Monitoring the fatigue of construction equipment operators (CEOs) is critical for preventing accidents and ensuring precision construction occupational health and safety (COHS). However, there exists a theoretical dilemma between centralized technical efficiency and decentralized data privacy. Thus, this study introduces smart work package learning (SWPL), a decentralized deep learning approach to

Large-scale urban networks are usually loaded heterogeneously with a polycentric congestion pattern, resulting in a highly scattered network macroscopic fundamental diagram (NMFD or MFD). Thus, researchers have tried to partition city networks into homogeneous subzones. In this study, a six-step partitioning algorithm is proposed. The framework allows the NMFD information to be used. It combines traffic

This paper proposes the upscaling of conventional individual bridge health monitoring problems into urban regions and transportation networks via mobile and smart sensing techniques together with an innovative reconnaissance procedure. The paper associates structural failure probabilities with systemic features and proposes decision criteria to optimize postdisaster actions. Twenty bridges constituting

This study aims to facilitate a more reliable automated postdisaster assessment of damaged buildings based on the use of multiple view imagery. Toward this, a Multi-View Convolutional Neural Network (MV-CNN) architecture is proposed, which combines the information from different views of a damaged building, resulting in 3-D aggregation of the 2-D damage features from each view. This spatial 3-D context

In contrast to the traditional uncertainty quantification of the modal parameters identified from the output-only measurement data, this study develops an uncertainty propagation and quantification method for structural flexibility identified from input–output measurement data and then uses the derived flexibility matrix for structural reliability analysis. First, a novel procedure for variance estimations

Applying a subpixel refinement technique in vision-based displacement sensing can significantly improve the measurement accuracy. However, digital image signals from the camera are highly sensitive to drastically varying lighting conditions in the field measurements of structural displacement, causing pixels expressing a tracking target to have nonuniform grayscale intensity changes in different recording

This paper proposes a smoothness optimization method of horizontal alignment based on orthogonal least squares and the theory of controlling the track smoothness. With this method, multiple curve types can be fitted simultaneously according to the maintenance requirements, and the residual deviation in the track is optimized according to the smoothness standard. The target alignment of the tamping

This paper proposes an arterial signal control stochastic simulation-based optimization model with traffic safety and efficiency as biobjectives and solves it by a biobjective surrogate-based promising area search (BOSPAS) method. In this model, traffic safety and efficiency are indexed by the average potential collision energy (APCE) and the vehicular throughput of the arterial road, respectively

This research proposes a hybrid approach for predicting incident duration that integrates the salient features of both factorial design of experiments (DOE) and machine learning (ML). This study compares DOE with another widely used technique, forward sequential feature selection (FSFS). Moreover, to confirm the effectiveness and robustness of the proposed approach, multiple ML techniques are employed

The building group is the basis for the maintenance and operation of the city. The rapid recovery of community building group (CBG) can effectively reduce economic losses caused by earthquakes. There is service function interdependence among the buildings, and the impact of this interdependence on the postdisaster recovery of CBG is not clear. In order to improve the postdisaster recovery efficiency

In this article, an improved impervious solid boundary condition of the coupled method called smooth particle hydrodynamics and discrete element method (SPH-DEM) is proposed, which prevents the fluid particles from penetrating solid boundary under earthquake action. And an improved transmitting boundary condition of SPH-DEM is designed in order to conquer the reflection of seismic waves on the boundary

Convolutional neural networks (CNNs) have gained growing interest in recent years for their advantages in detecting cracks on concrete bridge components. Class imbalance is a fundamental problem in crack segmentation, resulting in unsatisfactory segmentation for tiny cracks. Besides, limited by the local receptive field, CNNs often cannot integrate local features with global dependencies, thus significantly

A method based on the baseline model of the visual characteristics of images (BMVCI) is proposed to detect cracks in concrete structures. BMVCI refers to the model, which consists of images of the noncrack areas of a concrete structure with cracks or images of the noncrack state of a concrete structure. Compared with the performance of edge detection (ED) methods for detecting cracks in concrete structures

Virtual coupling (VC) brings unprecedented opportunities for the train operation system by controlling multiple trains as a virtually coupled train set (VCTS) via train automation and communication. To deal with communication delays and small disturbances in a VCTS, this paper developed a tube-based control approach for the VCTS, focusing on optimizing the control performances and meanwhile guaranteeing

