Computer-Aided Civil and Infrastructure Engineering最新文献_第4页

Cover Image, Volume 40, Issue 12 封面图片，第40卷，第12期

IF 8.5 1区工程技术

Computer-Aided Civil and Infrastructure Engineering Pub Date : 2025-04-28 DOI: 10.1111/mice.13498

引用次数: 0

An integrated framework for multiple traffic anomalies detection on highways using vehicle trajectories 基于车辆轨迹的高速公路多重交通异常检测集成框架

IF 11.775 1区工程技术

Computer-Aided Civil and Infrastructure Engineering Pub Date : 2025-04-28 DOI: 10.1111/mice.13494

Zhiyuan Liu, Anfeng Jiang, Zhirui Wang, Zhen Zhou, Lue Fang, Qixiu Cheng, Ziyuan Gu

{"title":"An integrated framework for multiple traffic anomalies detection on highways using vehicle trajectories","authors":"Zhiyuan Liu, Anfeng Jiang, Zhirui Wang, Zhen Zhou, Lue Fang, Qixiu Cheng, Ziyuan Gu","doi":"10.1111/mice.13494","DOIUrl":"https://doi.org/10.1111/mice.13494","url":null,"abstract":"Fast and accurate identification of traffic anomalies on highways is of utmost importance. This study presents an integrated framework for multiple traffic anomaly detection on highways using vehicle trajectories. The framework addresses both macroscopic congestion patterns and microscopic driving behaviors, offering a comprehensive solution that simultaneously detects multiple anomalies within a unified framework. The developed framework comprises three main components: data acquisition and preprocessing, vehicle trajectory recognition, and traffic anomaly detection. The former two components are responsible for acquiring real-time vehicle trajectories on highways. With such trajectory information and the continuously monitored short-term traffic state, the latter component seeks to simultaneously detect all the traffic anomalies via a tailored sub-algorithm for each of them. For macroscopic anomaly detection, an algorithm for detecting stop-and-go waves by constructing localized shockwaves is proposed to capture the propagation of traffic congestion waves even in limited field-of-view scenarios. For microscopic anomaly detection, a dynamic background traffic state updating mechanism is introduced, allowing the framework to adaptively integrate historical traffic data and environmental factors. Additionally, a double-layer stacking framework based on unsupervised methods is designed to integrate diverse feature types and addressing perspective distortions. The developed framework is tested in experiments on both simulation and real-world data on highways. The results confirm its effectiveness in the simultaneous detection of multiple traffic anomalies within an integrated framework.","PeriodicalId":156,"journal":{"name":"Computer-Aided Civil and Infrastructure Engineering","volume":"94 1","pages":""},"PeriodicalIF":11.775,"publicationDate":"2025-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143884948","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Cover Image, Volume 40, Issue 12 封面图片，第40卷，第12期

IF 8.5 1区工程技术

Computer-Aided Civil and Infrastructure Engineering Pub Date : 2025-04-28 DOI: 10.1111/mice.13499

引用次数: 0

A first-order link-based flow model with variable speed limits and capacity drops for freeway networks 高速公路网络一阶可变限速和容量下降的路段流模型

IF 11.775 1区工程技术

Computer-Aided Civil and Infrastructure Engineering Pub Date : 2025-04-23 DOI: 10.1111/mice.13492

Lei Wei, Yu Han, Meng Wang

{"title":"A first-order link-based flow model with variable speed limits and capacity drops for freeway networks","authors":"Lei Wei, Yu Han, Meng Wang","doi":"10.1111/mice.13492","DOIUrl":"https://doi.org/10.1111/mice.13492","url":null,"abstract":"First-order link-based traffic flow models are computationally efficient in simulating freeway networks. However, the standard link transmission models fall short of reproducing traffic phenomena such as capacity drop (CD). Moreover, traffic control measures such as variable speed limits (VSLs) control may change the fundamental diagram and should be captured by traffic flow models. This study proposes a first-order link-based flow model incorporating VSL and CD for freeway simulation. In the proposed model, the vehicle flow through each link is characterized by cumulative inflow and outflow, which are influenced by the time-varying free flow speed caused by the VSL at the link's upstream boundary. CD is modeled by incorporating the traffic state-dependent capacity at the freeway lane-drop positions. A node model is then developed to determine and regulate the flow propagation between adjacent links. Simulation experiments were conducted on freeways to evaluate the model's effectiveness. The results demonstrate its ability to accurately predict traffic operations under VSL and CD while maintaining a computationally tractable representation of flow propagation.","PeriodicalId":156,"journal":{"name":"Computer-Aided Civil and Infrastructure Engineering","volume":"32 1","pages":""},"PeriodicalIF":11.775,"publicationDate":"2025-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143862456","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

High-resolution flood probability mapping using generative machine learning with large-scale synthetic precipitation and inundation data 基于大规模合成降水和淹没数据的生成式机器学习的高分辨率洪水概率映射

