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A reasonable method for estimating alighting stops can provide a data guarantee for schedule design and vehicle scheduling. However, few studies, to our best knowledge, have addressed this problem using the variable distance threshold for different types of passengers. This paper is based on available smart card data, and a two-stage algorithm is designed to obtain missing alighting stations in the entry-only urban public transportation system. This algorithm divides passengers into seven types, determines varying distance thresholds for different passengers, and includes five assumptions. Based on the first three assumptions, the corresponding subalgorithms are designed, respectively, constituting the first-stage algorithm, and the first match for all passengers' alighting stations is carried out. The second-stage algorithm is designed on the basis of the latter two assumptions. A secondary match is conducted on data that were not successfully matched in the first stage to ensure that alighting stops for all passengers are identified. The proposed methods are verified using urban public transport data from the city of Zhuhai as an example. Research results show that the drop-off stops where approximately 74% of passengers can be determined after matching the first-stage algorithm. The distance thresholds for various passengers are different, and the distance thresholds for the same type of passengers over different days are also different. Compared with the traditional fixed distance threshold, the variable distance threshold for various passengers proposed in this paper can improve the match rate of alighting station estimate, about 9%. By appropriately extending the distance thresholds for different passengers, the match rate for passengers who work or live in the suburbs can be further improved, which is in line with the actual travel status of such passengers. The second-stage algorithm can identify alighting sites for a single or unlinked trip. The efficiency of the methodology proposed in this study can meet the needs for practical applications, which may be applied to urban public transport systems. [ABSTRACT FROM AUTHOR]

Collision accidents of construction vehicles frequently occur during the construction of building projects. It is difficult to warn of collision risk efficiently by using the safety control methods of on-site inspection and viewing surveillance video. Therefore, a novel collision risk warning model for construction vehicles is proposed in this paper. The model comprises a state awareness module and a collision risk assessment module. In the state awareness module, the YOLOv7 algorithm is used to identify and locate construction vehicles. The DeepSORT algorithm is used for the real-time tracking of construction vehicles. Finally, the state awareness module obtains a wealth of sensing information and provides it to the collision risk assessment module. In the collision risk assessment module, the speed assessment method and the minimum safety distance assessment method of construction vehicles are innovatively proposed and compiled. In the speed assessment method, the speed of construction vehicles is calculated and speeding warnings are performed using the transformation matrix between latitude and longitude coordinates and image pixel coordinates. In the minimum safety distance assessment method, the original surveillance picture is converted into a bird's eye view (BEV) of the world coordinate system through inverse perspective mapping (IPM). Then the distance of all construction vehicles is traversed, and the visual warning of the minimum safe distance is provided. The effectiveness and robustness of this collision risk warning model were verified by taking three practical projects in different construction stages as examples. The research results show that the mean average precision (mAP@0.5:0.95) of the construction vehicles collision risk warning model can reach 89.10%, and it has a good tracking effect and an efficient collision risk warning strategy. [ABSTRACT FROM AUTHOR]

Tensegrity structures, which consist of tension cables and compression rods, are widely used in various fields. It is particularly important to find the tensegrity with definite geometric configuration for the following research and application. This paper presents a traversal topology-finding method for tensegrity structures based on dynamic programming algorithm and ground structure method. After the designer has given the cable topology, the dynamic programming is adopted to realize the traversal selection of the rod. In order to reduce the traversal space and realize the constraint of rod length type, the rod is classified according to length. Compared with existing topology-finding methods, the algorithm can not only output all feasible topologies but also incorporate structural constraints, such as rod length type and prestressed stability, into the calculation process. Four tensegrity numerical examples illustrate the feasibility and effectiveness of the topology-finding method. [ABSTRACT FROM AUTHOR]

To improve the current manual and iterative nature of clash resolution on construction projects, current research efforts continue to explore and test the utilization of machine-learning algorithms to automate the process. Though current research shows significant accuracy in automating clash resolution, many have failed to provide clear explanation and justification for the selection of their feature and label space. Since this is critical in developing an effective and explainable solution in machine learning, it is crucial to address this research gap. In this paper, the authors utilize an in-depth literature review and industry interviews to capture domain knowledge on how design clashes are resolved by industry experts. From analysis of the knowledge captured, we identified 23 factors considered by experts when resolving clashes and five alternative solutions/options to resolve a clash. Using a pool of industry experts, a modified Delphi approach was conducted to validate the factors and options and to determine a priority ranking. The authors identified 94 industry experts based on a predetermined qualification matrix to take part in the modified Delphi. Twelve participants responded and took part in the first round, and 11 completed the second round. A consensus was reached on all clash factors and resolution options. Factors including "clashing elements type," "constrained slope," "critical element in the clash," "location of the clash," "code compliance," and "project stage clashing element is in" were ranked as the most important factors, while "clashing element material" and "insulation type" were considered the least important. Participants also showed more preference to the "moving the clashing element with low priority in/along x-y-z directions" option to resolve clashes. These identified factors and options will be utilized to collect specific clash data to train and test effective and explainable machine-learning algorithms toward automating clash resolution. [ABSTRACT FROM AUTHOR]

To improve the current manual and iterative nature of clash resolution on construction projects, current research efforts continue to explore and test the utilization of machine-learning algorithms to automate the process. Though current research shows significant accuracy in automating clash resolution, many have failed to provide clear explanation and justification for the selection of their feature and label space. Since this is critical in developing an effective and explainable solution in machine learning, it is crucial to address this research gap. In this paper, the authors utilize an in-depth literature review and industry interviews to capture domain knowledge on how design clashes are resolved by industry experts. From analysis of the knowledge captured, we identified 23 factors considered by experts when resolving clashes and five alternative solutions/options to resolve a clash. Using a pool of industry experts, a modified Delphi approach was conducted to validate the factors and options and to determine a priority ranking. The authors identified 94 industry experts based on a predetermined qualification matrix to take part in the modified Delphi. Twelve participants responded and took part in the first round, and 11 completed the second round. A consensus was reached on all clash factors and resolution options. Factors including "clashing elements type," "constrained slope," "critical element in the clash," "location of the clash," "code compliance," and "project stage clashing element is in" were ranked as the most important factors, while "clashing element material" and "insulation type" were considered the least important. Participants also showed more preference to the "moving the clashing element with low priority in/along x-y-z directions" option to resolve clashes. These identified factors and options will be utilized to collect specific clash data to train and test effective and explainable machine-learning algorithms toward automating clash resolution. [ABSTRACT FROM AUTHOR]

