Computer-Aided Civil and Infrastructure Engineering最新文献

英文中文

Mainshock–aftershock sequence simulation via latent space encoding of generative adversarial networks 通过生成式对抗网络的潜在空间编码进行主震-余震序列模拟

IF 11.775 1区工程技术 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Computer-Aided Civil and Infrastructure Engineering

Pub Date : 2024-09-29 DOI: 10.1111/mice.13348

Zekun Xu, Jiaxu Shen, Huayong Wu, Jun Chen

Aftershocks (ASs) following strong mainshocks (MSs) can exacerbate structural damage or lead to collapse. However, the scarcity of recorded data necessitates reliance on artificial sequences, which have difficulty in characterizing the time-frequency correlation between MSs and ASs. This study innovatively converts the AS time history prediction into an image translation task, exploiting the invertible transformation between accelerograms and time-frequency representations. An encoder–decoder neural network is developed to encode the MS information into the latent space of a pre-trained generative adversarial network, enabling accurate AS predictions through the decoder. The integration of seismic parameters further improves the AS prediction performance. Comparative analyses demonstrate that the proposed method outperforms the traditional ones on accuracy and robustness and reproduces the non-stationarity of ASs.

强烈主震（MS）之后的余震（AS）会加剧结构破坏或导致坍塌。然而，由于记录数据稀缺，必须依赖人工序列，而人工序列难以表征 MS 和 AS 之间的时频相关性。本研究利用加速度图与时频表示之间的可逆变换，创新性地将自动变速器时间历史预测转换为图像转换任务。开发的编码器-解码器神经网络可将 MS 信息编码到预先训练好的生成式对抗网络的潜在空间中，从而通过解码器实现准确的 AS 预测。地震参数的整合进一步提高了 AS 预测性能。对比分析表明，所提出的方法在准确性和鲁棒性方面优于传统方法，并能再现 AS 的非平稳性。

引用次数: 0

Characterization of mechanical properties of shale constituent minerals using phase-identified nanoindentation 利用相位识别纳米压痕法表征页岩成分矿物的力学特性

IF 11.775 1区工程技术 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Computer-Aided Civil and Infrastructure Engineering

Pub Date : 2024-09-21 DOI: 10.1111/mice.13346

Jianting Du, Ka-Veng Yuen, Andrew J. Whittle, Liming Hu, Thibaut Divoux, Jay N. Meegoda

Characterization of mechanical properties of shale constituent minerals (viz., the mechanical genes of shale) has been challenging but of great significance for engineering applications in shale formations. In this study, a phase-identified nanoindentation is proposed to decode the mechanical genes of shale using a large nanomechanical dataset. With the consideration of uniform prior probability density functions (PDFs) and Gaussian posterior PDFs, the evidence of the measured dataset generated by the candidate model classes was assessed by applying the expectation–maximization algorithm and solving the Hessian matrix of the likelihood function. In contrast with Bayesian information criterion analysis, which has been widely used in prior studies, the proposed phase-identified nanoindentation approach is insensitive to the size of the dataset. Here, the identified clusters are well matched with the constituent phases measured by coupling grid nanoindentation and surface physicochemical identification.

页岩成分矿物的力学特性（即页岩的力学基因）的表征具有挑战性，但对页岩层的工程应用具有重要意义。本研究提出了一种相位识别纳米压痕法，利用大型纳米力学数据集解码页岩的力学基因。考虑到均匀先验概率密度函数（PDF）和高斯后验概率密度函数（PDF），通过应用期望最大化算法和求解似然函数的 Hessian 矩阵，评估了候选模型类产生的测量数据集的证据。与之前研究中广泛使用的贝叶斯信息准则分析相比，所提出的相位识别纳米压痕方法对数据集的大小不敏感。在这里，通过耦合网格纳米压痕法和表面物理化学识别法测得的相位与识别出的簇非常匹配。

引用次数: 0

Automated quantification of crack length and width in asphalt pavements 自动量化沥青路面的裂缝长度和宽度

IF 8.5 1区工程技术 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Computer-Aided Civil and Infrastructure Engineering