Pavement macrotexture is one of the major factors affecting pavement functions, and it is meaningful to reconstruct the pavement macrotexture rapidly and accurately for pavement life cycle performance and quality evaluation. To reconstruct pavement macrotexture from monocular image, a novel method was developed based on a deep convolutional neural network (CNN). First, the red-green-blue (RGB) images

A new convex quadratically-constrained quadratic programming (QCQP) model is proposed for modeling side-slopes volumes in the minimization of earthwork operations to compute the vertical alignment of a resource road while satisfying design and safety constraints. The new QCQP model is convex but nonlinear; it is compared to a state-of-the-art mixed integer linear programming (MILP) model. The QCQP

In bridge health monitoring (BHM), crack identification and width measurement are two of the most important indices for evaluating the functionality of bridges. In order to reduce the labor cost in field detection, researchers have proposed a variety of deep learning (DL)-based detection techniques for crack recognition. However, some problems still exist in extending these techniques to practical

Rapid adoption of electric vehicles (EVs) requires the development of a highly flexible charging network. The design and management of the charging infrastructure for EV-dominated transportation systems are intertwined with power grid operations both economically and technically. High penetration of EVs in the future can increase the charging loads and cause a wide range of operational issues in power

Deep learning (DL) methodologies have been recently employed to solve various civil and earthquake engineering problems. Nevertheless, due to the limited number of reliable data in the field of earthquake engineering, it is not convenient to obtain accurate results using DL. To tackle this challenge, the generative adversarial network (GAN) approach may be considered a reliable possible candidate.

This study is the first attempt to integrate flood resilience into the electric vehicle (EV) charging station planning process. Instead of fully avoiding flood-prone areas, an optimized placement considering the magnitude of flood inundations can minimize the impact of flood hazards and simultaneously maximize the socio-economic benefit of EV charging station networks. In this study, an integrated

The deflection of railroad bridges under in-service loads is an important indicator of the structure's health. Over the past decade, an increasing number of studies have demonstrated the efficacy of using vision-based approaches for displacement tracking of civil infrastructure. These studies have relied primarily on external processing of manually recorded videos of a structure's motion to estimate

The presented methodology results in an optimal portfolio of resilience-oriented resource allocation under weather-related risks. The pre-event mitigations improve the capacity of the transportation system to absorb shocks from future natural hazards, contributing to risk reduction. The post-event recovery planning results in enhancing the system's ability to bounce back rapidly, promoting network

Structural condition perception is a crucial step in structural health monitoring (SHM). Random loss or corruption of sensing data seriously hinders the reliability of the monitoring system. This paper discusses the recovery of randomly lost data in SHM from the perspective of conditional probability generation. A novel data-driven neural semantic recovery framework is proposed, transforming data recovery

Deep learning provides an efficient automated method for pavement condition surveys, but the datasets used for this model are usually images taken in good lighting conditions. If images are taken at night, this model cannot work effectively. This paper proposes a method for normalizing pavement images at night, which includes three main steps. First, the image feature point detection and matching method

Existing studies on automated construction equipment monitoring have focused mainly on activity recognition rather than fault detection. This paper proposes a novel equipment activity recognition and fault detection framework called hybrid unsupervised and supervised machine learning (HUS-ML). HUS-ML first identifies normal operations and known faulty conditions through supervised learning. Then, an

Overheight vehicle collisions continuously pose a serious threat to transportation infrastructure and public safety. This study proposed a vision-based method for automatic vehicle height measurement using deep learning and view geometry. In this method, vehicle instances are first segmented from traffic surveillance video frames by exploiting mask region-based convolutional neural network (Mask R-CNN)

The automatic recognition of cracks is an essential requirement for the cost-efficient maintenance of concrete structures, such as bridges, buildings, and roads. It should allow the localization and the determination of the crack type and the evaluation of the crack severity by providing information on the shape, orientation, and crack area and width. The first step in this direction is the automatized

Discontinuities in rock mass are the characteristic challenge of rock tunnel engineering projects, which have a vital impact on rock mass exposures' mechanical and hydrological characteristics. There is a growing demand for predicting the fracture maps during tunnel excavation to ensure a smooth tunnel excavation process. The computer vision measurement of fractures in the tunnel surface is a current

