Computer-Aided Civil and Infrastructure Engineering最新文献

Mass timber construction has gained significant traction in recent years due to its sustainability and lower energy demands. However, its broader adoption remains limited by higher material costs, compared to conventional construction materials. To address this challenge, this study introduces a Monte Carlo tree search (MCTS)-based optimization framework aimed at minimizing the material cost of single-story post–beam–panel mass timber frame designs under gravity loads. By formulating the design task as a Markov Decision process, the MCTS algorithm can systematically guide step-by-step design decisions toward cost-efficient outcomes while satisfying structural constraints. The methodology is tested on four design scenarios modeled after real building dimensions. Results show that MCTS is capable of finding near-optimal solutions within just 1000 iterations, significantly reducing the computational effort required by exhaustive brute-force search. These findings underscore the effectiveness of MCTS as a promising tool for structural optimization in mass timber construction.

This paper proposes a structural optimization framework referred to as the ternary-quantized gradient (TQG) method. Departing from the prevailing assumption of a fixed design variable dimension during the search, it performs integrated optimization of size, shape, and topology without pre-specifying the dimension. The proposed method combines a single-agent search scheme, zeroth-order optimization, a Leaky ReLU-based penalty function, and an additional exploration strategy to enable efficient and automated design space exploration through implicit topology control. The proposed method was validated through optimization of a truss cantilever and truss girder, representing stiffness- and strength-governed structures, respectively. In both cases, TQG method successfully determined the optimal panel count while simultaneously optimizing size and shape, producing results comparable to those obtained by well-known metaheuristic algorithms under predefined topology settings. The proposed method was applied to the early-stage decision-making process of high-rise building design, optimizing panel count and configuration to efficiently resist lateral loads while satisfying serviceability constraints. These results demonstrate that the proposed TQG method can optimize the number of design variables through implicit topology control while achieving integrated optimization of size, shape, and topology in a single run, offering a practical and efficient approach for early-stage structural design.

Road damage detection faces significant challenges including extreme scale variations, complex visual interference from road textures, diverse orientational patterns, and irregular boundaries. This paper proposes a semantic-enhanced and adaptive fusion detection transformer to address these domain-specific challenges through two synergistic innovations. The semantic enhancement attention module exploits distinctive frequency-domain characteristics of road damages through learnable spectral processing, where damaged regions exhibit 50.5% higher high-frequency energy, compared to intact surfaces, enabling effective discrimination between structural defects and background interference. The adaptive information fusion module implements a three-stage progressive architecture: loss-less transmission establishes information integrity across extreme scales through amplitude-aware upsampling and attention-driven fusion; omnidirectional pattern capture via multi-directional convolutions addresses diverse damage orientations; dual-path processing optimizes computational efficiency. Comprehensive evaluation across four datasets demonstrates state-of-the-art performance with significant improvements: 83.4% mean average precision at intersection over union threshold 0.5 on UAV-PDD2023 (+3.4% over previous best), 31.2% on CNRDD (+1.3%), 61.9% on RDD2020 (+3.0%), and 90.2% on nighttime NPD (+0.6%), while achieving superior efficiency with 62 giga floating-point operations, 20 million parameters, and 51 frames per second inference speed for real-time processing.

The objective of this paper is to present a novel intelligent train control system for virtual coupling in railroads based on a learning model predictive control (LMPC). Virtual coupling is an emerging railroad technology that reduces the distance between trains to increase the capacity of the line, whereas LMPC is an optimization-based controller that incorporates artificial intelligence methods to improve its control policies. By incorporating data from past experiences into the optimization problem, LMPC can learn unmodeled dynamics and enhance system performance while satisfying constraints. The LMPC developed in this paper is simulated and compared, in terms of energy consumption, with a general MPC, without learning capabilities. The simulations are divided into two main practical applications: an LMPC applied only to the rear trains (followers) and an LMPC applied to both the followers and the first front train of the convoy (leader). Within each application, the LMPC is independently tested for three railroad categories: metro, regional, and high-speed. The results show that the LMPC reduces energy consumption in all simulation cases while approximately maintaining speed and travel time. The effect is more pronounced in rail applications with frequent speed variations, such as metro systems, compared with high-speed rail. Future research will investigate the impact of using real-world data in place of simulated data.