In this paper, a framework of active obstacle avoidance for autonomous vehicles based on the hybrid soft actor-critic (SAC) algorithm is proposed. In the stage of local path planning, a comprehensive cost function considering collision risks, deviation from the global route, road lines crossing, and driving comfortability is developed to provide a local optimal path avoiding both static and dynamic obstacles considering multiple predicting timesteps. Then, a path tracking controller on the foundation of a hybrid SAC algorithm is designed to mitigate the problem of high sample complexity caused by random initialization of parameters in conventional reinforcement learning approaches. Model predictive control (MPC) plays a guiding role by applying its control action to combine with the action of SAC online to obtain a more effective state and reward information for training. The mechanism of the combination of MPC with SAC to balance the exploration and reliability is explained in detail. In order to improve the convergence rate and learning efficiency, a dual actor network structure for two different control actions is adopted. With considerations of various relevant factors influencing the control effect, the reward for the hybrid SAC algorithm is designed carefully. Finally, the results of simulation experiments illustrate that the proposed approach performs effectively with the assurance of safety and driving comfortability. In summary, the hybrid SAC algorithm with dual actor networks performs better than other algorithms for comparison in all test scenarios in this paper. [ABSTRACT FROM AUTHOR]

The estimation of construction quantities is a critical aspect of any construction project, and accuracy in the quantity takeoff (QTO) process is an integral part in achieving project success. Accuracy can be improved by automating the QTO process. However, one of the gaps in automated QTO is found to be the lack of a platform-ignorant computing algorithm regarding different building information modeling (BIM) authoring tools. Available BIM-based commercial cost estimation software programs such as Autodesk Naviswork, Autodesk QTO, iTWO CostX, Innovaya Visual QTO, ConstrucSim, and so forth, are limited in terms of full automation, largely because full interoperability across all the different applications has not yet been achieved. This paper explores interoperable QTO algorithms using industry foundation classes (IFC)-based BIM. To evaluate the IFC-based QTO algorithms, a case study was conducted on a residential development project in Kalamazoo, MI. Comparing with commercial software used in practice, the IFC-based QTO algorithms showed consistent results, whereas were more powerful regarding their independence from BIM authoring tools in extracting the volumetric and areal quantities of building components. The IFC-based QTO algorithms processed the different types of building element configurations in the residential development project smoothly. Furthermore, the QTO algorithms are expected to reduce the manual input required in generating QTO through interfacing with IFC data which is the ISO international standard for BIM. [ABSTRACT FROM AUTHOR]

This paper describes the design and optimization of a cooling strategy based on a battery cooling system with indirect liquid-cooled plate heat exchange. The performance of the battery cooling system was analyzed using single-factor thermal simulation analysis. A cosimulation platform was built, and the rule-based multiparameter control strategy was used to design the heat dissipation logic, which was verified in the cosimulation platform. To improve the heat dissipation performance further, an orthogonal test (OT) was used to analyze the effects of different control parameters on each performance index during the battery discharge process, and the optimal combination of control parameters under a balanced multiobjective strategy was obtained. The results of the particle swarm optimization (PSO) algorithm with different weight coefficients were introduced for comparison. It was found that the OT and PSO can keep the temperature difference within the safe range. The maximum temperature was reduced by 3.57 and 4.10 K, respectively, and the average output power was increased by 11% and 9%, respectively, which indicates that the OT maintained good performance despite the significant workload reduction. The feasibility and reliability of the battery thermal management system control strategy optimized using OT were verified. [ABSTRACT FROM AUTHOR]

The origin and destination (O-D) of public transit passengers are important for the planning and operation of the transit system. However, only 46% of public transit agencies have a smart card system in the US, and most of them require an entry-only tap, which prohibits identifying passenger destinations unless utilizing an estimation model. Therefore, there is a need for a cost-effective and automated solution to facilitate the majority of the US transit agencies in recognizing the origin and destination of passengers as well as capturing passenger transfers. This paper created a novel algorithm for transit agencies to count passengers with the consideration of transfers using a costeffective Wi-Fi sensing--based approach. Two pilot studies were conducted in the city of Louisville, Kentucky on three different bus routes to explore the feasibility of the method. AWi-Fi detector was installed in the bus to detect passengers, and a manual counting was performed to be used as ground truth data. After data collection, the proposed algorithms were applied to optimize the detection radius and to eventually find origins, destinations, and transfers. Analysis revealed that the proposed Wi-Fi--based approach is capable of recognizing 78.7% of the total passengers as well as detecting their boarding and alighting activities. The paper demonstrates the ability of the proposed method to detect passengers with a reasonable detection rate by using Wi-Fi technology on bus routes, which makes it feasible for transit agencies to conduct frequent and low-cost network-level passenger O-D studies. [ABSTRACT FROM AUTHOR]

The paper proposes an improved single-step method of explicit displacement and velocity (SSMEDV) with controllable numerical damping based on the discrete control theory, which can be applied to real-time hybrid simulation (RTHS). The stability, overshoot, and numerical damping characteristics of the proposed algorithms are studied. It is shown that certain algorithms have unconditional stability for linear and softening nonlinear structures. The overshoot phenomenon is small, where the displacement is one power of the time step due to the initial velocity, whereas the velocity does not. The amount of numerical damping is adjusted by a single parameter to control the divergence of false higher-order modes. The analysis of RTHS was performed on a multiple degrees-of-freedom (MDOF) structure and complex building–damper structure to verify the theoretical analysis. The comparison with two typical algorithms of the explicit Newmark and Gui- λ demonstrates the effectiveness of the proposed algorithms in improving stability, accuracy, and computational efficiency, which can be applied to complex RTHS. [ABSTRACT FROM AUTHOR]

The stochastic subspace algorithm is one of the most widely used structural identification techniques, which is generally involved with the stabilization diagram for estimating modal parameters. However, the conventional stabilization diagram has an inherent problem: some spurious modes may be identified as stable results, resulting in adverse effects on structural modal identification. To address this critical issue, this paper proposes an improved stochastic subspace algorithm involving a Monte Carlo–based stabilization diagram. Through a numerical simulation study, the good performance of the Monte Carlo–based stabilization diagram for discriminating the poles denoting the physical modes from those representing spurious modes is demonstrated. The numerical simulation results show that the proposed method can estimate structural modal parameters with high accuracy and robustness. Moreover, the proposed method is applied to field measurements on a 600-m-high skyscraper during Super Typhoon Mangkhut, and the results verify the applicability and effectiveness of the proposed method to field measurements. This paper aims to provide an effective tool for accurate estimation of modal parameters of civil structures. [ABSTRACT FROM AUTHOR]

