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Preload State Detection of Transmission Tower Bolt Joints Based on Sound Recognition 基于声音识别的输电塔螺栓接头预紧状态检测
IF 5.1 2区 工程技术 Q1 CONSTRUCTION & BUILDING TECHNOLOGY Pub Date : 2025-10-22 DOI: 10.1155/stc/3099843
Dehong Wang, Zixuan Zhang, Cong Zeng

Timely mastering the preload state of transmission tower bolts is crucial for maintaining the structural performance of transmission towers and preventing the occurrence of tower collapse accidents. A preload state detection method for transmission tower bolt joints based on sound recognition is proposed. Based on the Mel spectrogram, the bolt sound signal features are extracted, and the mapping relationship between the sound signal and the preload state is established after feature fusion; extreme gradient boosting (XGBoost) is introduced as an independent predictor in the deep forest model, and an improved deep forest model is obtained; finally, experiments are conducted on single-bolt and multibolt joints. The results show that the fusion feature proposed in this paper can effectively support multiple machine learning models. The improved deep forest model has an accuracy of 97.6% and 96.9% in identifying the preload state of single-bolt and multiple-bolt joints, respectively, and has a fast detection speed. The model can still achieve accurate classification even in the case of sample imbalance. The recognition accuracy of the method remains above 95% at −2, 0, and 2 dB signal-to-noise ratio (SNR) levels, demonstrating excellent noise immunity.

及时掌握输电塔螺栓预紧力状态,对于保持输电塔结构性能,防止塔倒塌事故的发生至关重要。提出了一种基于声音识别的输电塔螺栓连接预紧状态检测方法。基于Mel谱图提取锚杆声信号特征,特征融合后建立锚杆声信号与预加载状态的映射关系;在深度森林模型中引入极端梯度增强(XGBoost)作为独立预测因子,得到改进的深度森林模型;最后,对单螺栓和多螺栓节点进行了试验。结果表明,本文提出的融合特征可以有效地支持多种机器学习模型。改进的深度森林模型对单螺栓和多螺栓节点预紧状态的识别准确率分别达到97.6%和96.9%,且检测速度快。即使在样本不平衡的情况下,该模型仍能实现准确的分类。在- 2、0和2 dB信噪比(SNR)水平下,该方法的识别准确率保持在95%以上,具有良好的抗噪性。
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引用次数: 0
Smart Monitoring of Cyclic Freeze–Thaw Damage of Lining Concrete Using Dual-Channel Feature Fusion of Piezoelectric Conductance and Stress Wave Signals 基于压电电导和应力波双通道特征融合的衬砌混凝土循环冻融损伤智能监测
IF 5.1 2区 工程技术 Q1 CONSTRUCTION & BUILDING TECHNOLOGY Pub Date : 2025-10-15 DOI: 10.1155/stc/8554958
Chuan Zhang, Qixiang Yan, Xiaolong Liao, Zhengyu Xiong, Kai Yang, Shouju Miao, Binjia Li

The durability and stability of cold regional tunnels are extensively threatened by cyclic freeze–thaw environments, highlighting the necessity of accurate monitoring and assessing the accumulative damage within lining concrete. This study proposed an innovative integrated sensing strategy that concurrently incorporated the piezoelectric-based electromechanical impedance (EMI) and wave propagation (WP) techniques for the detection of cyclic freeze–thaw damage in bended concrete. First of all, the visual inspection and four-point flexural testing were performed to study the characteristics of damage evolution and strength deterioration of cyclically freeze–thawed concrete under different bending loads. In addition, the coupling effect of freeze–thaw cycling and bending on damage progression was interpreted. The degrading flexural strength was employed to quantify the overall damage degree. Moreover, the performances of EMI and WP methods in monitoring the concrete freeze–thaw damage were explored by analyzing the varying conductance and stress wave signatures based on conventional approaches. It was found that the statistic metrics of conductance and energy of stress wave signals served as reliable indicators of damage. The key novelty of this study was that a dual-channel feature fusion network (DCFF-Net) was developed to extract and coordinate damage-relevant features from cross-dimensional conductance and continuous wavelet transform spectral data, enabling automated and intelligent damage assessment. The proposed framework demonstrated a substantial improvement in damage classification performance, achieving accuracies exceeding 0.98 on two distinct datasets. This performance notably surpassed that of single-sensing methods. These findings manifested the feasibility and effectiveness of leveraging dual-channel piezoelectric data fusion for improved detection and characterization of complex damage evolution within concrete infrastructures.

