Zhou A, Peeta S, Wang J. Cooperative control of a platoon of connected autonomous vehicles and unconnected human-driven vehicles. Computer-Aided Civil and Infrastructure Engineering. 2023;38(18): 2513–2536. In the “Funding Information” section, the text “National Key Research and Development Program of China, Grant/Award Number: 2018YFE0102700.” was incorrect. This should have read: “National Key Research

This paper introduces a methodology designed to reduce cost, improve demand coverage, and ensure equitable vaccine distribution during the initial stages of the vaccination campaign when demand significantly exceeds supply. We formulate an enhanced maximum covering problem as a mixed integer linear program, aiming to minimize the total vaccine distribution cost while maximizing the allocation of vaccines

Retrofitting building designs is crucial given the global aging infrastructure and increased in frequency of natural hazards like earthquakes. While traditional data‐driven models are widely used for predicting building conditions, there has been limited exploration of recent artificial intelligence (AI) techniques in structural design. This study introduces a novel explainable AI framework that utilizes

Virtual reality (VR) technology is increasingly vital in various sectors, particularly for simulating real environments in training and teleoperation. However, it has primarily focused on static, controlled settings like indoor industrial shopfloors. This paper proposes a novel method for remotely controlling robots in hazardous environments safely, without compromising efficiency. Operators can execute

Prestressing wire breakage induced by corrosion is hazardous, especially for concrete structures subjected to severe aging factors, such as bridges. Developing an automated monitoring system for such a damage event is therefore essential for ensuring structural integrity and preventing catastrophic failures. In line with this target, a supervised deep learning–based approach is proposed to detect and

This study proposes a novel self-training framework for unsupervised domain adaptation in the segmentation of concrete wall cracks using accumulated crack data. The proposed method incorporates Bayesian neural networks for uncertainty estimation of pseudo-labels, and spatial priors of cracks for screening noisy labels. Experiments demonstrate that the proposed approach achieves significant improvements

Street view imagery has emerged as a valuable resource for urban analytics research. Recent studies have explored its potential for estimating lowest floor elevation (LFE), offering a scalable alternative to traditional on‐site measurements, crucial for assessing properties' flood risk and damage extent. While existing methods rely on object detection, the introduction of image segmentation has expanded

Zhang, T. T., Jin, P. J., Piccoli, B., & Sartipi, M. (2024). Deep spatial-temporal embedding for vehicle trajectory validation and refinement. Computer-Aided Civil and Infrastructure Engineering, 39, 1597−1615. https://doi.org/10.1111/mice.13160 In the “Methodology” section, Equation (2) “S⁡(ei,ej)=12(1+eiT∗ejei⁢2⁢ej⁢2)$S\ ( {{{e}_i},{{e}_j}} ) = \frac{1}{2}\ \left( {1 + \frac{{e_i^T{\mathrm{*}}{{

Dynamic cone resistance (DCR) is a recently introduced soil resistance index that has the unit of stress. It is determined from the dynamic response at the tip of an instrumented dynamic cone penetrometer. However, DCR evaluation is generally a manual, time-consuming, and error-prone process. Thus, this study investigates the feasibility of determining DCR using a stacked ensemble (SE) machine learning

The seismic resilience of cities plays a crucial role in achieving the United Nations Sustainability Development Goal. However, despite the occurrence of elevator passenger entrapment in numerous earthquakes, there is a notable lack of studies addressing this sophisticated issue. This study aims to bridge this gap by proposing a novel urban risk assessment model designed to evaluate city-scale earthquake-induced

This study proposes a methodology for deriving the optimal work zone schedule for the annual routine maintenance planning in an infrastructure asset management system considering the (i) economies of scale in work zone costs due to work zone synchronization and (ii) user costs across the road network with traffic assignments. A key aspect of the proposed methodology is the ability to derive in detail

Efficient traffic safety management necessitates real-time crash risk prediction using expressway characteristics. With the emergence of autonomous vehicles (AVs), the development and evaluation of variable speed limit (VSL) strategies, a key active traffic management technique, become crucial for enhancing safety and mobility in mixed traffic flows. This underscores the need for optimized VSL strategies

Traffic accidents occur frequently in cold and snow- or ice-covered regions due to weather changes that occur during the winter season. To detect the snow- or ice-covered roads in road surface conditions, road surface images captured using fixed-point cameras installed along the route are sufficient. This paper proposes a snow- or ice-covered road detection method that uses the deep convolutional autoencoding

The privileged permit service can be provided as an alternative to the conventional meter and reserved services in the off-street parking lots. In view of the unbalanced demand and the simplistic off-street parking lot management, this paper proposes a novel parking management problem for setting up and withdrawing the temporary permit-only policy. To optimize the access rule regarding uncertainty

This study evaluates a capacitated facility location model enhanced with distance constraints for an emergency response problem, ensuring certain neighborhoods remain within an accessible range from facilities following a hurricane. The proposed model takes into account the capacity constraints for drones and vehicles. The model determines optimal locations for facilities and the distribution of supplies

Autonomous equipment is playing an increasingly important role in construction tasks. It is essential to equip autonomous equipment with powerful 3D detection capability to avoid accidents and inefficiency. However, there is limited research within the construction field that has extended detection to 3D. To this end, this study develops a light detection and ranging (LiDAR)-based deep-learning model

In many engineering applications, missing data during system identification can hinder the performance of the identified model. In this paper, a novel two-stage nonparametric framework is proposed for missing data imputation, uncertainty quantification, and its integration in system identification with reduced computational complexity. The framework does not require functional forms for both the imputation