The demand for resilient and smart structures has been rapidly increasing in recent decades. With the occurrence of the big data revolution, research on data-driven structural health monitoring (SHM) has gained traction in the civil engineering community. Unsupervised learning, in particular, can be directly employed solely using field-acquired data. However, the majority of unsupervised learning SHM

A building information model for pipes already in place is essential in maintenance, for example, mending, reconstruction, and modernizing. However, current point cloud construction methods are not suited to complex piping systems, and many point cloud merging methods perform poorly in complex piping environments. To provide critical functions for constructing pipe point clouds from digital photogrammetry

The prefabricated building construction (PBC) project is sensitive to uncertainties due to the highly required coordination and interdependency among the installation activities, which may cause progress delay. Hence, it is necessary to monitor the installation progress and evaluate the schedule in terms of the project duration to take proactive control actions to avoid actual project delay. This study

Due to the lack of professional equipment, convenient deformation measurement methods are always needed, especially in damage assessments after extreme disasters. Different measurement and judgment methods require different reference data and corresponding processing methods. Considering the flexibility of smartphones, this paper proposes a rapid and simple process of visual measurement from the first-person

Modular integrated construction (MiC) is the most advanced construction method that involves off-site manufacturing, factory-to-site transportation, and on-site assembly of free-standing integrated modules. Despite the growing interest in the manufacturability of MiC, little research is available on the passing ability issues where efficient and safe transportation of modules is critical to the project's

Mountain railway alignment design is an important but complex civil engineering problem. To overcome the drastically undulating terrain, long tunnels and high bridges are major structures used along a mountain railway, which poses great challenges for railway design and construction. Unfortunately, despite being studied for many years, the crucial construction factors of complex structures have received

Aiming to solve the challenges of low detection accuracy, poor anti-interference ability, and slow detection speed in the traditional tunnel lining defect detection methods, a novel deep learning-based model, named You Only Look Once network v4 enhanced by EfficientNet and depthwise separable convolution (DSC; YOLOv4-ED), is proposed. In the YOLOv4-ED, EfficientNet is used as the backbone to improve

Collecting massive amounts of image data is a common way to record the postevent condition of buildings, to be used by engineers and researchers to learn from that event. Key information needed to interpret the image data collected during these reconnaissance missions is the location within the building where each image was taken. However, image localization is difficult in an indoor environment, as

This work proposes a nodal-based evolutionary design optimization algorithm to design frame structures whose edges are the Delaunay triangulation of homogeneously distributed nodes in the design domain. The remaining nodes can freely sway in the design domain except for the loading nodes and boundary nodes. As a result, it can extend the space of admissible solutions to this optimization problem and

Various earthquake early warning (EEW) methodologies have been proposed globally for speedily estimating information (i.e., location, magnitude, ground-shaking intensities, and/or potential consequences) about ongoing seismic events for real-time/near real-time earthquake risk management. Conventional EEW algorithms have often been based on the inferred physics of a fault rupture combined with simplified

1 INTRODUCTION The proposed approach aims at simultaneously optimizing 3-D alignments and station locations for a dedicated high-speed railway link between two selected terminal stations. The optimized design of the 3-D alignment for the high-speed link builds on the recent work of the same authors on mountain railway alignment optimization (Pu et al., 2019). The problem of optimizing station locations

Although previous research laid the foundation for vision-based monitoring systems using convolutional neural networks (CNNs), too little attention has been paid to the challenges associated with data imbalance and varying object sizes in far-field monitoring. To fill the knowledge gap, this paper investigates various loss functions to design a customized loss function to address the challenges. Scaffold

This paper proposes a deep reinforcement learning (DRL)-based distributed longitudinal control strategy for connected and automated vehicles (CAVs) under communication failure to stabilize traffic oscillations. Specifically, the signal-interference-plus-noise ratio-based vehicle-to-vehicle communication is incorporated into the DRL training environment to reproduce the realistic communication and time–space

Reinforced concrete structures exposed to marine environments often experience chloride ingress, reinforcement corrosion, and corrosion-induced cracking. The electrochemical repair method is a promising concrete rehabilitation technique with advantages of non-destructive and economic performance in crack repair, chloride removal, and corrosion protection, which is particularly suitable for structures