IF 11.775 1区工程技术

Computer-Aided Civil and Infrastructure Engineering Pub Date : 2025-04-23 DOI: 10.1111/mice.13490

Lipai Huang, Federico Antolini, Ali Mostafavi, Russell Blessing, Matthew Garcia, Samuel D. Brody

{"title":"High-resolution flood probability mapping using generative machine learning with large-scale synthetic precipitation and inundation data","authors":"Lipai Huang, Federico Antolini, Ali Mostafavi, Russell Blessing, Matthew Garcia, Samuel D. Brody","doi":"10.1111/mice.13490","DOIUrl":"https://doi.org/10.1111/mice.13490","url":null,"abstract":"High-resolution flood probability maps are instrumental for assessing flood risk but are often limited by the availability of historical data. Additionally, producing simulated data needed for creating probabilistic flood maps using physics-based models involves significant computation and time effort, which inhibit its feasibility. To address this gap, this study introduces Precipitation-Flood Depth Generative Pipeline, a novel methodology that leverages generative machine learning to generate large-scale synthetic inundation data to produce probabilistic flood maps. With a focus on Harris County, Texas, Precipitation-Flood Depth Generative Pipeline begins with training a cell-wise depth estimator using a number of precipitation-flood events model with a physics-based model. This cell-wise depth estimator, which emphasizes precipitation-based features, outperforms universal models. Subsequently, the conditional generative adversarial network (CTGAN) is used to conditionally generate synthetic precipitation point cloud, which are filtered using strategic thresholds to align with realistic precipitation patterns. Hence, a precipitation feature pool is constructed for each cell, enabling strategic sampling and the generation of synthetic precipitation events. After generating 10,000 synthetic events, flood probability maps are created for various inundation depths. Validation using similarity and correlation metrics confirms the accuracy of the synthetic depth distributions. The Precipitation-Flood Depth Generative Pipeline provides a scalable solution to generate synthetic flood depth data needed for high-resolution flood probability maps, which can enhance flood mitigation planning.","PeriodicalId":156,"journal":{"name":"Computer-Aided Civil and Infrastructure Engineering","volume":"23 1","pages":""},"PeriodicalIF":11.775,"publicationDate":"2025-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143862457","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Cover Image, Volume 40, Issue 11 封面图片，第40卷，第11期

IF 8.5 1区工程技术

Computer-Aided Civil and Infrastructure Engineering Pub Date : 2025-04-20 DOI: 10.1111/mice.13486

引用次数: 0

Cover Image, Volume 40, Issue 11 封面图片，第40卷，第11期

IF 8.5 1区工程技术

Computer-Aided Civil and Infrastructure Engineering Pub Date : 2025-04-20 DOI: 10.1111/mice.13485

引用次数: 0

Predicting pavement cracking performance using laser scanning and geocomplexity-enhanced machine learning 使用激光扫描和地质复杂性增强机器学习预测路面开裂性能

IF 11.775 1区工程技术

Computer-Aided Civil and Infrastructure Engineering Pub Date : 2025-04-19 DOI: 10.1111/mice.13489

Chunjiang Chen, Yongze Song, Wenbo Lv, Ammar Shemery, Keith Hampson, Wen Yi, Yun Zhong, Peng Wu

{"title":"Predicting pavement cracking performance using laser scanning and geocomplexity-enhanced machine learning","authors":"Chunjiang Chen, Yongze Song, Wenbo Lv, Ammar Shemery, Keith Hampson, Wen Yi, Yun Zhong, Peng Wu","doi":"10.1111/mice.13489","DOIUrl":"https://doi.org/10.1111/mice.13489","url":null,"abstract":"Transport infrastructure is vulnerable to crack formation and deterioration due to aging and repetitive loading. Accurate and timely crack assessment and prediction are crucial for effective road maintenance, but existing studies often rely on individual indicators such as crack types, attributes, and severity, which fail to capture the full complexity of crack deterioration. Furthermore, limited research has explored the long-term impacts of traffic, socioeconomic, and climate changes on crack progression, and existing machine learning (ML) models struggle to explain the contributions of individual predictors due to inherent complexities in such spatial data. This study develops a geocomplexity-enhanced ML (GML) approach to evaluate crack deterioration and predict cracks under various future scenarios in the Wheatbelt region of Australia. The study employs laser-scanning data to generate a novel cracking performance index (CPI) and integrates geocomplexity (GC) measures with random forest models to capture local spatial complexities. Results demonstrate that GML significantly outperforms standard ML models in predicting CPI-based crack deterioration. Crack predictions in future scenarios reveal that in the Wheatbelt region, changes in climate factors over time have a more substantial impact on crack progression than traffic and socioeconomic changes, and without effective maintenance, crack propagation rate will significantly increase. It provides empirical evidence for developing preventive maintenance strategies. The developed methods and findings can support the development of adaptive, climate-resilient infrastructure, and long-term road management strategies, enhancing the sustainability of transport infrastructure.","PeriodicalId":156,"journal":{"name":"Computer-Aided Civil and Infrastructure Engineering","volume":"17 1","pages":""},"PeriodicalIF":11.775,"publicationDate":"2025-04-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143849405","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Three-dimensional morphological analysis of Chang'e-5 lunar soil using deep learning-automated segmentation on computed tomography scans 基于计算机断层扫描的深度学习自动分割的嫦娥五号月球土壤三维形态分析