The constant passage of trains on the railways tracks causes, in the course of time, deviations that must be corrected periodically by means of a track calibration process. It consists of designing a new layout, called recreated horizontal alignment (RHA), as close as possible to the deformed center track fulfilling also the technical constraints according to the operational requirements of the railway

Pavement segments are functionally interdependent under traffic equilibrium, leading to interdependent maintenance and rehabilitation (M&R) decisions for different segments, but it has not received significant attention in the pavement management community yet. This study developed a maintenance optimization model for interdependent pavement networks based on the simultaneous network optimization (SNO)

The diagnosis of railway system faults is significant for its comfort, efficiency, and safety. The rail surface wear is the key impact factor when considering the health conditions of rails. This paper accomplishes contactless rail wear diagnosis by using multidimensional scaling based on a novel informational dissimilarity measure (IDM) to cluster intact and different worn rail profile data. The IDM

The occurrence of pavement cracks poses a significant potential threat to road safety, thus the rapid and accurate acquisition of pavement crack information is of paramount importance. Deep learning methods have the capability to offer precise and automated crack detection solutions based on crack images. However, the slow detection speed and huge model size in high-accuracy models are still the main

This study introduces the constraint-aware optimization model (CAOM), a novel optimization framework designed to optimize the size, shape, and topology of plane truss structures simultaneously. Unlike traditional optimization models, which rely on gradient descent and frequently struggle with managing various constraints due to their dependence on a single optimization agent, CAOM effectively addresses

Microimage strain sensing (MISS) is a novel piston-type sensor based on microscopic vision. In this study, optical disc slice is used as information carriers to improve MISS. There are multiple pits on the surface of an optical disc. By using machine vision algorithms, the pits can be converted into digital information, making them scales for recording displacements. By this means, we proposed a sensing

The relationship between real-world traffic and pavement raveling is unclear and subject to ongoing debates. This research proposes a novel approach that extends beyond traditional correlation analyses to explore causal mechanisms between mixed traffic and raveling. This approach incorporates the causal discovery method, and is applied to five Dutch porous asphalt (PA) highway sites that have substantial

Network-wide short-term passenger flow prediction is critical for the operation and management of metro systems. However, it is challenging due to the inherent non-stationarity, nonlinearity, and spatial–temporal dependencies within passenger flow. To tackle these challenges, this paper introduces a hybrid model called multi-scale dynamic propagation spatial–temporal network (MSDPSTN). Specifically

Point cloud of existing bridges provides important applications in their maintenance and management, such as to the three-dimensional (3D) model creation. However, point cloud data acquired in actual bridges are caused missing parts due to occlusions and limitations in sensor placements. This study proposes a learning method to realize the point cloud completion of such structure: the component-wise

Traditional computational fluid dynamics (CFD) methods used for wind field prediction can be time-consuming, limiting architectural creativity in the early-stage design process. Deep learning models have the potential to significantly speed up wind field prediction. This work introduces a convolutional neural network (CNN) approach based on the U-Net architecture, to rapidly predict wind in simplified

This study presents a novel multistage expressway fast charging station (EFCS) planning problem coordinated with the dynamic regional power structure (PS) transformation. Under the prerequisite of the EFCS network's sustainable operation, network accessibility, and orderly construction, a three-step planning method oriented to the enhancement of energy saving and emission reduction (ESER) benefits

Virtual trial assembly (VTA) using 3D laser scanning as the digital carrier can overcome the shortcomings of time-consuming and costly physical preassembly. However, its application in large steel structures with bolted connections remains limited. First, this study introduces a novel approach for acquiring multiscale point cloud data of large steel members using terrestrial laser scanners (TLSs) and

This study develops an intelligent optimization method of the facility environment (i.e., road facilities and surrounding landscapes) from drivers’ visual perception to adjust operation speeds on rural roads. Different from previous methods that heavily rely on expert experience and are time-consuming, this method can rapidly generate optimized visual images of the facility environment and promptly

High‐resolution image segmentation is essential in structural health monitoring (SHM), enabling accurate detection and quantification of structural components and damages. However, conventional convolutional neural network‐based segmentation methods face limitations in real‐world deployment, particularly when handling high‐resolution images producing low‐resolution outputs. This study introduces a

Point clouds are utilized to enable automated engineering applications for their ability to represent spatial geometry. However, they inherently lack detailed surface textures, posing challenges in differentiating objects at the texture level. Hence, this study introduces a 2D–3D fusing approach, leveraging material properties recognized from registered images as an augmented feature to enhance deep

This paper proposes a new stochastic model updating method based on the homotopy surrogate model (HSM) and Bayesian sampling. As a novel intrusive surrogate model, the HSM is established by the homotopy stochastic finite element (FE) method. Then combining the advanced delayed-rejection adaptive Metropolis–Hastings sampling technology with HSM, the structural FE model can be updated by uncertain measurement

This paper formulates a cooperative traffic control methodology that integrates traffic signal timing and ramp metering decisions into an optimization model to improve traffic operations in a corridor network. A mixed integer linear model is formulated and is solved in real time within a model predictive controller framework, where the cell transmission model is used as the system state predictor.

This paper proposes a method based on the clustering algorithm, deep learning, and transfer learning (TL) for short-term traffic prediction with limited data. To address the challenges posed by limited data and the complex and diverse traffic patterns observed in traffic networks, we propose a profile model based on few-shot learning to extract each detector's unique profiles. These profiles are then