冻融循环环境对寒冷地区隧道的耐久性和稳定性构成了广泛的威胁,因此对衬砌混凝土内部累积损伤进行准确监测和评估是十分必要的。本研究提出了一种创新的集成传感策略,该策略同时结合了基于压电的机电阻抗(EMI)和波传播(WP)技术,用于检测弯曲混凝土的循环冻融损伤。首先,通过目测和四点抗弯试验,研究了不同抗弯荷载作用下循环冻融混凝土的损伤演化和强度劣化特征。此外,还解释了冻融循环和弯曲对损伤进展的耦合效应。采用退化抗折强度来量化整体损伤程度。此外,在常规方法的基础上,通过分析不同电导和应力波特征,探讨了电磁干扰和WP方法在混凝土冻融损伤监测中的性能。结果表明,应力波信号的电导和能量的统计指标可以作为损伤的可靠指标。该研究的关键新颖之处在于开发了双通道特征融合网络(DCFF-Net),用于从跨维电导和连续小波变换光谱数据中提取和协调损伤相关特征,从而实现自动化和智能损伤评估。提出的框架在损伤分类性能上有了实质性的改进,在两个不同的数据集上实现了超过0.98的准确率。这一性能明显优于单感测方法。这些发现表明了利用双通道压电数据融合来改进混凝土基础设施中复杂损伤演变的检测和表征的可行性和有效性。
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引用次数: 0
Seismic Protection of Long-Period Structures Using Modified Maxwell–Wiechert Damping Devices 改进Maxwell-Wiechert阻尼装置对长周期结构的抗震保护
IF 5.1 2区 工程技术 Q1 CONSTRUCTION & BUILDING TECHNOLOGY Pub Date : 2025-10-13 DOI: 10.1155/stc/4735698
Wei Liu, Jiang Liu

In long-period structures, rate-independent linear damping (RILD) offers superior control of acceleration responses compared with conventional viscous damping. However, the noncausality of RILD limits its application in buildings. Further, practical implementation of methods to approximate RILD in specific situations remains challenging owing to the difficulty of mechanical realization. In this study, the inerter, Maxwell–Wiechert model, and negative stiffness were employed to construct novel RILD-approximating devices: the negative-stiffness inerter Maxwell–Wiechert (NSIMW) model and the NSI-spring Maxwell–Wiechert (NSISMW) model. These devices were designed to replicate the storage and loss stiffness of RILD at the target frequencies. This study proposes causal devices for approximating RILD using inerters, negative stiffness, and Maxwell–Wiechert models. The initial design—the negative-stiffness viscous-inerter Maxwell–Wiechert (NSVIMW) model—was unrealizable owing to negative design parameters. To resolve this, the viscous damper was removed, resulting in a simplified NSI Maxwell–Wiechert (NSIMW) model. An enhanced version of the NSISMW model was further developed by adding a spring element. Both models were designed to match the storage and loss stiffness of RILD at target frequencies, and direct design methods were developed to determine their parameters. Real-time hybrid simulations and analytical analyses were conducted to evaluate seismic control performance. The findings demonstrate that while NSIMW provides a causal approximation of RILD with moderate effectiveness, the NSISMW model achieves a more satisfactory seismic performance than the RILD, negative-stiffness Maxwell–Wiechert model, and traditional tuned viscous mass dampers. These results clarify the relative merits of the two designs and suggest that the NSISMW model offers a promising direction for the practical implementation of RILD-inspired damping systems.

