optimization component into the hyperparameter optimization of ARD (automatic relevance determination) prior, the proposed learning approach is capable of performing continuous model training on a prescribed predictive formula with unknown error pattern. A database of strong ground motion records in the Tangshan region of China is obtained for the analysis. It is shown that the trained optimal model class by the proposed approach is promising as that, the trained optimal model class retains model simplicity of the predictive formula with capability on both robustness enhancement ground motion prediction and precise determination of the error pattern.

This article tackles the real-world planning problem of railway operations. Improving the timetable planning process will result in more reliable product plans and a higher quality of service for passengers and freight operators. We focus on the microscopic models for computing accurate track blocking times for guaranteeing feasibility and stability of railway timetables. A conflict detection and resolution model manages feasibility by identifying conflicts and computing minimum headway times that provide conflict-free services. The timetable compression method is used for computing capacity consumption and verifying the stability according to the UIC Capacity Code 406. Furthermore, the microscopic models have been incorporated in a multilevel timetabling framework for completely automated generation of timetables. The approach is demonstrated in a real-world case study from the Dutch railway network. Practitioners can use these microscopic timetabling models as an important component in the timetabling process to improve the general quality of timetables.

The traffic safety of a railway bridge is generally evaluated by levels of structural responses such as acceleration, vertical displacement, and deck twist. Whereas acceleration can be readily measured in general, acquiring displacement and twist responses in field testing is often a challenging task due to lack of appropriate sensors. As most existing displacement transducers are designed to measure at a single location, the deck twist which is calculated from four displacements requires costly and labor-intensive sensor instrumentation. To effectively address the issue, this study proposes an integral strategy for the traffic safety evaluation of railway bridges using multisensor data. The proposed approach provides a formulation to estimate the dense displacement necessary for obtaining twist responses using acceleration and strain measurements. Wireless sensors are adopted because of their intrinsic advantages in multimetric sensing of heterogeneous data, convenient sensor instrumentation, and high-fidelity time-synchronized data acquisition. The proposed approach for dense displacement estimation is numerically and experimentally validated using beam models. Subsequently, a full-scale experiment on a railway bridge is conducted to evaluate the traffic safety for high-speed trains at three different speeds of 280 km/h, 300 km/h, and 400 km/h. The acceleration, vertical displacement, and twist are obtained and compared with design limits to determine the traffic safety of the railway bridge.

Infrastructure networks play an important role in improving economic prosperity, enabling movement of resources, and protecting communities from hazards. As these networks serve population, they evolve in response to social, economic, environmental, and technological changes. Consideration of these interactions has thus far been limited by use of simplified data sets and idealized network structures, and is unable to explain the complexity and suboptimal structures displayed by real infrastructure networks. This article presents a new computational model that simulates the growth and evolution of infrastructure systems. Empirical evidence obtained from analysis of nontrivial real-world data sets is used to identify the mechanisms that guide and govern system-scale evolution of infrastructure networks. The model investigates the interplay of three key drivers, namely network demand, network efficiency, and network cost in shaping infrastructure network architectures. The validity of the model is verified by comparing key topological and spatial properties of simulated networks with real-world networks from six infrastructure sectors. The model is used to develop and explore different scenarios of infrastructure network futures, and their resilience is shown to change as a result of different infrastructure management policies. The model can therefore be used to identify system-wide infrastructure engineering strategies to reduce network costs, increase network efficiency, and improve the resilience of infrastructure networks to disruptive events.

Travel time prediction is one of the most important components in Intelligent Transportation Systems implementation. Various related techniques have been developed, but the efforts for improving the applicability of long-term prediction in a real-time manner have been lacking. Existing methods do not fully utilize the advantages of the state-of-the-art cloud system and large amount of data due to computation issues. We propose a new prediction framework for real-time travel time services in the cloud system. A distinctive feature is that the prediction is done with the entire data of a road section to stably and accurately produce the long-term (at least 6-hour prediction horizon) predicted value. Another distinctive feature is that the framework uses a hierarchical pattern matching called Multilevel k-nearest neighbor (Mk-NN) method which is compared with the conventional k-NN method and Nearest Historical average method. The results show that the method can more accurately and robustly predict the long-term travel time with shorter computation time.

