Search

Transportation infrastructures and operations can be severely impacted during flood events, leading to significant disruptions to the flow of goods and services. Although numerous studies have evaluated the direct impacts of flood events on the performance of transportation infrastructures, the indirect impacts or cascading effects have been rarely assessed. Hence, this paper examines the cascading effects of floods on transportation infrastructure using data mining algorithms. First, 33 effects of flood events on transportation infrastructure have been identified based on data collected for multiple flood events in New York and New Jersey. Second, association rule mining analysis was implemented to identify the key co-occurrences between flooding and the different events. Third, network analysis was conducted to quantify the co-occurrences or key combinations among the events. Fourth, cluster analysis was used to group or prioritize the cascading effects and co-occurring events into highly connected clusters to identify the most critical ones based on two scenarios: (1) without consideration of co-occurrences (Scenario 1); and (2) with consideration of co-occurrences (Scenario 2). The findings provided insights that while some cascading impacts could be individually critical/frequent (under Scenario 1), other cascading impacts could also result due to a combination of different effects that might not be perceived to be critical on the individual level but rather become critical when combined with other cascading events (under Scenario 2). The outcomes of this paper demonstrate the importance of considering the co-occurrences between the events and cascading effects, rather than analyzing them in isolation. This study adds to the body of knowledge by offering an analytical approach that could be used to identify and prioritize critical cascading effects of flood events on the operations and performance of transportation infrastructures. [ABSTRACT FROM AUTHOR]

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]

Over the decades, significant efforts have been made in evaluating pavement condition by mounting cameras on a vehicle and automatically analyzing images by incorporating digital image processing techniques. Despite recent advances, there are still some limitations associated with current automated crack collection and analysis systems such as potential risk to vehicular safety, limited coverage by cameras, traffic blocking camera views, and errors in analyzing images for crack extent and severity. Inspired by current developments of deep learning technology such as object classification and sematic segmentation along with unmanned aerial vehicle (UAV) technology, this paper presents a set of comprehensive automated crack analysis algorithms based on a combination of deep learning and UAV images. To extract pavements from UAV images and segment cracks from extracted pavement images, the contracting encoder path of the U-Net model was modified with various deep learning models such as Pre-trained VGG 16, ResNet 50, Inception V3, and DenseNet 169 models as a backbone. Based on the least false negative and false positive outputs, the Inception V3 model with dice loss using a nonaugmented dataset and the Inception V3 model with focal loss using a nonaugmented dataset model showed the best performance for pavement extraction and crack segmentation, respectively. A tile-based pavement crack analysis system was then developed to measure percent cracking and crack widths from segmented crack images. It can be concluded that the developed pavement extraction and crack analysis system using UAV images will help public agencies evaluate pavement conditions in a systematic and cost-effective manner. [ABSTRACT FROM AUTHOR]

The high pressure of water distribution networks (WDNs) increases leakage, bursts, and water consumption. Pressure-reducing valves (PRVs) are widely used in WDNs because of their flexibility and effectiveness in pressure management. This paper studies pressure management by locating and adjusting PRVs in WDNs, considering the proper objective function and the suitable optimization algorithm. The single-objective algorithms and the EPANET hydraulic simulator in MATLAB programming are combined to manage the pressure. Initially, the proposed method improved by selecting the optimal optimization algorithm and objective function. Both fixed outlets and time-modulated modes of PRVs for two ZJ and Bellingham WDNs as case studies were used. Considering the cost, reliability, hydraulic performance, and leakage, the results were then analyzed and compared by changing the number, location, and adjustment of each PRV. The outcomes show the effect of choosing the best algorithms and cost functions on the results. The results also show that this method increases the reliability and controls the nodal pressure to a desired level in WDNs. Practical Applications: Leakages in water distribution networks (WDNs) are problematic for utilities and other governmental agencies. Pressure management in WDNs is inevitable to reduce the rate of bursts and leaks. Using fixed- and time-modulated pressure-reducing valves (PRVs) is essential for smart pressure management in WDNs. This study presented a method for positioning and adjusting PRVs in WDNs. The proposed method was applied to ZJ and Bellingham WDNs by choosing the best optimization algorithm and objective functions. The grey wolf optimizer (GWO) algorithm is proposed as one of the best algorithms for this subject. The presented method tries to provide the best plan for pressure management by examining the cost, leakage, and reliability. The results show that the presented method effectively increases reliability and reduces leakage in WDNs. [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]

