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Three Optimization Methods for Preprocessing Dam Safety Monitoring Data Using Machine Learning
IF 4.6 2区 工程技术 Q1 CONSTRUCTION & BUILDING TECHNOLOGY Pub Date : 2024-12-23 DOI: 10.1155/stc/4385464
Zihan Jiang, Hao Gu, Yue Fang, Chenfei Shao, Xi Lu, Wenhan Cao, Jiayi Wang, Yan Wu, Mingyuan Zhu

The sensor-based dam health monitoring (DHM) systems of concrete-faced rockfill dam (CFRD) are easily affected by environmental factors, which inevitably causes sensor fault, and the measured value of its effect quantities is nonlinear and unstable. The application of machine learning in the preprocessing of dam safety monitoring data is very extensive, mainly including two parts: gross error elimination and missing data completion. In this paper, support vector regression (SVR), a typical machine learning algorithm, is chosen to accomplish these two tasks, while suggesting possible optimizations in different situations of hydraulic monitoring, including optimization of parameters in SVR using the population algorithm sparrow search algorithm (SSA); optimization of the pattern of gross error discriminant using the minimum covariance determinant (MCD) algorithm; and the hierarchical clustering on principal components (HCPC) algorithm to optimize the selection method of spatial measurement points when completing a segment of missing data. The results show that the optimized SVR method has greater accuracy in both gross error elimination and the completion of individual missing data or a segment of missing data for DHM systems, which is applicable to measured data of CFRD. These optimization methods can also be extended to other engineering applications.

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引用次数: 0
Generative Adversarial Networks for Improved Model Training in the Context of the Digital Twin 数字孪生环境下用于改进模型训练的生成对抗网络
IF 4.6 2区 工程技术 Q1 CONSTRUCTION & BUILDING TECHNOLOGY Pub Date : 2024-12-19 DOI: 10.1155/stc/9997872
María Megía, Francisco Javier Melero, Manuel Chiachío, Juan Chiachío

Digital twins (DTs) have revolutionised digitalisation practices across various domains, including the Architecture, Engineering, Construction and Operations (AECO) sector. However, DTs often face challenges related to data scarcity, especially in AECO, where tests are costly and difficult to scale. Historical data in this domain are often limited, unstructured and lack interoperability standards. Data scarcity directly affects the accuracy and reliability of the DT models and their decision-making capabilities. To address these challenges, classical methods are used to produce synthetic data based on predefined statistical distributions, which are barely scalable to unpredictable scenarios and prone to overfitting. Alternately, this work presents a novel comprehensive approach that covers every aspect from synthetic data generation to training and testing of these data on the system’s models. This strategy not only delivers high-quality data that meets the model’s requirements in terms of diversity, complexity and class balance, but also provides the diagnostic and prognostic capabilities of the DT of the system through its trained models. State-of-the-art techniques including generative adversarial networks (GANs), specifically Wasserstein generative adversarial networks with gradient penalty (WGAN-GP), and convolutional neural networks (CNNs) are employed in this novel pervasive approach, participating in the same architecture for generative, diagnostic and prognostic purposes. GANs enable data augmentation and reconstruction, while CNNs excel in spatial pattern recognition tasks. The proposed framework is demonstrated through an experimental case study on damage diagnostics and prognostics of a laboratory-scale metallic tower, where synthetic datasets are generated to supplement limited health monitoring data. The results showcase the effectiveness of the generated data for damage detection, prognostics and operational decision-making within the DT context. The presented method contributes to overcoming data scarcity challenges and improving the accuracy of DT models in the AECO sector. The article concludes with discussions on the application of the results and their implications for decision-making within the DT framework.