Pub Date : 2024-09-18 DOI: 10.1111/mice.13344

Zhe Li, Tuo Zhang, Yi Miao, Jiupeng Zhang, Mehran Eskandari Torbaghan, Yinzhang He, Jiasheng Dai

Rapid, accurate, and fully automated estimation of both length and width of asphalt pavement cracks, essential for achieving a proactive asset management, presents a significant challenge, primarily due to limitations in the effectiveness of automatic image segmentation and the accuracy of crack width and length estimation algorithms. To address this challenge, this paper introduces the Branch Growing (BG) algorithm, specifically designed for crack length estimation in asphalt pavements, along with an optimized OrthoBoundary algorithm tailored for crack width estimation. Leveraging four widely adopted deep learning models for asphalt pavement crack segmentation, four distinct sets of image segmentation results have been produced. Subsequently, a comprehensive evaluation has been conducted to assess the effectiveness of both crack dimensions estimation algorithms. The findings demonstrate that the integration of the BG algorithm, the optimized OrthoBoundary algorithm, and the fully convolutional network with the HRNet backbone achieve a prediction accuracy of 80.21% for crack length estimation and 84.32% for average width estimation. Moreover, the image processing speed, at a resolution of 3024 × 3024, can be maintained at approximately 5 s, with average width estimation observed to be up to 9.1-fold faster than the unoptimized OrthoBoundary algorithm. These results signify advancements in automated crack quantification methodologies, with implications for enhancing civil infrastructure maintenance practices.

沥青路面裂缝长度和宽度的快速、准确和全自动估算对于实现积极的资产管理至关重要，但主要由于自动图像分割的有效性以及裂缝宽度和长度估算算法的准确性受到限制，这给估算工作带来了巨大挑战。为应对这一挑战，本文介绍了专为沥青路面裂缝长度估算设计的分支生长（BG）算法，以及为裂缝宽度估算量身定制的优化 OrthoBoundary 算法。利用四种广泛采用的沥青路面裂缝分割深度学习模型，产生了四组不同的图像分割结果。随后，对两种裂缝尺寸估算算法的有效性进行了综合评估。研究结果表明，将 BG 算法、优化的 OrthoBoundary 算法和全卷积网络与 HRNet 骨干进行整合后，裂缝长度估算的预测准确率达到 80.21%，平均宽度估算的预测准确率达到 84.32%。此外，在分辨率为 3024 × 3024 的情况下，图像处理速度可保持在 5 秒左右，平均宽度估计速度比未经优化的 OrthoBoundary 算法快达 9.1 倍。这些结果标志着自动裂缝量化方法的进步，对加强民用基础设施维护实践具有重要意义。

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引用次数: 0

A hybrid non-parametric ground motion model of power spectral density based on machine learning 基于机器学习的功率谱密度混合非参数地动模型

IF 11.775 1区工程技术 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Computer-Aided Civil and Infrastructure Engineering

Pub Date : 2024-09-18 DOI: 10.1111/mice.13340

Jiawei Ding, Dagang Lu, Zhenggang Cao

In the fields of engineering seismology and earthquake engineering, researchers have predominantly focused on ground motion models (GMMs) for intensity measures. However, there has been limited research on power spectral density GMMs (PSD-GMMs) that characterize spectral characteristics. PSD, being structure-independent, offers unique advantages. This study aims to construct PSD-GMMs using non-parametric machine learning (ML) techniques. By considering 241 different frequencies from 0.1 to 25.12 Hz and evaluating eight performance indicators, seven highly accurate and stable ML techniques are selected from 12 different ML techniques as foundational models for the PSD-GMM. Through mixed effects regression analysis, inter-event, intra-event, and inter-site standard deviations are derived. To address inherent modeling uncertainty, this study uses the ratio of the reciprocal of the standard deviation of the total residuals of the foundational models to the sum of the reciprocals of the total residuals of the seven ML GMMs as weight coefficients for constructing a hybrid non-parametric PSD-GMM. Utilizing this model, ground motion records can be simulated, and seismic hazard curves and uniform hazard PSD can be obtained. In summary, the hybrid non-parametric PSD-GMM demonstrates remarkable efficacy in simulating and predicting ground motion records and holds significant potential for guiding seismic hazard and risk analysis.