For a pilot, the angle of attack (AoA) estimation may represent a possibility to cross-check the traditional cockpit indicators and also to obtain information whose benefits are widely recognized in many inflight situations. This paper proposes an AoA algorithm—named ACF—capable of detecting fast changes in the actual signal, that is a useful stall prevention capability, for example. The solution is based on the complementary filtering methodology and does not involve any airdata sensor output, with consequent saving of weight and cost reduction. In particular, it does not use the airspeed measurement, easily subject to failure and has been identified as the root cause of several fatal accidents. Consequently, the ACF shows the same (high) reliability as the used inertial sensors. Furthermore, the ACF algorithm requires neither information on the aircraft aerodynamic coefficients nor forecast wind information to be preloaded onboard, thus being time and cost effective. The ACF is also characterized by a low computational load, only needing a few dozen of algebraic operations. In this work, the ACF performances have been assessed using both a detailed simulation environment and real flight data. In the former case, an airliner following a sample maneuver is assumed, and a mean root mean square (RMS) less than 3° is guaranteed under all possible conditions of wind, gust, and turbulence. In the latter case, data collected in two different general aviation flights are used, providing a mean RMS of less than 0.3°. During piloting, the angle of attack (AoA) readings may represent a possibility to cross-check the traditional indicators and to obtain information whose benefits are recognized in many inflight situations. Unfortunately, the AoA sensors are exposed to several possible failures, often common to the other airdata such as airspeed and altitude. In addition, especially for small aerial vehicles, the systems cannot accommodate direct sensors due to size, weight, and power constraints. For these reasons, having an AoA sensorless estimation would be extremely important. A software solution (ACF) is proposed to estimate the AoA without using any external sensor, saving weight and costs, and with a strong improvement of reliability since the angle vanes—and especially the air probes—are generally identified as the root cause of several inflight accidents. Due to the fact that the ACF algorithm relies on inertial measurements only, it could even be implemented in a pilot's personal device because it does not require any tuning or flight calibration. Furthermore, in spite of its simplicity, it can achieve good accuracy by detecting—in any weather and regardless of particular airplane or maneuver—fast changes in the actual AoA that may endanger the safety of the flight. [ABSTRACT FROM AUTHOR]

This paper focuses on the track-to-object association problem based on the two-line element (TLE) set. A five-step track-to-object association algorithm is designed for the radar observation data. First, for all objects in the TLE set, their orbits are propagated and stored with a step of 10 min. Then the orbital position at an arbitrary epoch can be obtained through the interpolation algorithms. Second, for tracklets longer than 60 s, the extended Laplace method is implemented to determine the orbital elements. Third, the semimajor axis, eccentricity, inclination, and the right ascension of the ascending node are used to filter the TLE sets. More than 90% of the objects can be filtered, improving the association efficiency significantly. Fourth, a first-order association process is implemented to get candidate objects, with the difference between the observed positions and the TLE orbit positions as a metric. Finally, considering the measurement error and the TLE orbit uncertainty, an uncertainty-based final association process checks the candidate objects and gives the final association result. The proposed approach is tested with simulated and observed data. The true-positive rate is higher than 99.5%, while the false-positive rate and the false-negative rate are both lower than 0.1%. [ABSTRACT FROM AUTHOR]

Estimating pavement internal forces due to the swelling or shrinking of expansive soils is of vital importance for planning, design, and construction. Such efforts can minimize some possible damage due to expansive soils during pavement services. This study extends an analytical model on the single-layer Euler–Bernoulli beam resting on the Winkler foundation to estimate pavement internal forces in the multilayer pavement structure. Besides, an improved efficient algorithm is proposed in this paper for computing the value of virtual load parameters. The proposed closed-form solution and iterative algorithm are then verified by comparing the degraded solution with the single-layer beam model and the existing iterative algorithm. The influences of the beam model (single layer or multilayer) and foundation parameters on the pavement internal forces have also been investigated through parametric studies. Furthermore, the computational efficiency is compared between the iterative algorithm adopted by the previous researcher and the iterative algorithm proposed in this paper. It can be seen that a single-layer beam model may underestimate pavement internal forces induced by expansive soils. Foundation parameters will affect the value of virtual load parameters. Also, it can be found that the iterative algorithm proposed in this paper can reduce the iterative calculation time considerably. Moreover, it can be observed that with more fitting points and fitting terms, the fitting has a more accurate effect, and the advantages of using the iterative algorithm proposed in this paper are more pronounced. [ABSTRACT FROM AUTHOR]

Detecting small leaks in time can prevent an accident from getting worse, which plays an extremely important role in safe pipeline transportation. In order to improve the ability to detect small leaks, this paper proposes a new pipeline leak detection method based on the combination of the improved fast differential algorithm and integral algorithm. This paper first reviews and analyzes the advantages and disadvantages of the fast differential algorithm; on this basis, to further improve the signal-to-noise ratio (SNR) of small leaks and the robustness of the leakage monitoring system, an integral algorithm with adaptive initialization is proposed; Next, to improve the location accuracy of small leaks, historical data are used to estimate the wave speed. The application of actual leakage test data shows that the proposed method can detect a leakage rate of about 0.3%; the false alarm rate is significantly reduced, and the positioning accuracy is improved. All of those verify the effectiveness and practicability of the proposed method. [ABSTRACT FROM AUTHOR]

Building information modeling (BIM) can facilitate effective three-dimensional (3D) earthwork modeling by furnishing insightful information. An earthwork area is generally represented in a cell-based environment for planning purposes such as allocation plans or equipment plans. However, previous studies utilized conventional methods, which are tedious and time-consuming, to create cell-based representations. Therefore, a method that can be applied to automatically represent earthwork BIM models in a cell-based environment should be developed. To address that research gap, this paper proposes a novel method to develop voxel-based representations of earthwork models. The voxel-based method is parametric, and the size, number, and properties of the voxels can be easily varied. This method, validated for accuracy, rapidly creates a parametric voxel model linked with geotechnical information necessary for earthwork operations. A visual programming tool, Grasshopper, is used to develop an algorithm that can automatically divide the earthwork model into voxels. Finally, experiments are conducted to validate the proposed method using an actual earthwork BIM design. The paper contributes to the existing body of knowledge by proposing a voxel-based earthwork representation and algorithm that automatically create a cell-based 3D environment that is flexible enough to integrate geotechnical parameters. The results indicate that the proposed method will help project engineers, planners, and managers create an optimal-size voxel-based earthwork model with customized geotechnical information. [ABSTRACT FROM AUTHOR]

Bat algorithm (BA) is one of the well-established metaheuristic algorithms based on the echolocation characteristics of microbats. Literature studies demonstrate that BA provides good results in a wide range of optimization problems due to its simple structures, easy implementation, and effectiveness. However, the standard BA has two general shortcomings when applied to complex optimization problems. The first is the high probability of falling in local optima, and the second is insufficient population diversity, leading to unwanted premature convergence. This paper proposes an improved bat algorithm based on the Doppler effect (IBA-DE) to improve the performance of the standard BA. In the proposed IBA-DE, a new equation with some idealized rules is driven from the Doppler effect and adopted for updating the bat velocities in the algorithm body. The local search part of the standard BA is also improved via searching around the better bat individuals. This part is also equipped with Lévy flight to maintain the population diversity. The performance of IBA-DE is evaluated on three real-world engineering design problems. The results obtained by these problems show that IBA-DE is superior to the basic BA and other considered algorithms. For further investigation, IBA-DE is applied to the optimal design of four special truss structures. Comparing the optimization results found by the proposed algorithm with those of some other state-of-art metaheuristics indicates that the proposed IBA-DE can be an efficient optimizer for the optimal design of real-size structures. [ABSTRACT FROM AUTHOR]