在长周期结构中,与传统的粘性阻尼相比,速率无关线性阻尼(RILD)提供了更好的加速度响应控制。然而,RILD的非因果性限制了其在建筑中的应用。此外,由于机械实现的困难,在具体情况下近似RILD的实际实施方法仍然具有挑战性。在本研究中,利用惯性器、Maxwell-Wiechert模型和负刚度构建了新型的rild逼近装置:负刚度惯性器Maxwell-Wiechert (NSIMW)模型和nsi -弹簧Maxwell-Wiechert (nismw)模型。这些器件被设计成在目标频率下复制RILD的存储和损耗刚度。本研究提出了使用干涉器、负刚度和Maxwell-Wiechert模型近似RILD的因果装置。最初的设计-负刚度粘滞器Maxwell-Wiechert (NSVIMW)模型-由于负设计参数而无法实现。为了解决这个问题,我们去掉了粘性阻尼器,得到了简化的NSI Maxwell-Wiechert (NSIMW)模型。nismw模型的增强版本通过添加弹簧元件进一步开发。设计了两种模型,以匹配目标频率下RILD的存储和损耗刚度,并开发了直接设计方法来确定其参数。进行了实时混合仿真和分析分析,以评估地震控制性能。研究结果表明,虽然NSIMW提供了RILD的因果近似,但效果中等,但nismw模型比RILD、负刚度Maxwell-Wiechert模型和传统的调谐粘性质量阻尼器获得了更令人满意的抗震性能。这些结果阐明了两种设计的相对优点,并表明nismw模型为实际实现rild启发的阻尼系统提供了一个有希望的方向。
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引用次数: 0
A Semisupervised Transfer Learning Method for Structural Damage Identification and Its Application to a Cable-Stayed Bridge 结构损伤识别的半监督迁移学习方法及其在斜拉桥上的应用
IF 5.1 2区 工程技术 Q1 CONSTRUCTION & BUILDING TECHNOLOGY Pub Date : 2025-10-12 DOI: 10.1155/stc/6840921
Naiwei Lu, Xiangyuan Xiao, Jian Cui, Yiru Liu, Yuan Luo

Engineering structures usually have rare damage scenarios, which provide insufficient labels to identify the structural damage. Therefore, the effectiveness and accuracy of traditional data-driven methods with limited training samples is an urgent task. This study develops a semisupervised transfer learning (TL) method for structural damage identification. The effectiveness is validated through experiments on a scale cable-stayed bridge specimen. Initially, a convolutional neural network (CNN) was trained based on a numerical dataset simulated by a finite element model, where a source domain is modeled with strong recognition performance and generalizability. Subsequently, the network structure and hyperparameters in the source domain were transferred to the corresponding positions in the experimental training model to create a pretrained model in the target domain; this pretrained model was updated by using site-specific measured data from the engineering structure in the target domain. Finally, a dynamic threshold self-training algorithm was employed to further optimize the model: in the initial stage, a high-confidence threshold of 90% was set to filter reliable pseudolabels, and the threshold decreases to 80% gradually, under the condition that the model cannot provide predictions with sufficiently high confidence. Experimental study on a scale cable-stayed bridge specimen was conducted to demonstrate the effectiveness of the proposed method. Four networks were established based on (1) experimental samples; (2) both labeled and unlabeled experimental samples with the self-training algorithm; (3) TL from the finite element model and experimental samples; and (4) applying self-training with unlabeled samples to the target model derived from TL. The results indicate that the damage identification accuracy of the aforementioned models is 73.6%, 79.4%, 84.6%, and 89.8%, respectively. The TL improves the reliability of generating pseudolabels by utilizing the self-training process and unlabeled data and decreases errors in pseudolabels. Both finite element simulation data and practical unlabeled sample data were successfully combined by the TL and self-training semisupervised learning method for damage identification in cable-stayed bridges effectively.