在工程地震学和地震工程学领域，研究人员主要关注烈度测量的地动模型 (GMM)。然而，对描述频谱特征的功率谱密度 GMM（PSD-GMM）的研究还很有限。PSD 与结构无关，具有独特的优势。本研究旨在利用非参数机器学习（ML）技术构建 PSD-GMM。通过考虑从 0.1 到 25.12 Hz 的 241 个不同频率，并评估 8 个性能指标，从 12 种不同的 ML 技术中筛选出 7 种高度准确和稳定的 ML 技术作为 PSD-GMM 的基础模型。通过混合效应回归分析，得出了事件间、事件内和站点间的标准偏差。为了解决建模固有的不确定性，本研究使用基础模型总残差标准偏差的倒数与七个 ML GMM 总残差倒数之和的比值作为权重系数，构建混合非参数 PSD-GMM。利用该模型可以模拟地动记录，并获得地震危险性曲线和均匀危险性 PSD。总之，混合非参数 PSD-GMM 在模拟和预测地动记录方面表现出卓越的功效，在指导地震灾害和风险分析方面具有巨大的潜力。

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引用次数: 0

Diagnosis of high-speed railway ballastless track arching based on unsupervised learning framework 基于无监督学习框架的高速铁路无砟轨道起拱诊断

IF 11.775 1区工程技术 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Computer-Aided Civil and Infrastructure Engineering

Pub Date : 2024-09-18 DOI: 10.1111/mice.13342

Xueyang Tang, Yi Wang, Xiaopei Cai, Fei Yang, Yue Hou

Vehicle-mounted detection methods have been widely applied in the maintenance of high-speed railways (HSRs), providing feasibility for diagnosing ballastless track arching. However, applying detection data faces several key limitations: (1) The threshold mostly requires manual setting, making recognition accuracy highly subjective; (2) the extensive workload of manual inspections makes it challenging to label detection data, hindering the application of supervised learning approaches. To address these problems, this paper utilizes the longitudinal level irregularity data obtained from vehicle-mounted detection, employing the concept of unsupervised learning for dimensionality reduction, combined with clustering algorithms and minimal label fine-tuning, to design two frameworks: the fully unsupervised framework (FUF) and the few-shot fine-tuned framework (FFF). Experiments on dynamic detection data from a Chinese HSR line were conducted, comparing the performance of data dimensionality reduction, clustering, and classification under different strategy combinations. The results show that the improved variational autoencoder significantly enhances the performance of the encoder in dimensionality reduction, facilitating better feature extraction; the FUF achieves effective clustering outcomes without any labeled samples and its adjusted rand index score exceeded 0.8, showcasing its robustness and applicability in scenarios with no prior annotations; the FFF requires only a small number of labeled samples (labeling ratio of 5%) and achieves excellent performance, with metrics such as accuracy exceeding 0.85, thus greatly reducing the reliance on labeled data. This study offers a novel method for solving engineering issues with limited labeled data, providing an efficient solution for identifying track arching defects and advancing railway infrastructure monitoring.

车载检测方法已广泛应用于高速铁路（HSR）的维护中，为诊断无砟轨道拱起提供了可行性。然而，检测数据的应用面临着几个主要的局限性：（1）阈值大多需要人工设置，使得识别精度具有很大的主观性；（2）人工检测的工作量大，使得对检测数据进行标注具有挑战性，阻碍了监督学习方法的应用。为解决这些问题，本文利用车载检测获得的纵向水平不规则数据，采用无监督学习的降维概念，结合聚类算法和最小标签微调，设计了两个框架：完全无监督框架（FUF）和少量标签微调框架（FFF）。对中国高铁线路的动态检测数据进行了实验，比较了不同策略组合下数据降维、聚类和分类的性能。结果表明，改进的变分自动编码器显著提高了编码器的降维性能，有利于更好地提取特征；FUF在没有任何标注样本的情况下实现了有效的聚类结果，其调整后的兰德指数得分超过了0.8，显示了其在无先验标注场景下的鲁棒性和适用性；FFF只需要少量的标注样本（标注率为5%）就能实现出色的性能，准确率等指标超过了0.85，从而大大降低了对标注数据的依赖。这项研究为利用有限的标注数据解决工程问题提供了一种新方法，为识别轨道拱起缺陷和推进铁路基础设施监测提供了一种高效的解决方案。