Structural health monitoring (SHM) provides strong support for bridge safety evaluation by tracking the real-time structural performance. An accurate and efficient imputation method for missing data in the SHM system is of vital importance for bridge management. In this paper, an innovative vertical–horizontal combined (VHC) algorithm is proposed to estimate the missing SHM data by a more comprehensive consideration of different types of information reflected in different time dimensions of the monitoring data. In the vertical time dimension, the variation trends and changing ranges for the missing data are analyzed by the relational analysis from the global view of data. In the horizontal time dimension, the exact value of the missing data is estimated based on the adjacent data inside the missing data set from the local view of data. The proposed VHC algorithm is validated and compared with the current methods in the data imputation for the missing deflection values in the SHM system of an example tie-arch bridge. The results show that the proposed algorithm demonstrates the best imputation performance among all methods for both missing types with the highest imputation accuracy/similarity and satisfying the 95% confidence bound. The application of reward coefficient significantly increases the imputation accuracy for the continuous missing situation and the improvement grows with the missing step number. Loss ratio and sample size also have significant influences on the imputation performance with the proposed method. Data missing in the structural health monitoring (SHM) system affects the continuity of monitoring data and the structural conditions reflected by them. This paper proposed an innovative data imputation algorithm combining information from both vertical and horizontal time dimensions of the monitored data in the bridge SHM system. The proposed vertical–horizontal combined (VHC) algorithm shows better imputation performance when compared with the current methods widely used in engineering practice and shows a higher applicability in a small sample size situation. Thus the proposed algorithm provides an accurate and efficient tool for dealing with the data missing phenomenon in the SHM system and is of great importance to the safety evaluation and further management decision process for bridge structure. Moreover, the methodologies and approaches proposed in this paper are quite general and can be applied to the missing data imputation problems in many research fields with time series data. [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]

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 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]

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]

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]

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]

Due to its complex geometry and mechanical behavior, the prestress design of the cable–pylon anchorage zone is difficult to achieve but critical. In this paper, the use of the strut-and-tie model (STM) is evaluated to simplify the cable–pylon anchorage zone design. The traditional Evolutionary Structural Optimization (ESO) method is first used to generate the STM of a common one-box two-cell cable–pylon anchorage zone. However, the resulting STM is shown to be irrational by comparison with the real distribution of principal stress in the cable–pylon anchorage zone. Therefore, this paper proposes a tailored ESO method using a modified algorithm for the removal criterion based on the strain energy of the element. The proposed method is shown to overcome the drawback of the classical ESO method that some elements playing key roles in the distribution of principal stress, but having small von Mises stresses, may be deleted mistakenly. The prestress design of the Cao'e River Bridge is then presented using the correct STM generated by the tailored ESO method as a practical example. Finally, the rationality of the resulting prestress design of the cable–pylon anchorage zone is substantiated using the results of a full-scale model test. [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]

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 proposes an equivalent plane algorithm of spatial stay cables using spatial rigid arms and the stay cable corotational coordinate system. The equivalent equations for the cable displacements and force elements are established, and the equivalent plane-element stiffness matrix of the spatial-cable element and its transfer matrix are derived. The matrix is automatically transformed into the plane-cable element model with rigid arm when the cable plane of the cable-stayed bridge is parallel. The spatial characteristic parameters of the cable plane are proposed, and the spatial effect of the spatial-cable plane on the internal force and deformation of the cable-stayed bridge is also analyzed. This equivalent plane algorithm is programmed into the bridge design and construction monitoring system procedure, and a comparison analysis with MIDAS software is also conducted. The proposed method can simplify the calculation model for the entire construction process of cable-stayed bridges with spatial cables and yield a good calculation accuracy. The equivalent plane algorithm has been successfully applied to the Jiayu Yangtze River Bridge (a kilometer-scale cable-stayed bridge). The results show that the number of stay-cable elements in the calculation model is reduced by 50%. The difference between the proposed method and the calculation result of a spatial-cable plane model is very small for a cable-stayed bridge with a spatial-cable plane, whether the initial tension is used or an unstressed cable is used. The proposed method provides an efficient and reliable calculation method for the design and construction of spatial-cable-stayed bridges. [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]