*Physics-based models are intensively studied in mechanical and civil engineering but their constant increase in complexity makes them harder to use in a maintenance context, especially when degradation model can/should be updated from new inspection data. On the other hand, Markovian cumulative damage approaches such as Gamma processes seem promising*; *however, they suffer from lack of acceptability by the civil engineering community due to poor physics considerations. In this article, we want to promote an approach for modeling the degradation of structures and infrastructures for maintenance purposes which can be seen as an intermediate approach between physical models and probabilistic models. A new statistical, data-driven state-dependent model is proposed. The construction of the degradation model will be discussed within an application to the cracking of concrete due to chloride-induced corrosion. Numerical experiments will later be conducted to identify preliminary properties of the model in terms of statistical inferences. An estimation algorithm is proposed to estimate the parameters of the model in cases where databases suffer from irregularities*.

The emerging taxi services, for instance, Uber and Lyft, are challenging traditional fully regulated taxi markets. Transportation agencies are spending significant efforts to understand the optimal pricing and fleet size taxis that are efficient for a given urban area. This study develops a modeling framework for studying a decentralized equilibrium based market study where the fare is strictly regulated by a taxi commission. The nature of demand-supply equilibria with stochastic demand are discussed to determine optimal development strategies, for instance, number of issued licenses and fare setting. Two Stackelberg games are formulated to specify leader-follower relationships between transportation authorities and and the followers — taxi drivers and passengers. An iterative approach is designed to simulate the games and solve corresponding mathematical optimization problems. The case study is based on New York City data which shows that the taxi market may be oversupplied and underpriced, which confirms findings from other studies and price hikes in 2012. Furthermore, different development strategies are proposed based on two Stackelberg games to respond to intended taxi system changes, such as price and quantity elasticity of taxi demand, levels of demand variance, average taxi operation speed, passengers' waiting time value, and taxi service coverage. The results have important implications in determining development strategies for taxi industry with emerging taxi services, stochastic demand, and the rapidly changing environment.

In real conditions, decision makers usually deal with multiple objectives and should make a decision in a state of certainty or uncertainty. The selection of the best constructions for a building from a number of alternatives is of great importance for owners, contractors, and stakeholders. Dozens of multicriteria/multi-attribute decision-making (MCDM/MADM) models developed for evaluating the performance of the available alternatives can be used for selecting the most suitable alternative from a given finite set of options based on a set of attributes. A guide to systematic selection among the available alternatives of building structures is the integrated methodology, thoroughly analyzed in the article. The article presents a MCDM model for selecting the type of foundation for a single-storey dwelling house based on the WASPAS-G (Weighted Aggregated Sum Product Assessment) method and Analytic Hierarchy Process (AHP) approach. The aggregate criteria weights are determined by using the AHP and experts’ judgement methods.

A Bayesian network model is developed, in which all the items or elements encountered when travelling a railway line, such as terrain, infrastructure, light signals, speed limit signs, curves, switches, tunnels, viaducts, rolling stock, and any other element related to its safety are reproduced. Due to the importance of human error in safety, especial attention is given to modeling the driver behavior variables and their time evolution. The sets of conditional probabilities of variables given their parents, which permits quantifying the Bayesian network joint probability, are given by means of closed formulas, which allow us to identify the particular contribution of each variable and facilitate a sensitivity analysis. The probabilities of incidents affecting safety are calculated so that a probabilistic safety assessment of the line can be done and its most critical elements can be identified and sorted by importance. This permits improving the line safety and saving time and money in the maintenance program by concentrating on the most critical elements. To reduce the complexity of the problem, an original method is given that permits dividing the Bayesian network in to small parts such that the complexity of the problem becomes linear in the number of items and subnetworks. This is crucial to deal with real lines in which the number of variables can be measured in thousands. In addition, when an accident occurs the Bayesian network allows us to identify its causes by means of a backward inference process. The case of the real Palencia–Santander line is commented on and some examples of how the model works are discussed.

*An optimization model is developed to guide recovery of a disrupted water distribution system. The model minimizes the total cost of recovery, including the disruption cost of unmet demand during the repair process and the repair cost itself. The optimization schedules repair tasks under precedence and resource constraints and contains an embedded flow problem that optimizes the distribution of water in each time period, given the state of the network. A simulated annealing algorithm is developed for scheduling the tasks, with the embedded flow problem solved using a generalized reduced gradient method. Experiments with a test water distribution system confirm the effectiveness of the model and provide insight regarding the effects of limited resources available for recovery and of the usefulness of having multiple modes for execution of specific tasks*.