工程结构通常具有罕见的损伤场景,这些场景提供的标识不足以识别结构损伤。因此,在训练样本有限的情况下,传统的数据驱动方法的有效性和准确性是一个迫切需要解决的问题。本研究提出一种半监督迁移学习(TL)方法用于结构损伤识别。通过斜拉桥模型试验,验证了该方法的有效性。首先,基于有限元模型模拟的数值数据集训练卷积神经网络(CNN),其中源域建模具有较强的识别性能和泛化能力。随后,将源域中的网络结构和超参数转移到实验训练模型中的相应位置,在目标域中创建预训练模型;该预训练模型通过使用目标域中工程结构的特定地点测量数据进行更新。最后,采用动态阈值自训练算法对模型进行进一步优化:在初始阶段,设置90%的高置信度阈值过滤可靠的伪标签,在模型无法提供足够高置信度预测的情况下,阈值逐渐降低至80%。通过对某斜拉桥试件的试验研究,验证了该方法的有效性。基于(1)实验样本,建立了四个网络;(2)使用自训练算法对标记和未标记的实验样本进行自训练;(3)有限元模型和实验样品的TL;(4)将未标记样本的自训练应用于TL导出的目标模型。结果表明,上述模型的损伤识别准确率分别为73.6%、79.4%、84.6%和89.8%。TL利用自训练过程和未标记的数据,提高了生成伪标签的可靠性,减少了伪标签的错误。基于自训练半监督学习方法的斜拉桥损伤识别,成功地将有限元模拟数据与实际无标记样本数据相结合,实现了斜拉桥损伤识别。
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引用次数: 0
Optimization of Concrete Mesoscopic Parameters Based on Deep Learning and Energy Dissipation Theory 基于深度学习和能量耗散理论的混凝土细观参数优化
IF 5.1 2区 工程技术 Q1 CONSTRUCTION & BUILDING TECHNOLOGY Pub Date : 2025-10-12 DOI: 10.1155/stc/8864055
You Wang, Weihang Li, Rui Wang, Bosong Ding, Dongchen Li

Optimizing the mesoscopic structure of concrete based on energy dissipation theory is an effective approach to enhancing its performance. However, related works are limited, and the mathematical relationship between the mesoscopic structure and the energy dissipation mechanism remains unclear. In this study, image processing technology and MATLAB were employed to quantitatively characterize the mesoscopic structure of concrete. A mesoscopic model of concrete was constructed using PFC2D to study the influence of mesoscopic parameters on energy dissipation, and a mathematical model of energy dissipation incorporating mesoscopic parameters was fitted using a deep neural network. In addition, the genetic algorithm with associated individuals (GA-AIs) was applied to optimize the mesoscopic parameters. The main findings are as follows: (1) Based on the entropy-containing random aggregate method, Fourier shape reconstruction analysis, and density-damping random field method, the aggregate spatial arrangement, shape characteristics, and mortar matrix characteristic property were quantified separately. (2) Under uniaxial compression, the three types of mesoscopic parameters showed positive correlations to varying degrees with the energy dissipation capacity of concrete. By adjusting these parameters, the energy dissipation capacity can be effectively modulated. (3) The GA-AIs efficiently optimized the mesoscopic parameters, enabling effective control of the energy dissipation capacity of concrete. Based on the optimization results under a specific working condition, the algorithm can infer mesoscopic parameter values to meet different performance requirements for this condition.

基于能量耗散理论优化混凝土细观结构是提高混凝土性能的有效途径。然而,相关工作有限,介观结构与能量耗散机制之间的数学关系尚不清楚。本研究采用图像处理技术和MATLAB对混凝土细观结构进行定量表征。利用PFC2D建立混凝土的细观模型,研究细观参数对耗能的影响,并利用深度神经网络拟合了包含细观参数的混凝土耗能数学模型。此外,采用关联个体遗传算法(GA-AIs)优化介观参数。主要研究结果如下:(1)基于含熵随机骨料法、傅里叶形状重建分析和密度-阻尼随飞机场法,分别量化了骨料的空间排列、形状特征和砂浆基体特征性质。(2)在单轴压缩条件下,3种细观参数与混凝土耗能能力均呈现不同程度的正相关关系。通过调整这些参数,可以有效地调节系统的耗能能力。(3) GA-AIs有效地优化了细观参数,有效地控制了混凝土的耗能能力。该算法根据特定工况下的优化结果,推断出满足该工况下不同性能要求的细观参数值。
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引用次数: 0
Multibridge Inference Structural Health Monitoring (MISHM): A Drive-By Crowdsensing Approach at the Network Level 多桥推理结构健康监测(MISHM):一种网络层面的驱动式群体感知方法
IF 5.1 2区 工程技术 Q1 CONSTRUCTION & BUILDING TECHNOLOGY Pub Date : 2025-10-12 DOI: 10.1155/stc/8624965
Jiangyu Zeng, Qipei Mei, Mustafa Gül