数字孪生(DTs)已经彻底改变了各个领域的数字化实践,包括建筑、工程、施工和运营(AECO)领域。然而,数字孪生往往面临着与数据稀缺有关的挑战,尤其是在 AECO 领域,测试成本高昂且难以扩展。该领域的历史数据通常有限、非结构化且缺乏互操作性标准。数据稀缺直接影响了 DT 模型的准确性和可靠性及其决策能力。为了应对这些挑战,传统的方法是根据预定义的统计分布生成合成数据,但这种方法几乎无法扩展到不可预测的场景,而且容易造成过度拟合。作为替代方案,这项工作提出了一种新颖的综合方法,涵盖了从合成数据生成到这些数据对系统模型的训练和测试等各个方面。这种策略不仅能提供高质量的数据,满足模型在多样性、复杂性和类别平衡方面的要求,还能通过训练有素的模型提供系统 DT 的诊断和预后能力。这种新颖的普适方法采用了最先进的技术,包括生成对抗网络(GANs),特别是具有梯度惩罚功能的瓦瑟斯坦生成对抗网络(WGAN-GP),以及卷积神经网络(CNNs),它们在同一架构中参与生成、诊断和预后目的。GAN 可以进行数据扩增和重建,而 CNN 则擅长空间模式识别任务。通过对实验室规模的金属塔进行损伤诊断和预报的实验案例研究,展示了所提出的框架,其中生成了合成数据集以补充有限的健康监测数据。结果表明,生成的数据在 DT 环境下的损坏检测、预后分析和运营决策方面非常有效。所介绍的方法有助于克服数据稀缺的挑战,并提高 AECO 部门 DT 模型的准确性。文章最后讨论了结果的应用及其对 DT 框架内决策的影响。
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引用次数: 0
A Combined Approach to Estimate Modal Parameters for Updating the Finite Element Model of a High-Rise Building 高层建筑有限元模型更新中模态参数估计的组合方法
IF 4.6 2区 工程技术 Q1 CONSTRUCTION & BUILDING TECHNOLOGY Pub Date : 2024-12-12 DOI: 10.1155/stc/3650202
Kang Cai, Mingfeng Huang, Chunhe Wang, Chen Yang, Yi-Qing Ni, Binbin Li

Accurate estimation of modal parameters is crucial for various aspects of tall buildings, including structural design, vibration control, and state assessment. This paper first presents a combined approach for the structural modal parameter estimation by combining the empirical wavelet transform (EWT), smoothed discrete energy separation algorithm-1 (SDESA-1), and half-cycle energy operator (HCEO), referred to as EWT-SH. A numerical study on a five-story frame structure is conducted using the Newmark-β method to validate its effectiveness and accuracy. The results demonstrate that relative errors in estimating the natural frequency and damping ratio using the EWT-SH method are significantly smaller compared to traditional methods. Furthermore, the EWT-SH method is applied to estimate the modal parameters of a real super-tall building, i.e., the SEG Plaza building in Shenzhen, using acceleration responses. Identified results confirm the applicability and accuracy of the EWT-SH method in real-world scenarios and indicate that the frequencies and damping ratios of the SEG Plaza building noticeably decrease after 20 years of service, which could partially explain the SEG building vibration event on May 18, 2021. Since the identified frequencies are quite different from those of the original finite element (FE) model of the tall building, the dual-loop particle swarm optimization (PSO) is specifically developed to update the FE model of SEG Plaza building.

模态参数的准确估计对于高层建筑的结构设计、振动控制和状态评估等各个方面都至关重要。本文首先提出了一种结合经验小波变换(EWT)、平滑离散能量分离算法-1 (SDESA-1)和半循环能量算子(HCEO)的结构模态参数估计方法,即EWT- sh。采用Newmark-β方法对某五层框架结构进行了数值研究,验证了该方法的有效性和准确性。结果表明,与传统方法相比,EWT-SH方法在估计固有频率和阻尼比方面的相对误差明显较小。在此基础上,应用EWT-SH方法,利用加速度响应估计了实际超高层建筑——深圳SEG广场的模态参数。研究结果证实了EWT-SH方法在实际场景中的适用性和准确性,表明SEG Plaza大厦在服役20年后的频率和阻尼比明显降低,这可以部分解释2021年5月18日SEG大厦的振动事件。由于识别频率与原有高层建筑有限元模型存在较大差异,针对SEG Plaza大厦的有限元模型,专门开发了双环粒子群优化算法(PSO)。
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引用次数: 0
Concise Analytic Solutions for Random Seismic Response of High-Rise Structure With Series-Parallel Inerter System and Tuned Mass Damper 串联-并联惯性系统和调谐质量阻尼器高层结构随机地震反应的简明解析解
IF 4.6 2区 工程技术 Q1 CONSTRUCTION & BUILDING TECHNOLOGY Pub Date : 2024-12-04 DOI: 10.1155/stc/1806600
Lin Deng, Xinguang Ge, Junbo Wang, Hui He