In an environment of fierce market competition, companies commonly implement several projects simultaneously and employ programs to manage these multiprojects. The program is scheduled at the tactical layer to coordinate multiple projects, each scheduled independently by its project leader. The discrete time-cost trade-off problem (DTCTP) is one of the most significant project scheduling problems, and numerous studies have been conducted on DTCTP for several decades. However, there is still a notable gap in research on DTCTP in multiproject scenarios. A program is a popular approach for managing multiple projects that are related to each other. This paper proposes a new hierarchical discrete time-cost trade-off problem (HDTCTP) for the program that integrates the scheduling problems at the different layers. We clarify the interactions between different layers in program scheduling and introduce the model of a program cascading scheduling process. The program at the upper layer allocates budgets and targets to projects at the lower layer, where each project is scheduled independently. The scheduling results at the lower layer are fed back to the program for further coordination and optimization. This program iteratively coordinates, redistributes budgets, and assigns targets until the overall optimization is achieved. Then the HDTCTP is described and formulated based on analytical target cascading (ATC). To solve the presented HDTCTP, a top-down iterative algorithm is proposed according to the characteristics of HDTCTP in the frame of ATC. A real-life program case verifies the usability of the presented approach, and the results of experiments show that the presented algorithm based on ATC can efficiently solve the presented HDTCTP for programs. [ABSTRACT FROM AUTHOR]

This paper presents a new valve positioning algorithm (VPA) for water network pressure reduction. The proposed algorithm is a graph theory-based algorithm combined with EPANET2.2 to position pressure-reducing valves (PRVs) at the most effective locations, having the most effect on downstream pressure reduction, and then calculates the setpoint for locally controlled valves. Each algorithm solution positions one valve on the edge with the highest-pressure reduction indicator calculated using the depth-first search algorithm. The setpoint is then calculated according to downstream pressure differences to the minimum service pressure to be supplied. The PRV location and setpoint found by the algorithm may be used to install and set locally controlled PRVs or serve as a guide for positioning remote-controlled PRVs. The algorithm is demonstrated on a simple example network using demand-driven analysis (DDA), pressure-driven analysis (PDA), and genetic algorithms (GA) and on a medium and a large example application (DDA). Any number of PRVs may be installed using the algorithm, which returns stable results and very short solution times while maintaining minimum service pressure requirements to the critical consumers. [ABSTRACT FROM AUTHOR]

Durations are defined as an essential component of the three primary characteristics of strong earthquakes, which exhibit a remarkable influence on the damage characteristics of a concrete dam body. However, when considering the distinctive definitions of a strong motion duration, the influence of several durations on the seismic analysis of gravity dams remains unclear. In addition, there is no specific conclusion on the optimal choice for the duration of a strong ground motion. The endurance time analysis (ETA) method, which is a high-efficiency seismic performance method, is widely used, because of the high precision and low computation effort when calculating the dynamic responses of structures. However, when using this method it is almost impossible to account for any one type of duration contribution. Therefore, a modified ETA algorithm to correct duration effects will be proposed in this paper based on the three existing types of durations, such as bracketed (Tb), uniform (Tu), and significant (Ts) durations. The dynamic response will be further corrected using a modified ETA method in the numerical application to the Koyna concrete gravity dam, which is one of the largest dams in Maharashtra state, India. A group of 20 endurance time acceleration functions (ETAFs) and 10 sets of two-dimensional (2D) artificial waves with three total durations will be synthesized through the same site design acceleration spectrum. Three types of indices that include the dissipation energy, damage, and deformation indices will be used to measure the seismic performance. The nonlinear analysis results will be compared with those from the ETA and quantitative analysis methods, which show that the modified ETA method could provide an effective estimation to monitor earthquake resistance and structural seismic performance with a certain safety margin. The influence of the duration of a strong ground motion could be considered in the modified ETA method, which could be further applied in practical engineering. [ABSTRACT FROM AUTHOR]

Only a small minority of road incident detection algorithms (IDAs) have been designed for use in urban road networks. A review of the literature conducted by the authors revealed that approximately 10% of published papers on novel IDAs are designed specifically for urban networks. Urban networks present many challenges that are not faced on highways, such as signalized junctions causing queuing similar to that of an incident. This paper reviews the progress made in urban incident detection research and highlights where improvements are needed. It is found that few algorithms have been implemented or tested on real-world data, and so few have been sufficiently evaluated or compared. Those that have been implemented often find difficulty in differentiating disruption from incidents and contextual factors such as sporting events or public holidays. This has caused unnecessary false alerts to be triggered, leading to dissatisfied operators. Progress could be made if more research data and IDAs were published, allowing for thorough evaluations and comparisons of algorithm performance on realworld data. Further research is required to improve IDAs' ability to differentiate between incidents and contexts, particularly in complex urban networks. [ABSTRACT FROM AUTHOR]

Superblocks are city blocks whose size is significantly larger than average for a city block. They are considered to be close if they only have a few gates connecting the pedestrian and car traffic inside with that outside of the block. The gate setting of superblocks, namely the number and location of gates, despite playing a very important role in the overall traffic performance, has attracted limited attention in research. This paper narrows this gap by proposing a bilevel optimization model to calculate the optimal gate setting for superblocks. The lower-level model involves the traffic assignment, paying attention to travelers' mode and route choice behavior. The upper-level model computes the optimal number and location of gates to minimize the total cost, considering both travelers' cost and infrastructure cost. To solve this model efficiently, we also develop a solution algorithm, whose output is the optimized gate setting. A case study is used to illustrate the impact of the gate-setting problem on the performance of the transportation network, and the applicability of the proposed algorithm. Results indicate that the number and location of gates significantly impact traffic performance. For example, it is possible to have a lower cost with three gates that are well located than with nine gates that are poorly located. Furthermore, for the specific network studied, the optimal gate setting solution reduces the total cost by 17% and leads to a 3% mode shift from private car to metro when compared with the existing conditions. [ABSTRACT FROM AUTHOR]

The increasing number of defunct and fragmented spacecraft poses a growing hazard to existing onorbit assets. The redundant continuum manipulator with high flexibility provides dual-arm robotic systems with apparent advantages in active debris removal missions in space. Existing autonomously-coordinated control approaches for dual-arm continuum manipulators require a real-time inverse kinematic solution and a security assurance mechanism for possible collisions, which are difficult to upscale for space debris capture systems with high-speed maneuverability. In this paper, we consider collision avoidance and input saturation control in proposing a multiagent reinforcement learning approach, named the multiagent twin delayed deep deterministic policy gradient (MATD3), to generate a real-time inverse kinematic solution for coordinated manipulators. During the training process, the MATD3 algorithm performs lower overestimation than the multiagent deep deterministic policy gradient (MADDPG) algorithm. Then, a feedback dynamics controller is designed for the continuum manipulators. Under the guidance of the policy networks, each agent can schedule the joint trajectory design online according to the collaborator and target debris information. During the capture operation, a competitive mechanism for the anticollision function is developed through reasonable reward functions to maintain dual arms at a safe distance. Simulation results show that the average accuracy of the proposed approach is 42% higher than that of MADDPG in inverse kinematic trajectory planning. The designed integrated tracking controller can effectively perform capture missions in the simulation environment. Multiagent reinforcement learning shows promise for future onorbit servicing missions. [ABSTRACT FROM AUTHOR]