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引用次数: 0

Corrigendum to “Cooperative control of a platoon of connected autonomous vehicles and unconnected human-driven vehicles” "一排联网自动驾驶车辆与非联网人类驾驶车辆的合作控制 "更正

IF 8.5 1区工程技术 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Computer-Aided Civil and Infrastructure Engineering

Pub Date : 2024-09-17 DOI: 10.1111/mice.13345

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 and Development Program of China, Grant/Award Number: 2021YFB1600100.”

The authors apologize for this error.

Zhou A, Peeta S, Wang J. 互联自动驾驶车辆和非互联人类驾驶车辆排的合作控制。计算机辅助土木与基础设施工程》。2023;38(18): 2513-2536.在 "资助信息 "部分，"中国国家重点研发计划，资助/奖励编号：2018YFE0102700 "有误。应为"中国国家重点研发计划，资助/奖励编号：2021YFB1600100。"作者对此错误深表歉意。

引用次数: 0

A relaxation-based Voronoi diagram approach for equitable resource distribution 基于松弛的 Voronoi 图公平分配资源法

IF 11.775 1区工程技术 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Computer-Aided Civil and Infrastructure Engineering

Pub Date : 2024-09-17 DOI: 10.1111/mice.13339

Kuangying Li, Asya Atik, Dayang Zheng, Leila Hajibabai, Ali Hajbabaie

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 to population blocks under equity constraints. Block-level census data are employed to define demand locations, identifying gender, age, and racial groups within each block using population data. A Lagrangian relaxation technique integrated with a modified Voronoi diagram is proposed to solve the location–allocation problem efficiently. Empirical case studies in Pennsylvania, using real-world data from the Centers for Disease Control and Prevention and health department websites, were conducted for the first 4 months of the COVID-19 vaccination campaign. Preliminary results show that the proposed solution algorithm effectively solves the problem, achieving a 5.92% reduction in total transportation cost and a 28.15% increase in demand coverage. Moreover, our model can reduce the deviation from equity to 0.07 (∼50% improvement).

本文介绍了一种旨在降低成本、提高需求覆盖率并确保疫苗公平分配的方法，该方法适用于需求明显超过供应的疫苗接种活动初始阶段。我们将一个增强的最大覆盖问题表述为一个混合整数线性程序，旨在使疫苗分配总成本最小化，同时在公平约束条件下最大化疫苗在人口区块的分配。利用区块级人口普查数据确定需求地点，并利用人口数据识别每个区块内的性别、年龄和种族群体。为了有效解决位置分配问题，提出了一种与改进的沃罗诺图相整合的拉格朗日松弛技术。在 COVID-19 疫苗接种活动的前 4 个月，利用疾病控制与预防中心和卫生部门网站提供的真实世界数据，在宾夕法尼亚州进行了经验案例研究。初步结果显示，所提出的解决算法有效地解决了问题，实现了总运输成本降低 5.92%，需求覆盖率提高 28.15%。此外，我们的模型还能将公平偏差降低到 0.07（提高了 50%）。

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引用次数: 0

Data-driven model for seismic assessment, design, and retrofit of structures using explainable artificial intelligence 使用可解释人工智能的结构抗震评估、设计和改造数据驱动模型

IF 11.775 1区工程技术 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Computer-Aided Civil and Infrastructure Engineering