Underfunding and the continuous deterioration of pavement assets have been among the major challenges for transportation agencies in the US. These challenges motivate the use of different optimization techniques such as integer programming to perform maintenance and rehabilitation planning at the network level. However, one of integer programming's limitations is the massive computational power requirement associated with the large number of solutions searched by the algorithm to get the optimal solution, limiting the size of the network it can optimize. In this paper, the full mathematical derivation of the binary integer programming problem is shown in terms of budget constraints only and in terms of both budget and percentage of poor assets (PPA) constraints. An alternative approach including the PPA constraint is therefore suggested along with a new practical way of reducing the search space based on the statistical bootstrap approach. The proposed approach's adequacy is validated through a comparison of different optimization approaches applied to a small asphalt concrete (AC) pavement network. The suggested approach shows promising results because it significantly reduced the time required to allocate budget funds over the test network of 400 asphalt concrete (AC) segments. [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]

A standard interchangeable data format was recently developed through the Transportation Pooled-Fund Study TPF-5(299) to meet different requirements and needs for two- and three-dimensional (2D/3D) pavement image data storage and exchange. Considering pavement images occupy large amounts of storage space, efficient compression algorithms for both 8-bit 2D and higher-bit 3D images become one of the key components for a successful implementation of the standard. Highway agencies and industry vendors exclusively use the Joint Photographic Experts Group (JPEG) or its successor JPEG 2000 standard for 2D image compression, while the compression of 3D 16-bit images remains to be proprietary. The goal of this paper is to evaluate suitable algorithms for compressing 3D pavement images. An overview of the state-of-the-art pavement image data collection technologies was performed, and the relevant image data formats were identified. Subsequently, five compression algorithms suitable for 16-bit image data were selected based on results from a comprehensive survey of the TPF-5(299) technical community. The compressions of several representative field images were implemented and evaluated in terms of the computation efficiency and retaining of image quality. The expected benefits of this study include facilitating standard and efficient pavement image data collection and accelerating the development of consistent pavement condition evaluation. [ABSTRACT FROM AUTHOR]

The purpose of this paper is to present an innovative equation to predict the moment capacity of spirally reinforced concrete columns with high accuracy using a combination of neural network and metaheuristic optimization algorithms. To this end, a large experimental database has been gathered to train a neural network with seven independent parameters that deal with the dimensional properties of the columns, reinforcements, materials, and also the forces. Furthermore, the authors improved the process of training with consideration of two optimization techniques: particle swarm optimization (PSO) and Harris hawks optimization (HHO). Then, the best model was selected to a statistical methodology to extract an empirical equation to predict the target, which makes the proposed system of this article more applicable, especially for the practical usages. The results indicated that the neural network with the PSO algorithm had better results than the other model. Also, it has been found that the proposed formulation could predict the moment capacity of the considered element with high performance. The presented equation of this article has many applications in civil engineering, such as retrofitting and rehabilitation. [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]

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]

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]

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]

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]

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]

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]

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]

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]

To accelerate the convergence rate of high-dimensional optimization problems, inspired by the cooperative hunting process of spider colonies named Anelosimus eximius, a new A. eximius colony algorithm (AECA) was proposed to solve combinatorial optimization problems. In the AECA, a certain direction component of the problem solution is represented as a certain direction in which a spider travels, so that a high-dimensional optimization problem can be transformed into multiple low-dimensional optimization problems. The AECA includes two intelligent behaviors: the random walk of spiders and the summoning of the initiator. The random walk of spiders ensures the diversity of spider colonies, whereas the summoning of the initiator can accelerate the convergence rate. We theoretically proved that the AECA is globally convergent. The inversion method of asteroid spectrum reflectance template can be used to solve the problem that the measured planetary spectrum is affected by the asteroid absorption effect and improves the accuracy of celestial Doppler difference velocimetry, which uses celestial spectrum to provide the information of velocity measurement for navigation. The essence of this method is the optimal combination problem of intrinsic mode functions (IMFs). We applied the AECA to the inversion of the planetary spectrum reflectance template. Experimental results show that, compared with genetic algorithms (GAs), the AECA can obtain the optimal combination of spectrum reflectance templates faster. In addition, to verify the universality of the AECA, for the classical knapsack problem, the AECA has a better optimization effect, faster convergence rate, and higher stability than other swarm intelligence algorithms such as GA, discrete particle swarm optimization, and the quantum genetic algorithm. [ABSTRACT FROM AUTHOR]