Many classes of engineering problems focus on the process of calibrating mathematical models using observed data. The enormous progress of scientific computation and data-mining techniques has allowed the search for accurate mathematical models from experimental data using algorithms. Among them, the evolutionary polynomial regression (EPR) is an artificial intelligence (AI) technique that merges genetic algorithms (GAs) and regression techniques such as ordinary least square (OLS). This paper presents a robust and well-conditioned EPR technique to remove potential outliers and leverage points included in any biased data set. This hybrid approach combines bisquare, Huber, and Cauchy robust multivariate techniques with GAs and the Akaike weight-based method to assess the optimal polynomial model while limiting the impact of the data bias. The robust techniques will define the parameters, the GAs will determine the exponents, and the Akaike weight-based method will evaluate the relative importance of each observed variable of the proposed model. As a case study, a shear strength data set of RC beams without stirrups is used to compare the standard EPR algorithm with the new proposed hybrid methodology. Furthermore, the optimal robust model is compared with different benchmark formulations to highlight its accuracy and consistency. The proposed hybrid technique can be adopted as a mathematical tool for many engineering problems, providing an unbiased prediction of the observed variable. Furthermore, the shear strength equation that provides the best compromise between accuracy and complexity allows its potential use in many engineering practices and building codes. [ABSTRACT FROM AUTHOR]

Discrete response of structures often is a key probabilistic quantity of interest. For example, one may need to identify the probability of a binary event, such as whether a structure has buckled. This study used an adaptive domain-based decomposition and classification method, combined with sparse grid sampling, to develop an efficient classification surrogate modeling algorithm for such discrete outputs. An assumption of monotonic behavior of the output with respect to all model parameters, based on the physics of the problem, helps to reduce the number of model evaluations and makes the algorithm more efficient. As an application problem, this paper developed a computational framework for generation of a probabilistic penetration response of S- 2 glass/SC -15 epoxy composite plates under ballistic impact. This enables the computationally feasible generation of the probabilistic velocity response (PVR) curve, or the V0−V100 curve, as a function of the impact velocity, and prediction of the ballistic limit velocity as a function of the model parameters. The PVR curve incorporates the variability of the model input parameters and describes the probability of penetration of the plate as a function of impact velocity. [ABSTRACT FROM AUTHOR]

RC shear walls are commonly used as lateral load-resisting elements in seismic regions, and the estimation of their shear strengths can become simultaneously design-critical and complex when they have so-called squat geometries, i.e., height-to-length ratios less than two. This paper presents a study on the training and interpretation of an advanced machine-learning model that strategically combines two algorithms for the said purpose. To train the model, a comprehensive shear strength database of 434 samples of squat RC walls is utilized. First, the eXtreme Gradient Boosting (XGBoost) algorithm is used to establish a predictive model for estimating the shear strength, wherein 70% and 30% of the data are respectively used for training and validation. This effort resulted in an approximately 97% validation accuracy, which well exceeds current mechanics-based/semiempirical models. Second, the SHapley Additive exPlanations (SHAP) algorithm is used to estimate the relative importance of the factors affecting XGBoost's shear strength estimates. This step thus enabled physical and quantitative interpretations of the input-output dependencies, which are nominally hidden in conventional machine-learning approaches. Through this setup, several squat wall attributes are identified as being critical in shear strength estimates. [ABSTRACT FROM AUTHOR]

Automated three-dimensional (3D) as-built modeling of built infrastructure with embedded rebar is an ongoing area of research. The current practice for such a process is either highly manual, due to the need for localizing rebar during the scanning process, or not compatible with popular and widely used building information modeling (BIM) software. In order to fill the existing gap, this paper proposes a highly integrated approach that utilizes ground-penetrating radar (GPR) to detect rebar from in-service buildings and automatically translate the extracted rebar from GPR data into existing BIM. Rebar is localized through GPR data using the shearlet transform and the inverse correlation between the maximum intensity and depth parameters. To map the localized rebar into corresponding building elements in BIM, a classification system is developed based on a deep-learning algorithm trained by several on-site data sets. To evaluate the performance of the presented system, two buildings and their various concrete components are selected as the testbed. The obtained results are promising and reveal that (1) the proposed GPR2BIM system is highly automated and accurately integrates localized rebar into existing BIM; (2) the shearlet transform precisely recognizes rebar through GPR data; (3) the lowest absolute error for the depth estimation algorithm is 5.12%; and (4) rebar from GPR is automatically classified and assigned to corresponding building elements with an accuracy of 100%. The findings demonstrate that the highly integrated system developed in this research is capable of automatically building a complete BIM with both surface elements and rebar for in-service building. [ABSTRACT FROM AUTHOR]

Ultrasonic wave–based imaging is a popular nondestructive tool used to inspect RC structures. The inherent heterogeneity of concrete poses a great challenge for imaging incipient damage to concrete. This paper presents an algorithm for improving the quality of images obtained from a considerably reduced data set. A set of RC samples was prepared. The reflections of ultrasonic waves were recorded by placing a transmitter and a receiver on the surface of the concrete. The signals were processed to create an image of a section of the specimens, and the images were enhanced through a set of three-step cascading filters. The spatial apodization (SA) filter is first applied to control the spatial directivity of the ultrasonic transducers. At the second level, a contrast improvement (CI) filter is applied to further reduce ghosting. The third filter is the moving average–based reflection localization filter. Images produced by the algorithm are compared with those produced via conventional techniques in terms of clarity and contrast. It is demonstrated that the cascading filters produce a clearer image with a considerably smaller data set. [ABSTRACT FROM AUTHOR]

In recent years, connected vehicle (CV) technology has reached a level of maturity and can be beneficial for traffic control at signalized intersections. Enriched information provided by CVs could be utilized to perform a more efficient signal control. This paper proposes a methodology to dynamically adjust the offsets to provide progression bands for multiple critical paths in a CV environment. The critical paths refer to the routes connecting origin-destination (OD) pairs with the highest volumes and are determined based on the CV trajectory. A real-time optimization model is constructed to design a coordination plan, and the control objective is to provide maximum green bandwidth along the determined critical paths by optimizing the offsets of all intersections along an arterial. To solve this model, a solution algorithm based on dynamic programming is proposed. A real-world arterial in Salt Lake City, Utah, is modeled in commercially available software to evaluate the effectiveness and efficiency of the proposed control strategy. Simulations are conducted to compare the proposed system with a fixed coordination strategy. The results reveal that the travel times of those critical paths by the proposed coordination strategy are lower the ones by the fixed coordination strategy. The average delay and average number of stops of critical paths are reduced by about 16.82% and 5.94%, respectively, compared with the fixed coordination strategy. Moreover, results also indicate that the proposed coordination strategy outperforms the fixed coordination strategy by reducing 7.84% and 5.79% of the average delay and average stops, respectively. [ABSTRACT FROM AUTHOR]