IF 11.775 1区工程技术

Computer-Aided Civil and Infrastructure Engineering Pub Date : 2025-04-19 DOI: 10.1111/mice.13487

Siqi Zhou, Yu Jiang, Xinyang Tao, Feng Li, Chi Zhang, Wei Yang, Yangming Gao

{"title":"Three-dimensional morphological analysis of Chang'e-5 lunar soil using deep learning-automated segmentation on computed tomography scans","authors":"Siqi Zhou, Yu Jiang, Xinyang Tao, Feng Li, Chi Zhang, Wei Yang, Yangming Gao","doi":"10.1111/mice.13487","DOIUrl":"https://doi.org/10.1111/mice.13487","url":null,"abstract":"Grain morphology is a fundamental characteristic of lunar soil that influences its mechanical properties, sintering behavior, and in situ resource utilization. However, traditional two-dimensional imaging methods are time-consuming and lack full three-dimensional (3D) structural information. This study presents an automated deep learning-based segmentation and reconstruction algorithm for high-resolution X-ray computed tomography scans of Chang'e-5 lunar soil samples. By integrating a U-Net convolutional neural network with a watershed algorithm, this method enables efficient and accurate 3D reconstruction of 553,578 lunar soil particles, significantly reducing manual annotation time. The results reveal a median particle size of 63.73 µm, an average aspect ratio of 0.55, and an average sphericity of 0.87, providing key insights into lunar regolith morphology. A clustering analysis identified 30 representative particle types, whose STereoLithography models will be made publicly available for further research and numerical simulations. These findings offer crucial data for discrete element modeling, thermal analysis, and engineering applications, supporting future lunar exploration and the development of sustainable lunar infrastructure.","PeriodicalId":156,"journal":{"name":"Computer-Aided Civil and Infrastructure Engineering","volume":"37 1","pages":""},"PeriodicalIF":11.775,"publicationDate":"2025-04-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143849406","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Parameter identification in prestressed concrete beams by incremental beam–column equation and physics-informed neural networks 基于增量梁柱方程和物理信息神经网络的预应力混凝土梁参数识别

IF 11.775 1区工程技术

Computer-Aided Civil and Infrastructure Engineering Pub Date : 2025-04-19 DOI: 10.1111/mice.13480

Yifan Yang, Zengwei Guo, Zhiyuan Liu

{"title":"Parameter identification in prestressed concrete beams by incremental beam–column equation and physics-informed neural networks","authors":"Yifan Yang, Zengwei Guo, Zhiyuan Liu","doi":"10.1111/mice.13480","DOIUrl":"https://doi.org/10.1111/mice.13480","url":null,"abstract":"This paper explores a novel methodology for identifying prestress force (and bending rigidity) from the perspective of static deflection methods and derives an incremental beam–column equation (iBCE) by elucidating the mechanisms underlying the long- and short-term behaviors, with particular emphasis on a physical system that disregards long-term deflections, including self-weight and equivalent lateral loads. It allows for the scaling of measurements from the real world to the corresponding nondimensional form of the physical system. The methodology begins by constructing the non-homogeneous terms of the equations using parameters and variables observed in the real world. Subsequently, utilizing second-order theory induced by incremental loads during step loading, the decoupling and identification of prestress force and bending rigidity are accomplished. The identification algorithm is constructed by integrating the nondimensional form of the iBCE with physics-informed neural networks. Without any additional regularization, the rationality and adaptability of this methodology are validated by nine examples that exhibit no nonlinear relations. A comprehensive series of systematic studies indicates that high accuracy can be achieved with the decoupled algorithm. This accuracy is possible when one mechanical parameter, such as bending rigidity or prestress force, is known and utilized to identify the remaining parameter. When both mechanical parameters are unknown, investing more in training costs enables the inverse identification of multiple parameters. Even with a 1% noise level, reasonable accuracy in the decoupling and identification of two mechanical parameters can be achieved. This methodology avoids the traditional limitations associated with solving the forward and inverse problems of incremental differential equations and transcendental equations.","PeriodicalId":156,"journal":{"name":"Computer-Aided Civil and Infrastructure Engineering","volume":"15 1","pages":""},"PeriodicalIF":11.775,"publicationDate":"2025-04-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143849407","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0