As aging bridge infrastructure poses increasing safety risks, there is a critical need for reliable and scalable Structural Health Monitoring (SHM) systems. Traditional SHM methods, which rely on fixed sensor networks and assessments of individual bridges, face significant challenges in scalability, cost, and efficiency—particularly in complex urban environments. To address these limitations, this study introduces the Multibridge Inference SHM (MISHM) framework. MISHM leverages drive-by monitoring and crowdsensing to observe multiple bridges simultaneously. It employs a feature-based analysis using Mel-frequency cepstral coefficients (MFCCs) and Kullback–Leibler (KL) Divergence to identify structural changes. Here, “inference” refers to drawing conclusions about the health of each individual bridge by comparing patterns and features gleaned from the entire network, rather than relying on isolated measurements. By making multiple comparisons across all monitored structures, MISHM enhances fault tolerance, reduces missed detections, and offers a scalable solution for smart city infrastructure monitoring. This framework represents a vital advancement in SHM systems, addressing the evolving needs of large-scale urban infrastructure management.

随着桥梁基础设施老化带来的安全风险日益增加,人们迫切需要可靠、可扩展的结构健康监测系统。传统的SHM方法依赖于固定的传感器网络和对单个桥梁的评估,在可扩展性、成本和效率方面面临重大挑战,特别是在复杂的城市环境中。为了解决这些限制,本研究引入了多桥推理SHM (MISHM)框架。MISHM利用行车监控和人群感知同时观察多个桥梁。它采用基于特征的分析,使用mel频率倒谱系数(MFCCs)和Kullback-Leibler (KL)散度来识别结构变化。这里的“推断”是指通过比较从整个网络中收集的模式和特征,而不是依赖于孤立的测量,得出关于每座桥梁健康状况的结论。通过对所有监控结构进行多次比较,MISHM增强了容错性,减少了漏检,并为智慧城市基础设施监控提供了可扩展的解决方案。该框架代表了SHM系统的重要进步,解决了大规模城市基础设施管理不断变化的需求。
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引用次数: 0
Dynamic Horizontal Displacement Evaluation Method of Tunnel Shield Tunnel Based on MSD Method for Basement Side Tunnels 基于MSD法的地下室边洞盾构隧道动态水平位移评价方法
IF 5.1 2区 工程技术 Q1 CONSTRUCTION & BUILDING TECHNOLOGY Pub Date : 2025-09-30 DOI: 10.1155/stc/5170617
Gang Wei, Zhiyuan Mu, Yitong Li, Yongjie Qi, Guohui Feng

The impact of pit excavation on the surrounding environment is closely related to the deformation characteristics of the surrounding enclosure structure. However, most existing methods rely on calculating pit unloading stress based on the Mindlin solution, which does not adequately account for the dynamic deformation characteristics of the enclosure structure at different excavation stages and is difficult to apply for real-time assessment. This paper presents a new calculation method based on the mobilizable strength design (MSD) approach to dynamically predict the horizontal displacement of the shield tunnel adjacent to the excavation pit. By introducing dynamic evaluation of the horizontal displacement of the enclosure structure, the applicability of the traditional MSD method is enhanced. The paper compares and analyzes the differences between this method, the modified MSD (MMSD) method, the MSD method, and measured data from actual pit excavation cases. The results demonstrate that the proposed method more accurately reflects the deformation characteristics of the enclosure structure at different excavation stages and its dynamic impact on the horizontal displacement of the shield tunnel. The spatial distribution of horizontal displacement in the enclosure structure under zoned excavation is analyzed, revealing the coupling relationship between the deformation characteristics of the enclosure structure and the tunnel’s deformation response. The findings of this study provide valuable references for the safety assessment and protective measures of shield tunnels during pit excavation.