Tuned mass damper (TMD) is a single-terminal damper, while inerter is a two-terminal one, which are effective control devices. So, a hybrid damper with a series-parallel inerter system and a TMD (SPIS-TMD) in series is proposed. The main work of the manuscript is as follows. Firstly, based on the mechanical diagram of SPIS-TMD and its equipment on the roof of a high-rise structure, the general form of seismic motion equation was derived using dynamic finite element technology. Secondly, based on the method of quadratic decomposition for power spectrum density function (QD-PSDF), the concise analytic solutions for zero-, first-, and second-order response spectral moments (ZFSO-RSMs) of the general response of structure with SPIS-TMD were deduced. Thirdly, in response to the numerous parameters that affect the safety of high-rise structures and the varying difficulty for obtaining SPIS-TMD’s parameters, an optimization analysis technique was proposed, which is constrained by dynamic reliability and SPIS-TMD’s parameter weights. Finally, three examples were given and results show the following. (1) The proposed analytical solutions for ZFSO-RSMs are correct and high efficiency and can be extended to analysis of stationary seismic responses of general linear structures. (2) Calculating results of ZFSO-RSMs of general responses with the number of vibration modes corresponding to a participation weight of 100% is exactly the same as those with all vibration modes, and the calculating time of the case of the participation weight of 100% is less than 1/12 that of the case of all vibration modes. (3) The failure probabilities of the structure with SPIS-TMD with optimal parameters using the proposed method (with a limit failure probability of 0.1587), the structure only equipped with TMD with the same TMD’s parameters as SPIS-TMD, and the structure without dampers are 0.1570, 0.7060, and 0.9778, respectively. It indicates that under the same conditions, the hybrid damper SPIS-TMD has a better damping effect than a single TMD.

调谐质量阻尼器是一种单端阻尼器,而调谐质量阻尼器是一种双端阻尼器,是一种有效的控制装置。为此,提出了一种由串并联干涉器系统和TMD串联构成的混合阻尼器(SPIS-TMD)。手稿的主要工作如下。首先,基于SPIS-TMD及其设备在高层结构屋顶上的受力图,利用动力有限元技术推导了地震运动方程的一般形式;其次,基于功率谱密度函数(QD-PSDF)的二次分解方法,推导了SPIS-TMD结构一般响应的零阶、一阶和二阶响应谱矩(ZFSO-RSMs)的简明解析解。第三,针对影响高层结构安全的参数众多、SPIS-TMD参数获取难度不一的特点,提出了一种以动力可靠性和SPIS-TMD参数权值为约束的优化分析方法。最后给出了三个算例,结果表明:(1)提出的zfso - rsm解析解正确、高效,可推广到一般线性结构的平稳地震反应分析。(2)参与权为100%时振型数对应的一般响应的zfso - rsm计算结果与所有振型完全相同,且参与权为100%时的计算时间小于所有振型情况的1/12。(3)采用本文方法得到参数最优的SPIS-TMD结构的失效概率(极限失效概率为0.1587)、仅配置与SPIS-TMD参数相同的TMD结构的失效概率为0.1570、0.7060和0.9778。结果表明,在相同条件下,SPIS-TMD混合阻尼器比单一TMD阻尼器具有更好的阻尼效果。
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引用次数: 0
Effect of Cracks on the Influence Lines of a Smart Concrete Girder Bridge Based on the Element Size–Independent FE Model 基于单元尺寸无关有限元模型的智能混凝土梁桥裂缝影响线研究
IF 4.6 2区 工程技术 Q1 CONSTRUCTION & BUILDING TECHNOLOGY Pub Date : 2024-11-30 DOI: 10.1155/stc/9980733
Zhiwei Chen, Yu Shi, Jianfeng Chen, Yao Zhang