Automated and non-invasive anomaly detection methods are critical for ensuring operational safety and continuity on intelligent construction sites. This study proposes a novel unsupervised audio signal processing framework for real-time monitoring of construction equipment based on their operational acoustic signatures. The proposed method relies exclusively on historical data from normal operations to characterize temporal audio patterns, enabling the detection of previously unseen anomalies without requiring labeled anomaly data for training. It extracts 39 acoustic features from raw waveform audio and reconstructs them using a temporal convolutional network autoencoder. Anomalies are identified by monitoring the reconstruction errors through a multivariate cumulative sum (MCUSUM) statistical process control chart. Upon detecting an anomaly, the method identifies contributing acoustic features via correlation maximization decomposition of MCUSUM statistics. The proposed method detected 100% of anomalies in 50 real-world slider rail tests, with an average detection time of 2.15 s post onset.

The cover image is based on the article Optimization of passenger flow control and parallel bus bridging in urban rail transit based on intelligent transport infrastructure by Qingqing Zhao et al., https://doi.org/10.1111/mice.13460.

The cover image is based on the article Automated form-finding method of spoke cable net structures using physics-constrained neural network by Xuanzhi Li et al., https://doi.org/10.1111/mice.13491.

Freeway entrance ramp merging areas are often prone to traffic congestion and accidents, primarily due to the complex interactions between vehicles. The development of connected autonomous vehicles (CAVs) offers significant potential to mitigate these issues. This paper proposes a motion trajectory planning strategy that simultaneously enhances trajectory quality, computational efficiency, and driving comfort. The proposed method comprises three core modules: a hierarchical finite state machine-based lane-change decision model that accelerates decision-making by quantifying risks and opportunities in the S–T diagram; a lateral trajectory planning module that generates lateral paths using an improved grid sampling approach; and a longitudinal velocity planning module that employs a pruned dynamic programming algorithm to speed up the search process. The resulting lateral and longitudinal trajectories are then coupled to form an initial solution, which is further optimized by an iterative linear quadratic regulator under multi-objective constraints. Working in concert, these modules enable CAVs to perform real-time, smooth, and safe lane-change maneuvers. The experimental results indicate that the optimized lane-change method reduces time cost by approximately 20%, compared to the five-polynomial trajectory planning method. Moreover, the velocity and acceleration profiles are smoother than those produced by conventional dynamic programming algorithms, ensuring both trajectory smoothness and control precision. These improvements significantly enhance the safety and efficiency of the lane-change process.

Urban growth has generated an increasing demand for robust and accurate traffic monitoring systems. Traditional technologies such as inductive loops and computer vision have limitations under adverse environmental conditions and in high-density traffic scenarios. This work proposes a modular architecture for traffic monitoring based exclusively on 3D Light Detection and Ranging (LiDAR) sensors, which stand out for their high accuracy and resilience to light and weather variations. The proposed architecture consists of eight independent modular levels. Its main innovations include optimized methods for background subtraction, real-time detection algorithms using machine learning with subsampling techniques, a multi-object tracking system that preserves vehicle identity in the face of occlusions, and a decision tree-based classifier that assigns vehicle type based on geometric characteristics. The solution was validated in real conditions on the M-13 motorway in Madrid (Spain) over more than 100 h of data recordings, using an instrumented gantry with a 3D LiDAR that supervises two-lane roadway, evaluating critical attributes for traffic management such as vehicle detection, classification, and tracking. The proposed design facilitates scalability and compatibility with various sensors, enabling advanced applications in traffic monitoring, cooperative connected vehicle (vehicle-to-infrastructure) contexts, high-level automated driving, and smart highways.

Remote sensing (RS) technology is crucial for monitoring global changes, plays an important role in urban planning, disaster management, and environmental monitoring through building change detection (BCD). High-resolution RS images used in BCD tasks face challenges such as overlooked derived information, complex backgrounds, sample imbalances, and the selection of an optimal learning rate, complicating their effective utilization. Consequently, the ACSPNet, a Siamese-architecture BCD network, is introduced. Firstly, an adaptive edge visual feature extraction algorithm is designed to effectively capture architectural edge features, provide important a priori information, and reduce data redundancy and background noise problems. Secondly, coordinated context threshold-awareness is proposed to enhance the convolutional feature representation through cross-attention and threshold-awareness strategies to improve the sensitivity of the model to discriminative features and effectively cope with complex background interference. Subsequently, the self-calibrating visual field-enhanced convolution is developed to expand the perceptual range of input features, significantly enhancing the detection of foreground information. This approach sharpens the network's focus on the foreground region and effectively addresses the issue of sample imbalance. Finally, a particle chameleon algorithm is designed to search for the optimal learning rate, thereby accelerating convergence and improving training efficiency. Comparative experiments highlight ACSPNet's superior performance over six state-of-the-art BCD methods across the self-built dataset (CSUFT-CD) and three public datasets: Google-CD, WHU-CD, and LEVIR-CD.