This paper presents machine learning valley-filling (MLVF) to enhance plug-in electric vehicle (PEV) charging at the local power level while minimizing the effects of uncontrolled PEV charging. This study investigated whether a neural network algorithm could learn to identify when to begin charging a PEV by distinguishing low and high demand sections in the forecasted baseload. The results indicate that a neural network algorithm can indeed identify low and high demand sections in the forecasted baseload and learn when to begin charging electric vehicles to decrease demand loads. MLVF achieved a microaveraged F1 score, an indicator of a classifier's overall accuracy, of 92.84% of selecting the correct timeslot to initiate charging. [ABSTRACT FROM AUTHOR]

In recent years, pressure management based on district metering areas (DMAs) has played an irreplaceable role in background leakage reduction in water distribution networks (WDNs). To further improve DMA management, this paper describes a novel sectorization method to optimize the reduction of background leakage. First, the network trunk mains are optimally selected using a proposed evaluation index which contains three reference indicators and the resulting isolated regions are identified through graph theory algorithm. Meanwhile, a modified community detection algorithm is adopted to partition the oversized regions considering both elevation uniformity within DMAs and demand uniformity across DMAs. Then the optimal layout of flow meters (i.e., inlet pipes) and gate valves is achieved by solving a two-objective optimization for minimizing the number of flow meters and cumulative pressure differences compared with critical points in each DMA. Finally, pressure-reducing valves (PRVs) are installed in every inlet pipe and optimized to reduce leakage. The overall procedure is applied to a medium-sized network. The results reveal that the proposed sectorization methodology combined with pressure management can efficiently reduce leakage by controlling excess hydraulic capacity. [ABSTRACT FROM AUTHOR]

Leaks in water pipelines can create many problems, such as financial loss, environmental pollution, and public health hazards. Previous scholars and engineers have researched leak detection and localization methods for water-filled pipe systems, which have proven to be effective in pipe fault detection, even if under external noise. However, the performance of these techniques is unavoidably affected by the noise from the pipe, which includes branch noise, and has motivated researchers to explore more high-performance technologies further. For this purpose, this paper mainly focuses on the problem of locating leaks in a branch water pipe system. Then, a novel blind deconvolution algorithm, called the complex-efficient FastICA (C-EFastICA) algorithm, is proposed to extract the original leak signal from the mixed leak acoustic signal. Unlike the other complex field blind deconvolution methods, the proposed C-EFastICA can adaptively select the nonlinear function g to establish the cost function and iterative learning rules according to the different generalized Gaussian characteristics, which makes the algorithm more accurate in decomposing mixed leak signals in a complex field. The experiment results show that the C-EFastICA is faster than the classical complex FastICA (C-FastICA) algorithm. The relative precision of the leak localization reached approximately 88% under the interference of branch noise. [ABSTRACT FROM AUTHOR]

This paper presents a 3D reconstruction method for fast elevation determination on construction sites. The proposed method is intended to automatically and accurately determine construction site elevations using drone-based, low–high orthoimage pairs. This method requires fewer images than other methods for covering a large target area of a construction site. An up–forward–down path was designed to capture approximately 2∶1 -scale images at different altitudes over target stations. A pixel grid matching and elevation determination algorithm was developed to automatically match images in dense pixel grid-style via self-adaptive patch feature descriptors, and simultaneously determine elevations based on a virtual elevation model. The 3D reconstruction results were an elevation map and an orthoimage at each station. Then, the large-scale results of the entire site were easily stitched from adjacent results with narrow overlaps. Moreover, results alignment was automatically performed via the U-net detected ground control point. Experiments validated that in 10–20 and 20–40 orthoimage pairs, 92% of 2,500- and 4,761-pixels were matched in the strongest and strong levels, which was better than sparse reconstructions via structure from motion; moreover, the elevation measurements were as accurate as photogrammetry using multiscale overlapping images. [ABSTRACT FROM AUTHOR]

This paper presents an in-depth case study of a heavy-haul railway line in Sweden to analyze the twist and longitudinal level geometry defects. A linear model was applied to model the evolution of the amplitude of the longitudinal level defects and twist over time. Despite the effect of the defect shapes on the dynamic track loads, the amplitude of the defects still is the only criterion used for the assessment of geometry defect severity. The application of first- and second-order derivatives to capture information about the shape of defects was investigated in the case study. In addition, the RUSBoost algorithm was used to classify track sections into healthy and unhealthy sections using the imbalance class data set. In this algorithm, the standard deviation and the kurtosis of the geometry parameters were used as explanatory variables. Finally, the abnormal track geometry degradation patterns identified in the case study were explored in detail. The results of the analysis can be used directly in maintenance modeling and used for the purpose of maintenance scheduling. [ABSTRACT FROM AUTHOR]

Structure-dependent integration algorithms have been implemented for pseudodynamic testing because they are very promising for this type of test due to the combination of unconditional stability and explicit formulation simultaneously. Although their numerical properties have been assessed and their feasibility for pseudodynamic tests has been corroborated, the choice of an appropriate structure-dependent pseudodynamic algorithm might be a difficult task. This is because some undiscovered, adverse properties were found for specific structure-dependent algorithms, although their basic properties have been well explored. Because the adverse properties are not well disclosed for these structure-dependent pseudodynamic algorithms, they might lead to inaccurate test results. This paper summarized both the favorable and adverse numerical properties of the pseudodynamic algorithms so that appropriate pseudodynamic algorithms can be identified. This paper assessed and compared most structure-dependent pseudodynamic algorithms, and recommended some structure-dependent pseudodynamic algorithms for engineering practice. Both recommended nondissipative and dissipative structure-dependent algorithms are very suitable for pseudodynamic tests because they possess favorable properties but exhibit no adverse properties. It was also substantiated that numerical damping cannot be used to overcome an overshoot caused by weak instability. [ABSTRACT FROM AUTHOR]

Direct integration algorithms are effective methods to solve the temporally discretized differential equations of motion for structural dynamics. Numerous researchers have worked out various algorithms to achieve desirable properties of explicit expression, unconditional stability, and controllable numerical dissipation. However, studies involving the numerical dispersion of integration algorithms are limited. In this paper, a precorrected bilinear transformation from a continuous domain to a discrete domain associating with pole-matching based on the control theory is utilized to develop a new family of explicit structure-dependent integration algorithms, referred to as TL- φ algorithms. In contrast to the existing algorithms, the significant improvement of the proposed method is that it can control the amount of numerical dispersion by an additional parameter related to the critical frequency of the structure. Stability, energy dissipation, and numerical dispersion properties of the proposed algorithms for both linear and nonlinear systems are fully studied. It is shown that the proposed family of algorithms is unconditionally stable for linear systems while only conditionally stable for nonlinear systems. Though the numerical dissipation property of the TL- φ algorithms is quite similar to that of other well-developed methods, its ability to minimize the period errors when compared with other methods makes it beneficial to the accuracy of the numerical simulation of dynamic responses. Four numerical examples are used to investigate the improved performance of the new method, and the results show that the proposed algorithms can be potentially used to solve linear and nonlinear structural dynamic problems with desirable numerical dispersion performance. [ABSTRACT FROM AUTHOR]