An infrastructure adapted to dynamic wireless recharging of electric vehicles is often referred to generically as Electric Road (“e-road”). E-roads are deemed to become essential components of future grid environments and smart city strategies. Several technologies already exist that propose different ways to integrate dynamic inductive charging systems within the infrastructure. One e-road solution uses a very thin rail with box-section made of fibre-reinforced polymer, inside which an electric current flows producing a magnetic field. In spite of the great interest and research generated by recharging technologies, the structural problems of e-roads, including vibrations and structural integrity in the short and/or long period, have received relatively little attention to date. This article presents a novel computational methodology for assessing the time-dependent structural performance of e-roads, including a recursive strategy for the estimation of the lifetime of surface layers. The article also reports some numerical findings about e-roads that will drive further numerical analyses and experimental studies on this novel type of infrastructure. Finally, numerical simulations have been conducted to compare an e-road with a traditional road (“t-road”), in terms of static, dynamic and fatigue behavior.

The growing use of composite materials on aircraft structures has attracted much attention for the impact monitoring as a kind of structural health monitoring method. Uniform linear sensor array (ULSA)-based multiple signal classification (MUSIC) technology is a promising method because of its directional scanning ability and easy arrangement of the sensor array. However, the monitoring range of ULSA-based MUSIC method is 0°–180°, and its beamforming properties degrade at angles close to 0° and 180°. Besides, the ULSA-based MUSIC methods proposed require the knowledge of the direction dependent velocity profile obtained by additional experiments. This article presents a novel two-dimensional (2-D) plum-blossom sensor array (PBSA)-based MUSIC method. First, the velocity propagating at the specific direction is estimated by impact signal itself using PBSA directly. Second, 2-D PBSA-based MUSIC method well realizes omnidirectional 0°–360° impact localization of composite structures. Experimental results show its successful performance on epoxy laminate plate and complex composite structure.

The extreme importance of emergency response in complex buildings during natural and human-induced disasters has been widely acknowledged. In particular, there is a need for efficient algorithms for finding safest evacuation routes, which would take into account the 3-D structure of buildings, their relevant semantics, and the nature and shape of hazards. In this article, we propose algorithms for safest routes and balanced routes in buildings, where an extreme event with many epicenters is occurring. In a balanced route, a trade-off between route length and hazard proximity is made. The algorithms are based on a novel approach that integrates a multiattribute decision-making technique, Dijkstra's classical algorithm and the introduced hazard proximity numbers, hazard propagation coefficient and proximity index for a route.

Existing computer models used to optimize railway timetables lead to a high complexity when the number of analyzed services exceeds a given threshold. A time partitioning technique is proposed which allows line design and timetable optimization and a reduction in the complexity of the problem by considering small time windows of the same or different durations in which the timetables of a small (equal or not) number of running trains are optimized in sequence. Though the optimal solution is not expected to be attained with this method, the analyzed examples demonstrate that the resulting solution is close to the global optimum and practically satisfactory. This technique can be used at the planning and implementation stages. Examples of two real lines are analyzed to show the goodness of the proposed methods. One is the network Madrid–Sevilla–Toledo–Málaga–Valencia–Albacete with a dense traffic of 170 trains per day. The second is the Palencia–Santander line with 70 trains in which as an alternative to a double-track proposal with a cost of M€ 3,200 an alternate double–single-track (ADST) solution, with a cost of M€ 330 (one tenth) is proposed.

This article defines, formulates, and solves a new equilibrium traffic assignment problem with side constraints—the traffic assignment problem with relays. The relay requirement arises from the driving situation that the onboard fuel capacity of vehicles is lower than what is needed for accomplishing their trips and the number and distribution of refueling infrastructures over the network are under the expected level. We proposed this problem as a modeling platform for evaluating congested regional transportation networks that serve plug-in electric vehicles (in addition to internal combustion engine vehicles), where battery-recharging or battery-swapping stations are scarce. Specifically, we presented a novel nonlinear integer programming formulation, analyzed its mathematical properties and paradoxical phenomena, and suggested a generalized Benders decomposition framework for its solutions. In the algorithmic framework, a gradient projection algorithm and a labeling algorithm are adopted for, respectively, solving the primal problem and the relaxed master problem—the shortest path problem with relays. The modeling and solution methods are implemented for solving a set of example network problems. The numerical analysis results obtained from the implementation clearly show how the driving range limit and relay station location reshape equilibrium network flows.

The three-dimensional mapping of the built environment is of particular importance for engineering applications such as monitoring work-in-progress and energy performance simulation. The state-of-the-art methods for fitting primitives, non-uniform B-Spline surface (NURBS) and solid geometry to point clouds still fail to account for all the topological variations or struggle with mapping of physical space to parameter space given unordered, incomplete, and noisy point clouds. Assuming an input of points that can be described by a single non-self-intersecting NURBS, this article presents a new method that leverages segmented point clouds and outputs NURBS surfaces. It starts by successively fitting uniform B-Spline curves in two-dimensional as planar cross-sectional cuts on each surface. An intermediate B-Spline surface is then computed by globally optimizing and lofting over the cross-sections. This surface is used to parameterize the points and perform final refinement to a NURBS. For cylindrical segments such as pipes, a new supervised method is also introduced to string the fitted segments, identify connection types, standardize the connections, and then refine them using NURBS optimization. Experimental results show the applicability of the proposed methods for as-built modeling purposes.