Structural vibration measurement is a crucial and necessary step for structural health monitoring. Recently, computer vision-based techniques have been proposed by researchers to measure structural motion remotely. However, the direct application of vision-based measurement to practical applications still faces some challenges, mainly because intrinsic camera vibration can introduce significant errors

Accurate and reliable lane detection is vital for the safe performance of lane-keeping assistance and lane departure warning systems. However, under certain challenging circumstances, it is difficult to get satisfactory performance in accurately detecting the lanes from one single image as mostly done in current literature. Since lane markings are continuous lines, the lanes that are difficult to be

Model updating techniques are often applied to calibrate the numerical models of bridges using structural health monitoring data. The updated models can facilitate damage assessment and prediction of responses under extreme loading conditions. Some researchers have adopted surrogate models, for example, Kriging approach, to reduce the computations, while others have quantified uncertainties with Bayesian

As the most common road distress, cracks have a substantial influence on the integrity of pavement structures. Accurate identification of crack existence and quantification of crack geometry are thus critical for the decision-making of maintenance measures. This paper proposes a novel neural network for the detection and evaluation of road cracks at pixel level, which combines the advantage of encoding–decoding

Reasonable maintenance plans are important for ensuring safe train operation and prolonging the service life of tracks. However, previous studies on the scheduling and optimization of railway track maintenance plans possess the following limitations. First, scheduling optimization models generally operate at the planning level with months as the time units rather than at the operation scheduling level

Assessing road degradation typically requires specialized hardware (such as laser profilometers) or labor-intensive visual inspection. To facilitate large-scale, timely inspection of road surfaces, opportunistic sensing is proposed: Sound and vibration measurements are obtained from vehicles that are on the road for other purposes than measuring road quality. Prior work has addressed the problem of

Direct integration methods are widely used for dynamic response computation. However, the performance of their computational accuracy significantly degrades with increasing the time step. Although machine learning methods can address this shortcoming, they require training data for dynamic response computation. This paper proposes a novel computational method to overcome these shortcomings. The proposed

Infrastructure service disruptions impact households in an affected community disproportionally. To enable integrating social equity considerations in infrastructure resilience assessments, this study created a new computational multi-agent simulation model, which enables integrated assessment of hazard, infrastructure system, and household elements and their interactions. With a focus on hurricane-induced

Hundreds of fatal accidents occur each year due to wrong-way driving (WWD). Although several methods have been developed to detect WWD using existing closed-circuit television (CCTV) data, they all require manual recalibration whenever a camera rotates, and are thus not scalable across statewide CCTV networks. This paper, therefore, proposes an end-to-end deep-learning-based model that considers camera

Virtual–real interactive testing (VRIT) is a practical approach to assessing the safety and intelligence of connected vehicles. Under this testing scheme, state consistency between physical and virtual spaces must be guaranteed. This paper proposes a multispace synchronization framework to ensure the desired interactive performance of VRIT systems. According to this framework, the synchronization process

This study proposes an unsupervised, online structural health monitoring framework robust to the sensor configuration, that is, the number and placement of sensors. The proposed methodology leverages generative adversarial networks (GANs). The GAN's discriminator network is the novelty detector, while its generator provides additional data to tune the detection threshold. GAN models are trained with

Water distribution networks (WDNs) are critical infrastructure for communities. The dramatic expansion of the WDNs associated with urbanization makes them more vulnerable to high-consequence hazards such as earthquakes, which requires strategies to ensure their resilience. The resilience of a WDN is related to its ability to recover its service after disastrous events. Sound decisions on the repair

The vertical alignment optimization is about developing a minimum cost curvilinear vertical profile of constrained grade sections and appropriate non-overlapping vertical curves passing through fixed control points with elevation constraints. Variations in ground profile and discreteness in unit cutting and filling costs make it a non-convex, noisy, constrained optimization problem with many local

The data-driven approach based on plenty of high-fidelity data such as experimental data becomes prevalent in the prediction of structural behavior. However, sometimes the high-fidelity data are hard to obtain and are only in small amount. Meanwhile, the low-fidelity data like simulation result are in large amount but their accuracy is relatively poor and are not suitable for establishing models. Thus