The aim of this work is to investigate dynamic division of highway control subareas based on improved Label Propagation Algorithm (LPA) for implementing different signal control strategies in the subareas to improve operation efficiency. A highway network is used as the studied object, where each road segment is defined as a node and the network is divided into three types of nodes: only mainline segment, an on-off-ramp and its connecting mainline, and an overpass. The system is abstracted as a topological set of nodes and their relationships. The concept of multiroad segment correlation degree with predictive function of congestion and dissipation ranges is proposed. The correlation degree model of multiroad segments based on Pearson Correlation Coefficient (PCC) and the importance degree model of road segment based on technique for order preference by similarity to ideal solution (TOPSIS) are established, respectively. Then a dynamic division method of control subareas for highway network is proposed based on improved LPA, where the label updating model based on the importance degree of road segment is established by semisupervised classification algorithm. Multiple scenarios were discussed using the highway network of Baoding City in Hebei Province of China as an example. The results show that the number of subareas is moderate, the internal state is uniform, and the calculation is simple and efficient. The control effect is remarkable especially when the vehicle distribution is uneven. Total delay time is reduced by 10.47%, 4.38%, 4.24%, 9.73%, and 2.33%, respectively, and total travel time is reduced by 12.20%, 7.15%, 7.02%, 11.85%, and 3.78%, respectively, compared with no control method, global control method, the static division method by clustering algorithm, the method by normal LPA algorithm, and the method by Newman algorithm. [ABSTRACT FROM AUTHOR]

Construction firms face considerable challenges in relation to finding cost-effective formwork solutions to meet increased construction demands. Project stakeholders have relied on self-consolidating concrete (SCC) to speed up the construction time because SCC is highly fluid and has numerous advantages compared to traditional concrete. To withstand SCC's high fluidity, formwork systems should be robust. Although previous research has experimentally examined various characteristics of SCC, few research studies have used machine-learning algorithms to estimate or predict the lateral pressure exerted by SCC on formwork systems. Hence, this study addressed this knowledge gap by proposing a machine-learning approach to predict the lateral pressure of SCC on vertical formwork systems. First, laboratory tests were performed to collect data on lateral pressure measurements, material factors, placement conditions, and formwork characteristics affecting the SCC lateral pressure on formwork systems. Second, four supervised machine-learning algorithms were considered in this study: k-nearest neighbor (KNN), artificial neural network (ANN), decision tree (DT), and random forest (RF). Third, the hyperparameters of the machine-learning algorithms were tuned, and their performance metrics were compared. Fourth, the most accurate predictive machine-learning model was verified on an unseen testing set. The results showed that the RF machine-learning algorithm was the best model for predicting the lateral pressure of SCC on formwork systems, with a mean percentage error of 0.8%, a mean absolute percentage error of 4.29%, and a coefficient of determination R2 of 0.9548. This study adds to the construction engineering and management body of knowledge by developing a machine-learning predictive model that can be used to accurately assess the lateral pressure exerted by SCC on formwork, which helps to ensure safe design of formwork systems and economic construction operations in formwork-related activities. [ABSTRACT FROM AUTHOR]

Expansion of the rail transit network has increased public transportation accessibility, and the spatial and temporal distribution characteristics of ridership have become more complex. Herein, the characteristics of rail transit origin-destination (OD) ridership distribution were analyzed using a drift power law distribution model, and the robustness of ridership distribution in three periods (weekdays, weekends, and holidays) was investigated. A Louvain algorithm-based community detection method divided the rail transit network into different community structures. This division was generally consistent with the established rail transit line layout. However, central city communities were more dispersed, and the rail transit stations of different lines may have belonged to the same community, revealing the spatial correlation between different stations from the perspective of OD ridership. The outcomes of this division were stable for the different periods. This study contributes to the precise configuration of transportation facilities around rail transit stations and the formulation of rail transit operation management strategies. [ABSTRACT FROM AUTHOR]

Geotechnical site characterization using multivariate, limited (sparse), and missing (incomplete) data is an important but challenging task, particularly in high dimensions. Toward this problem, this study proposes a Bayesian vine algorithm. In the proposed algorithm, the task of Bayesian update in higher dimensions is translated into a series of lower-dimensional (usually ≤2) update tasks using conditional correlation vine. This feature of the proposed algorithm makes it scalable and computationally efficient in higher dimensions. Multiple examples using two-dimensional (2D), five-dimensional (5D), 10-dimensional (10D), 20-dimensional (20D), 50-dimensional (50D), and 100-dimensional (100D) data are shown to demonstrate the capability of the proposed algorithm. The results suggest that the proposed algorithm can be used successfully for geotechnical site characterization. Even an ultrahigh 50D joint distribution with >1,000 parameters (1,325) can be estimated in around 20 min. The proposed algorithm can handle multivariate data sets with limited and missing values and can also handle non-Gaussian multivariate joint distributions. The proposed algorithm only considers cross-correlation in the site data and doesn't take into account spatial correlation. [ABSTRACT FROM AUTHOR]