A vehicle equipped with a vehicle-to-vehicle (V2V) communications capability can continuously update its knowledge on traffic conditions using its own experience and anonymously obtained travel experience data from other such equipped vehicles without any central coordination. In such a V2V communications-based advanced traveler information system (ATIS), the dynamics of traffic flow and intervehicle communication lead to the time-dependent vehicle knowledge on the traffic network conditions. In this context, this study proposes a graph-based multilayer network framework to model the V2V-based ATIS as a complex system which is composed of three coupled network layers: a physical traffic flow network, and virtual intervehicle communication and information flow networks. To determine the occurrence of V2V communication, the intervehicle communication layer is first constructed using the time-dependent locations of vehicles in the traffic flow layer and intervehicle communication-related constraints. Then an information flow network is constructed based on events in the traffic and intervehicle communication networks. The graph structure of this information flow network enables the efficient tracking of the time-dependent vehicle knowledge of the traffic network conditions using a simple graph-based reverse search algorithm and the storage of the information flow network as a single graph database. Further, the proposed framework provides a retrospective modeling capability to articulate explicitly how information flow evolves and propagates. These capabilities are critical to develop strategies for the rapid flow of useful information and traffic routing to enhance network performance. It also serves as a basic building block for the design of V2V-based route guidance strategies to manage traffic conditions in congested networks. Synthetic experiments are used to compare the graph-based approach to a simulation-based approach, and illustrate both memory usage and computational time efficiencies.

In this article, an accurate method for the registration of point clouds returned by a 3D rangefinder is presented. The method modifies the well-known iterative closest point (ICP) algorithm by introducing the concept of deletion mask. This term is defined starting from virtual scans of the reconstructed surfaces and using inconsistencies between measurements. In this way, spatial regions of implicit ambiguities, due to edge effects or systematical errors of the rangefinder, are automatically found. Several experiments are performed to compare the proposed method with three ICP variants. Results prove the capability of deletion masks to aid the point cloud registration, lowering the errors of the other ICP variants, regardless the presence of artifacts caused by small changes of the sensor view-point and changes of the environment.

An on-site earthquake early warning system (EEWS) can provide more lead-time at regions that are close to the epicenter of an earthquake because only seismic information of a target site is required. Instead of leveraging the information of several stations, the on-site system extracts some P-wave features from the first few seconds of vertical ground acceleration of a single station. It then predicts the intensity of the forthcoming earthquake at the same station according to these features. However, the system may be triggered by some vibration signals that are not caused by an earthquake or by interference from electronic signals, which may consequently result in a false alarm at the station. Thus, this study proposes two approaches to distinguish the vibration signals caused by non-earthquake events from those caused by earthquake events based on support vector classification (SVC) and singular spectrum analysis (SSA). In the first approach (Approach I), the fast Fourier transform algorithm and the established SVC model are employed to classify the vibration signals. In the second approach (Approach II), a SSA criterion is added to Approach I for the purpose of identifying earthquake events that are classified as non-earthquake events by the SVC model with increased accuracy. Both approaches are verified by using data collected from the Taiwan Strong Motion Instrumentation Program and EEW stations of the National Center for Research on Earthquake Engineering. The results indicate that both of the proposed approaches effectively reduce the possibility of false alarms caused by an unknown vibration event.

Adaptable active control strategies besides advance sensors and actuators technologies lead to higher performance of vibrational control in civil infrastructures under severe ground motions. These resilience control systems are robust against model uncertainties as well as being online recoverable from the malfunctioning of sensors and actuators. In this study, resilient control system based on sliding mode (SM) fault detection observer and SM fault tolerant control is improved for actuator fault in large-scale systems. The SM fault detection observer is modified for eliminating the excessive chattering in estimating states and actuators’ fault, and the reconfigurable SM fault tolerant control is improved to minimizing input forces in control framework under seismic action. Design of observer and controller is performed using linear matrix inequalities. Numerical simulations on the cable-stayed bridge benchmark demonstrate the effectiveness of the proposed fault-tolerant system. Despite the high order of this large-scale structure, the proposed fault detection and diagnosis method can effectively find the location and size of faults in actuators without performance degradation and computational costs. The fault-tolerant controller maintains the performance of the structure at an acceptable level in the post-fault case by redistribution of control signal to actuators.