基坑开挖对周边环境的影响与周边围护结构的变形特性密切相关。然而,现有的方法大多是基于Mindlin解计算基坑卸载应力,不能充分考虑围护结构在不同开挖阶段的动态变形特征,难以应用于实时评估。本文提出了一种基于可动强度设计(MSD)法的盾构隧道水平位移动态预测方法。通过引入围护结构水平位移的动态评估,提高了传统MSD方法的适用性。对比分析了该方法与改进的MSD (MMSD)法、MSD法以及实际基坑开挖实测数据的差异。结果表明,该方法更准确地反映了盾构隧道不同开挖阶段围护结构的变形特征及其对盾构隧道水平位移的动力影响。分析了分区开挖下围护结构水平位移的空间分布,揭示了围护结构变形特征与隧道变形响应之间的耦合关系。研究结果为盾构隧道基坑开挖时的安全评价和防护措施提供了有价值的参考。
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引用次数: 0
Coupling sPCA-Based Statistical Modeling With Deep Residual Networks Considering Thermal Effect for Deformation Forecasting in High Dams 考虑热效应的spca统计模型与深度残差网络耦合在高坝变形预报中的应用
IF 5.1 2区 工程技术 Q1 CONSTRUCTION & BUILDING TECHNOLOGY Pub Date : 2025-09-30 DOI: 10.1155/stc/6688960
Bo Liu, Fangfang Liu, Fei Song

Accurate prediction of deformation under thermal influences is critical for the safety assessment and long-term performance of high dams. This study proposes a novel two-stage prediction framework that integrates statistical modeling with deep learning to enhance the interpretability and accuracy of dam deformation forecasting. In the first stage, sparse principal component analysis (sPCA) is employed to extract dominant features from high-dimensional thermometer data. These features are then used to construct an interpretable dam deformation monitoring model using multiple linear regression (MLR), referred to as the HTsPCAT-MLR model. In the second stage, the multilayer bidirectional gated recurrent unit (multi-Bi-GRU) network is developed to model the residuals of the HTsPCAT-MLR framework, leveraging advanced gating mechanisms and bidirectional temporal learning to improve long-term prediction accuracy. Furthermore, the adaptive genetic algorithm (AGA) is utilized to optimize the hyperparameters of the multi-Bi-GRU model, enhancing the robustness and generalization of the residual correction module. The proposed methodology is validated using real-world monitoring data from an ultra-high arch dam. Quantitative evaluation at four representative measurement points shows that the proposed model consistently outperforms baseline methods across all key metrics. Specifically, it achieves R2 values above 0.99, mean absolute error reductions of over 80% compared to traditional models, and the lowest sMAPE across all cases. The experimental results demonstrate model’s superior prediction accuracy, robustness, and practical applicability for dam deformation. The integrated framework offers a reliable and interpretable solution for thermal deformation forecasting in high dam structures.

热影响下变形的准确预测对高坝的安全评价和长期运行至关重要。为了提高大坝变形预测的可解释性和准确性,本研究提出了一种将统计建模与深度学习相结合的两阶段预测框架。第一阶段,利用稀疏主成分分析(sPCA)从高维温度数据中提取优势特征;然后利用这些特征构建一个可解释的大坝变形监测模型,使用多元线性回归(MLR),称为HTsPCAT-MLR模型。在第二阶段,开发多层双向门控循环单元(multi-Bi-GRU)网络来模拟HTsPCAT-MLR框架的残差,利用先进的门控机制和双向时间学习来提高长期预测精度。利用自适应遗传算法(AGA)对多bi - gru模型的超参数进行优化,增强了残差校正模块的鲁棒性和泛化性。采用超高拱坝的实际监测数据对所提出的方法进行了验证。在四个具有代表性的测量点上的定量评估表明,所建议的模型在所有关键度量上始终优于基线方法。具体来说,它的R2值高于0.99,与传统模型相比,平均绝对误差降低了80%以上,并且在所有情况下的sMAPE最低。实验结果表明,该模型具有较好的预测精度、鲁棒性和实际应用价值。该综合框架为高坝结构热变形预报提供了可靠、可解释的解决方案。
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引用次数: 0
A Data-Driven Framework for Explainable Artificial Intelligence in Pavement Distress Analysis and Decision Support: Integrating Clustering Models and Principal Component Analysis 路面损伤分析与决策支持中可解释人工智能的数据驱动框架:整合聚类模型与主成分分析
IF 5.1 2区 工程技术 Q1 CONSTRUCTION & BUILDING TECHNOLOGY Pub Date : 2025-09-30 DOI: 10.1155/stc/8852297
Xiaogang Guo