The design of an underground metro alignment in an urban area is complex and challenging. The metro alignment constantly invades districts with high-density buildings and specially planned districts, which results in large additional expenses due to reconstruction, land requisition, demolition, vibration absorption, and environmental protection. This paper presents a method for the optimization of the horizontal alignment between metro stations to minimize the overall cost. The proposed method includes three steps: (1) low-cost path search, (2) initial horizontal alignment generation, and (3) horizontal alignment optimization. First, an augmented rapidly exploring random-tree connect (RRT-connect) algorithm is developed to search for a low-cost path, in which the buffer zone of the low-cost path defines the corridor constraint of the horizontal alignment optimization. Subsequently, based on the low-cost path, an initial horizontal alignment is generated that determines the number of design variables, i.e., intersection points. Next, within the corridor constraint, the method optimizes the intersection point coordinates and curve radius on the initial horizontal alignment using a differential evolution (DE) algorithm. A real-world metro line was studied to verify the effectiveness of the proposed optimization approach. [ABSTRACT FROM AUTHOR]

Recent work has shown the applicability of Bayesian inference techniques, which use a physics-based representation of the structure of interest, to structural health monitoring (SHM) tasks, such as damage identification. This paper focuses on Bayesian filtering algorithms that provide a way to detect, localize, and identify damage in an online fashion. These algorithms aim to identify the states and parameters of the structure, and take into account noise in the system and measurements, and are thus well fitted to quantify uncertainties. In this paper, a thorough review of these algorithms is provided, primarily the particle filter and the unscented Kalman filter. Estimates of the posterior probability-density functions (PDFs) obtained with these filters are compared for three nonlinear mechanical systems, thus providing an insight into the filters' behavior and their ability to quantify uncertainties. Furthermore, novel techniques are introduced to take into account non-Gaussian noise and non-Gaussian posterior PDFs by means of a novel framework that expands the nonlinear Kalman filtering theory to non-Gaussian baseline distributions. [ABSTRACT FROM AUTHOR]

The nonlinear Muskingum model is a hydrologic flood-routing method useful when the storage flow relation departs from the classic linear assumption. This paper extends versions of the nonlinear Muskingum model by introducing a parameterized initial storage condition. The extended nonlinear Muskingum values have an increased number of degrees of freedom that allows an enhanced capacity to accurately predict outflow hydrographs provided that parameter estimation is optimized as proposed in this work. The parameters of the nonlinear Muskingum models are estimated with the weed optimization algorithm (WOA), and the excellent performance of the extended nonlinear Muskingum models is demonstrated with several types of hydrographs using several criteria of statistical efficiency. The implementation results show that the nonlinear Muskingum model's predictions outperform those of the best results reported with other routing models for the examples presented in this paper. [ABSTRACT FROM AUTHOR]

The paper proposes an interactive algorithm for a three-dimensional (3D) surface that can substantially improve its cracking detection performance by adjustments based on the observer's feedback. Since the correct response to the observer's feedback is critical to the performance of an interactive algorithm, the paper presents a series of novel methodologies to accomplish the interaction between the detection system and the observer with a high level of success. A computer program is developed to establish the interactive detection system and implement two levels of detection. The bottom level is automated and can be used to find the majority of cracking. The top level is assisted by the users and can be applied for semi-automated refinement. In particular, the top level is designated to find missed cracks or delete noises within user-defined regions. The techniques of precision recall and confusion matrix are used to determine effectiveness of the developed system of algorithms. The results from the case study on a highly cracked pavement segment indicate that the automated level of the proposed interactive system can achieve high detection accuracy with appropriate training, and the assisted level can retrieve nearly all cracks missed by the automated level and delete almost all noises from the processing using the automated level. [ABSTRACT FROM AUTHOR]

Reservoir systems are essential for water resources management. The application and development of optimization techniques for optimal reservoir operation is therefore a valuable undertaking. This paper presents a modified firefly algorithm (MFA) and applies it to optimally solve reservoir operation problems. Three well-known benchmark multireservoir operation problems are optimized for energy production. The results of the MFA are compared with results obtained with other mathematical programming approaches, such as linear programming (LP), differential dynamic programming (DDP), and discrete DDP (DDDP), the genetic algorithm (GA), the multicolony ant algorithm (MCAA), the honey-bee mating optimization (HBMO) algorithm, the water cycle algorithm (WCA), the bat algorithm (BA), and the biogeography-based optimization (BBO) algorithm. The MFA was found to be more effective than alternative optimization methods in solving the test problems demonstrating its strong potential to tackle multireservoir operation problems. This paper's results indicate that the MFA differed by 0.01 and 0.79% with the LP global optimal solutions of a continuous four-reservoir problem (CFP) and a continuous 10-reservoir problem (CTP), respectively. The objective function of a discrete four-reservoir problem (DFP) obtained with the MFA is equal to the LP's objective function. This paper demonstrates that the MFA is a competitive optimization method with which to solve a variety of reservoir operation problems. [ABSTRACT FROM AUTHOR]

Evaluating the statistical moments of performance functions from the perspective of balancing accuracy and efficiency remains a challenge. This paper proposes an efficient algorithm for evaluating the statistical moments of performance functions. The main procedure of the proposed method consists of three steps. First, based on the bivariate dimension-reduction method, the performance function is approximated by a summation of one-dimensional and two-dimensional functions. Next, according to the criterion of delineating the existence of cross terms, the two-dimensional functions are decomposed as functions including and excluding cross terms. Third, the one-dimensional point estimate method is used to evaluate the statistical moments of the one-dimensional functions and the two-dimensional functions without cross terms, whereas the two-dimensional sparse grid stochastic collocation method is applied to estimate the statistical moments of the two-dimensional functions with cross terms. Several numerical examples are presented to illustrate the efficiency, accuracy, and applicability of the proposed method. The results demonstrate that the proposed method achieves a good balance between accuracy and efficiency and provides a useful tool for evaluating the statistical moments of performance functions. [ABSTRACT FROM AUTHOR]

To enable a water distribution system (WDS) model to yield predictions with a reasonably good match to measurements, pipe resistance coefficients (PRCs) of the model often need to be calibrated. The majority of methods developed recently solve the PRCs' calibration problem using evolutionary algorithms. The common feature of these methods is to perform the forward computation (i.e., solve the traditional hydraulic equation of networks) repeatedly to search optimal solutions. This paper presents a direct inversion algorithm for PRC calibration of WDSs, which has an identical framework with a global gradient algorithm (GGA) that has been proved to be the most effective method to solve networks and adopted by EPANET. Therefore, all advantages of GGA can be extended in terms of high computational efficiency and convergence, as well as being easy to understand and program. In addition, under the proposed framework, uncertainties propagated from measurement noises, nodal demand uncertainties, and model simplification errors to the estimates can be quantified individually or simultaneously in a flexible manner, enabling the identification of uncertainties source, as well as which kind of effort is preferred to reduce them. Three networks are applied to validate the proposed algorithm. Encouraging results achieved demonstrate that the proposed algorithm is valid for the calibration and uncertainty qualification of PRCs, as well as the uncertainty source identification. [ABSTRACT FROM AUTHOR]