Pub Date : 2024-09-17 DOI: 10.1111/mice.13338

Khurram Shabbir, Mohamed Noureldin, Sung-Han Sim

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 data-driven models for assessing, designing, and retrofitting of structures. The framework highlights the key global features of the model and further investigates them locally to adjust the input design parameters. It suggests the necessary changes in these inputs to achieve the desired structural performance. To achieve this, the framework employs interpretability techniques such as feature importance, feature interactions, Shapley Additive exPlanations, local interpretable model-agnostic explanations, partial dependence plot (PDP), and individual conditional expectation to highlight the important features. Additionally, a novel counterfactual) technique is applied for the first time as a design tool in seismic assessment and retrofitting of structures. The effectiveness of this framework is validated on a real benchmark structure through nonlinear time history analysis and natural earthquakes. The results show that the proposed framework is highly effective, especially under design-level earthquake conditions in achieving the necessary change in stiffness and strength of structures to meet the required seismic design objectives across different earthquake scenarios. This framework holds promise for wider adoption and applications in various other structural and civil engineering domains.

随着全球基础设施的老化和地震等自然灾害发生频率的增加，对建筑设计进行改造至关重要。虽然传统的数据驱动模型被广泛用于预测建筑状况，但最近在结构设计中对人工智能（AI）技术的探索还很有限。本研究介绍了一种新颖的可解释人工智能框架，该框架利用数据驱动模型对结构进行评估、设计和改造。该框架突出了模型的关键全局特征，并进一步对其进行局部研究，以调整输入的设计参数。它建议对这些输入参数进行必要的更改，以达到理想的结构性能。为实现这一目标，该框架采用了可解释性技术，如特征重要性、特征相互作用、夏普利加法前规划、局部可解释的模型对立解释、局部依赖图（PDP）和个体条件期望，以突出重要特征。此外，还首次将一种新颖的 "反事实"（counterfactual）技术用作结构抗震评估和改造的设计工具。通过非线性时间历史分析和自然地震，在实际基准结构上验证了该框架的有效性。结果表明，所提出的框架非常有效，尤其是在设计级地震条件下，能实现结构刚度和强度的必要变化，以满足不同地震情况下所需的抗震设计目标。该框架有望在其他各种结构和土木工程领域得到更广泛的采用和应用。

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引用次数: 0

Virtual reality-based dynamic scene recreation and robot teleoperation for hazardous environments 基于虚拟现实的危险环境动态场景再现和机器人远程操作

IF 11.775 1区工程技术 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Computer-Aided Civil and Infrastructure Engineering

Pub Date : 2024-09-12 DOI: 10.1111/mice.13337

Angelos Christos Bavelos, Efthymios Anastasiou, Nikos Dimitropoulos, George Michalos, Sotiris Makris

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 tasks from remote locations ensuring continuity regardless of distance. Real-time efficiency is achieved by updating the virtual environment from on-site sensors and mirroring the real environment, utilizing 3D reconstruction, Google Images, and video streams. Communication between VR and the remote robot is facilitated through a remote robot operating system connection. The efficacy of this concept will be validated through real road maintenance interventions.

虚拟现实（VR）技术在各行各业越来越重要，特别是在培训和远程操作中模拟真实环境。然而，它主要集中在静态、受控的环境中，如室内工业车间。本文提出了一种在不影响效率的前提下，在危险环境中安全远程控制机器人的新方法。操作员可以从远程位置执行任务，无论距离多远，都能确保连续性。利用三维重建、谷歌图片和视频流，通过现场传感器更新虚拟环境并镜像真实环境，从而实现实时高效。VR 与远程机器人之间的通信通过远程机器人操作系统连接实现。这一概念的有效性将通过实际道路维护干预来验证。

引用次数: 0

Cover Image, Volume 39, Issue 19 封面图片，第 39 卷第 19 期

IF 8.5 1区工程技术 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Computer-Aided Civil and Infrastructure Engineering

Pub Date : 2024-09-11 DOI: 10.1111/mice.13341

The cover image is based on the Article A multiscale model for wood combustion by H. L. Hao et al., https://doi.org/10.1111/mice.13187.

封面图片来自 H. L. Hao 等人撰写的文章《木材燃烧的多尺度模型》，https://doi.org/10.1111/mice.13187。

引用次数: 0

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