The visibility of pavement markings is one of the most critical factors for traffic safety, and a periodical assessment plan is crucial for maintaining this function. Traditional assessment methods, such as visual windshield surveys or manual testing using handheld devices, are unsafe, time-consuming, and labor-intensive. In recent years, transportation agencies have begun to adopt the use of mobile retroreflectivity units (MRUs) for condition assessment of pavement markings. MRUs, different from other manual methods, can be utilized to collect large-scale retroreflectivity data in an efficient manner. However, no relevant research has yet proposed a mathematical optimization model for arranging the evaluation schedule and paths of MRUs. This study aims to propose a MRU routing model, and an efficient solution methodology. A branch-and-price algorithm, including column generation and branch-and-bound, was implemented. Computational experiments have been conducted based on actual tasks from the Florida MRU program for validation. Results show that the proposed solution methodology with a set partitioning model in this study not only finds the optimal solution for problems with tasks less than 60, but also effectively narrows the solution gap to be within 1.0% for problems with tasks less than 131. [ABSTRACT FROM AUTHOR]

Therefore, a proper explanation of DE implementation is required with optimal hyperparameter values of population size ( HT ht ), scaling factor ( HT ht ), and crossover probability ( HT ht ). The authors need to explain HT ht , HT ht , HT ht , and so forth. [Extracted from the article]

Wind-induced vibration has become increasingly prominent for long-span bridges. Flutter instability, as a type of divergent vibration, is a key component in the wind resistance investigation of long-span bridges. Based on the principle of instantaneous power balance (IPB) of the flutter critical state of bridge girders, an algorithm for predicting the flutter critical wind speed of long-span bridges was proposed by utilizing a nonlinear optimization strategy. For the bending–torsional coupling two-dimensional (2-D) motion system, the contribution of the wind-induced self-excited and structural elastic forces of the 2-D bridge section to the energy of the system was revealed to be dependent on some key parameters, such as reduced frequency, wind speed, amplitude ratio, and phase lag between vertical and torsional motions. Therefore, according to the principle of IPB during the critical flutter state, the prediction of flutter onset wind velocity can be transformed into an extreme value optimization problem of the IPB objective function. The feasibility and accuracy of the IPB algorithm were verified by comparing them with those obtained from segmental model wind tunnel tests and previous 2-D flutter prediction algorithms. Compared with the traditional methods characterized by force balance, the proposed method clearly and quantitatively presents the contribution relationship among multiple self-excited aerodynamic components on the flat plate and bridge section while flutter occurs; the method first evaluates the flutter critical state as an alternative algorithm from an energy perspective. [ABSTRACT FROM AUTHOR]

The construction disturbance mechanism of rectangular pipe-jacking tunnels is more intricate than that of circular tunnels, leading to potential issues such as excessive accumulation and deformation of the surrounding formation, which can result in engineering disasters. However, there is currently a lack of reliable methods for predicting these disturbances. Machine-learning techniques have the capability to analyze the influence of multiple independent variables on a dependent variable, offering a new approach for predicting surface settlement in the construction of rectangular pipe-jacking tunnels. To address the sensitivity of existing machine-learning models to initial parameters, an improved particle swarm optimization (IPSO) method is employed. This method incorporates an adaptive mutation technique, adaptive inertia weight, and postoptimization method for mutant particles to enhance the particle size and determine the probability of obtaining the optimal value. By leveraging the strong mapping and nonlinear fitting abilities of the backpropagation (BP) algorithm, the IPSO-BP algorithm model is developed and compared with the BP, support vector machine, and random forest (RF) models using actual monitoring data. The findings indicate that in the presence of specific noise in the surface settlement data, the IPSO-BP prediction model demonstrates an enhanced accuracy of 26%, 25%, and 10% for the left amplitude. This approach can serve as a valuable reference for settlement prediction in similar projects. [ABSTRACT FROM AUTHOR]