In this paper, an integral feedback and feed-forward (IFF) optimal controller with hard terminal constraints for missile formation reconfiguration is designed. The controller has quadric optimal performance for expected terminal errors, output, and control quantity. From the viewpoint of the kinematics relationship of a formation in the relative coordinate frame, the authors establish a precisely linearized relative motion model by transforming the control variables. This relative motion model can intuitively manifest the relationship of the relative motion in three directions in the relative coordinate frame. In order to solve the designed IFF optimal controller, detailed deductions for deriving the related Lagrange parameters are presented. A precise integration algorithm was adopted instead of using a traditional backward integration algorithm to calculate more precise solutions for the relevant parameters in the IFF optimal controller. A collision avoidance system with four spherical domains was proposed, and a modifying principle to avoid collision during formation reconfiguration was presented. Simulation results demonstrate that the presented IFF optimal controller is capable of implementing missile formation reconfiguration rapidly, stably, and accurately. It can additionally restrain invariant or slowly varying perturbations induced by the velocity of a leader missile. Furthermore, the collision avoidance system developed in this paper can enable missiles to avoid collision during formation reconfiguration. [ABSTRACT FROM AUTHOR]

In this paper, the problem of longitudinal free vibration of nonuniform rods is investigated for the first time by using the discrete singular convolution (DSC). This method is a relatively novel and powerful numerical approach especially in analyzing vibration problems. The governing equation of this problem is nonlinear and has analytical solutions only in a few cases, so the analysis uses DSC as a numerical method. The regularized Shannon delta kernel is used in the present algorithm. The nondimensional natural frequencies of rods with various-area cross sections have been studied in this paper. Results show good agreement with the previous analytical solutions. This study indicates the efficiency of the DSC in solving nonuniform rod problems. [ABSTRACT FROM AUTHOR]

In recent years, there has been a surge in the use of building information modeling (BIM) within construction. Much of the previous published research focuses on the impact of BIM during the detailed design, construction, and postconstruction stages. This paper combines the search and exploration powers of evolutionary computing with BIM during the conceptual stage of design. The main contribution of this paper is a methodology to integrate solutions generated by the interactive and visual clustering genetic algorithm (IVCGA) into a BIM environment to enhance the design information and allow the solutions to be viewed in greater detail as building information models. These models can then be utilized or modified by individual members of the design team to aid in the selection of concept designs that appropriately meet client/design requirements. An Autodesk Revit plug-in application has been developed that enables the IVCGA solution to be transformed into a building information model in a common repository. Using this approach during the conceptual design stage can potentially enhance multidisciplinary decision-making capacity by allowing a wide range of concept designs to be assessed in high detail with more accurate costing prior to committing a large sum of funding to the project [ABSTRACT FROM AUTHOR]

Accurate and proactive inspection of constructed objects and identification of their defects and design conformity is vital to both facility management and construction projects. A project's building information model (BIM) needs to be regularly updated to more accurately reflect the actual site conditions. However, conventional practices lack site-data integration with models and rely on manual, errorprone, and costly updates. This paper proposes and demonstrates a robust technique that uses the industry foundation classes (IFC) schema to automatically update an as-designed BIM based on site observations for inspected building elements. It receives as input an inspected object's actual type and inspection details including the detected defects/changes, responsible actors, as-built/as-is type, captured images, and time and the date of the inspection. The algorithm automatically analyzes the IFC data model to retrieve the element's semantics and identifies discrepancies between the as-built/as-is and as-designed object conditions. Based on the results of the discrepancy analysis, the IFC data model is populated with new semantics, resulting in the update of object types, properties, and three-dimensional (3D) shape representations. Inspection details and user entries are automatically documented in the BIM and assigned to objects to enable potential diagnostics and tractability. This automates site-to-BIM data transfer and supports reality-capture techniques. [ABSTRACT FROM AUTHOR]

The progressive optimality algorithm (POA) is commonly used to identify optimal hydropower operation schedules in China. However, POA may not converge within a reasonable time for large and complex problems because its computational burden grows exponentially with the expansion of system scale. In order to effectively alleviate the dimensionality problem of POA, an improved POAvariant called orthogonal progressive optimality algorithm (OPOA) is introduced in this paper. In the OPOA, an orthogonal experimental design is used to replace the exhaustive combinatorial evaluation at each POA two-stage subproblem. The theoretical analysis shows that POA and OPOA have exponential and approximately polynomial growth in computational complexity, respectively. The proposed method is applied to a large-scale multireservoir system located on the Wu River in China. The results indicate that, compared with POA, OPOA can remarkably enhance the computing efficiency in different cases, showing its practicability and feasibility for multireservoir system operation. [ABSTRACT FROM AUTHOR]

In this paper a new guided structural damage identification approach is presented in order to localize and quantify multiple damage cases in different structural systems. The change in the amount of modal strain energy of a structural element due to damage is utilized as a means to guide the structural identification process, which is formulated as an inverse optimization problem. The objective function of the optimization problem, which is defined using the generalized flexibility matrix (GFM) of the structure, is then optimized using the newly developed tug-of-war optimization (TWO) algorithm. Three numerical examples are studied in order to demonstrate the applicability of the proposed approach. The results indicate that the presented method can properly locate and quantify induced structural damage using only the first few modes of the structure. The method is particularly useful when the extent of damage in the structural elements differs greatly. [ABSTRACT FROM AUTHOR]

The nature of an indoor construction site is dynamic and complex. This characteristic becomes even more evident when a multitude of interactions among workers, equipment, and materials exists. To assist various operations of construction, which are dynamic and complex in nature, previous research explored sensing technologies to discover advanced approaches to supporting construction operations. In particular, many researchers have focused on investigating the tracking of construction resources over the past several years. Despite the previous research efforts in radio-signal-strength-indication (RSSI)-based tracking systems, signal interference such as signal degradations, occlusions, obstructions, and multipath effects created by a complex and dynamic construction environment has not been clearly discussed. Furthermore, fundamental understanding of issues associated with signal interference is limited. This research aims to reduce the gap in knowledge of the RSSI-based tracking system when it is used in a highly noisy environment like a dynamic, complex, and large indoor construction site. This paper presents the development of a new probabilistic local search (PLS) algorithm using Bluetooth low energy network technology. The PLS is assessed with respect to accuracy, reliability, and robustness. Two conventional positioning methods are subjected to the same tests as a benchmark for comparison. The results indicate that PLS outperforms the other conventional methods when the signal interference is high and performs comparably when the signal interference is minimal. [ABSTRACT FROM AUTHOR]