The increasing complexity of transportation infrastructure demands advanced, data-driven approaches for early pavement distress detection and maintenance decision-making. Traditional assessment methods often fail to provide reliable, interpretable, and proactive insights into pavement degradation. This study introduces an Explainable Artificial Intelligence (XAI) framework that integrates clustering algorithms with principal component analysis (PCA) to improve early-stage pavement distress analysis. The proposed framework leverages K-means, Gaussian mixture models (GMMs), and hierarchical clustering, applied to a customized dataset encompassing pavement performance metrics, geospatial information, and aggregate properties. By incorporating ground-truth validation, our approach not only differentiates between high-quality and deteriorating pavement sections but also reveals underlying factors contributing to distress, overcoming the opacity of traditional machine learning (ML) models. Results demonstrate that this transparent, interpretable AI-driven framework enhances infrastructure resilience by enabling data-informed decision-making for predictive maintenance. Beyond transportation engineering, the methodology establishes a scalable paradigm for explainable AI applications in civil infrastructure, advancing the intersection of ML, geospatial analysis, and material science.

日益复杂的交通基础设施需要先进的、数据驱动的方法来进行早期路面损伤检测和维护决策。传统的评估方法往往不能提供可靠的、可解释的和前瞻性的路面退化的见解。本研究引入了一个可解释人工智能(XAI)框架,该框架将聚类算法与主成分分析(PCA)相结合,以改进早期路面破损分析。提出的框架利用K-means、高斯混合模型(gmm)和分层聚类,应用于包含路面性能指标、地理空间信息和聚合属性的定制数据集。通过结合地面真实性验证,我们的方法不仅区分了高质量和恶化的路面路段,还揭示了导致痛苦的潜在因素,克服了传统机器学习(ML)模型的不透明性。结果表明,这种透明、可解释的人工智能驱动框架通过为预测性维护提供数据知情决策,增强了基础设施的弹性。除了交通工程,该方法还为民用基础设施中的可解释人工智能应用建立了可扩展的范例,推动了机器学习、地理空间分析和材料科学的交叉。
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引用次数: 0
A Data-Driven Approach for Multirate Transitioning Between Complex and Nonlinear Substructures in Real-Time Hybrid Simulation 实时混合仿真中复杂和非线性子结构间多速率转换的数据驱动方法
IF 5.1 2区 工程技术 Q1 CONSTRUCTION & BUILDING TECHNOLOGY Pub Date : 2025-09-29 DOI: 10.1155/stc/5991335
Diego Mera, Gaston Fermandois, Fernando Gomez

This article proposes a new approach to interface both numerical and experimental substructures of a real-time hybrid simulation experiment running at different sampling rates. A regularized Wiener statistical finite impulse response filter is applied to the slow-rate sequence of interface target displacements at the numerical substructure to predict the next data point. Then, an interpolation rule using monomials is applied to obtain the sequence of interface target displacements at the experimental substructure running at a fast sampling rate. The Wiener filter is trained using offline simulations of the partitioned reference structure before the main experiment. The proposed scheme achieves good results for virtual simulations with linear and nonlinear structures, and it separates the task of determining simulation rates between substructures, ensuring both accuracy and stability in the experimental test.

本文提出了一种将不同采样率下的实时混合仿真实验的数值子结构和实验子结构连接起来的新方法。将正则维纳统计有限脉冲响应滤波器应用于数值子结构处的界面目标位移慢速序列,以预测下一个数据点。然后,应用单项式插值规则,得到实验子结构在快速采样速率下的界面目标位移序列。在主要实验之前,使用分割参考结构的离线模拟来训练维纳滤波器。该方案对线性和非线性结构的虚拟仿真均取得了较好的效果,并将确定仿真速率的任务与子结构分离,保证了实验测试的准确性和稳定性。
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Structural Control & Health Monitoring
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