A smart concrete girder bridge usually has various sensors, based on which several physical properties can be measured, and hence, the health condition can be evaluated. Cracks are always observed on a smart concrete girder bridge. In particular, some of the cracks are induced by overloaded vehicles, which is dangerous to its safe operation. However, due to the crack opening and closing effect, it exhibits nonlinear responses, posing challenges for accurately assessing its health condition. Influence lines (ILs) are a promising indicator for bridge damage. However, there is limited research on the effect of cracks on the ILs of a smart concrete girder bridge. A digital twin is commonly used to accompany the smart sensing system to accurately evaluate the health condition, where the finite element (FE) model is of great importance. Therefore, this study proposes an element size–independent FE model construction method based on the concrete damage plasticity (CDP) model to investigate the changes of displacement and strain ILs of different types of smart concrete girder bridges with bending cracks, which is helpful to guide how to use the ILs to identify the cracks and evaluate the health condition. Initially, a concrete constitutive model based on crushing/fracture energy is proposed, and the evolution law of tensile damage based on fracture energy is derived to construct the element size–independent FE model. Subsequently, experiments on a reinforced concrete (RC) simply supported beam and a prestressed concrete (PC) simply supported bridge subjected to bending failure are used to verify the FE models constructed by the proposed method. Finally, the FE models of a smart RC T-beam bridge and a smart three-span PC continuous bridge are established to study the changes in ILs caused by bending cracks. The change of displacement IL at the midspan due to cracks for the smart RC bridge exceeds 10% when the reinforcements yield, while it is less than 10% for the smart PC bridge even if the bridge is in the failure state. The change of both displacement and strain ILs becomes greater when the measurement point approaches the cracks, and the change of strain IL is only detectable when the measurement is close to the cracks. Due to the crack opening and closing effect, the displacement and strain ILs of a smart concrete girder bridge with bending cracks are inconsistent when different loads are applied. The findings can also be used as a pre-IL-based crack detection using the passing inspection vehicle-induced dynamic response on a selection of type of ILs, determination of layout of sensors, and mass of inspection vehicle.

智能混凝土梁桥通常具有多种传感器,基于这些传感器可以测量几种物理特性,从而可以评估其健康状况。智能混凝土梁桥经常出现裂缝。特别是,有些裂缝是由超载车辆引起的,这对其安全运行是危险的。然而,由于裂缝的开闭效应,它表现出非线性响应,给准确评估其健康状况带来了挑战。影响线是一种很有前途的桥梁损伤指标。然而,裂缝对智能混凝土梁桥ILs的影响研究较少。智能传感系统通常使用数字孪生模型来准确评估健康状况,其中有限元模型非常重要。因此,本研究提出了一种基于混凝土损伤塑性(CDP)模型的单元尺寸无关有限元模型构建方法,研究不同类型智能混凝土梁桥弯曲裂缝的位移和应变ILs变化,有助于指导如何利用ILs识别裂缝和评估健康状况。首先,提出了基于破碎/断裂能的混凝土本构模型,推导了基于断裂能的拉伸损伤演化规律,构建了与单元尺寸无关的有限元模型。随后,通过钢筋混凝土简支梁和预应力混凝土简支桥的弯曲破坏试验,对所建立的有限元模型进行了验证。最后,建立了智能型钢筋混凝土t梁桥和智能型三跨PC连续梁桥的有限元模型,研究了弯曲裂缝引起的ILs变化。钢筋屈服时,智能RC桥跨中裂缝位移IL的变化超过10%,而智能PC桥即使处于破坏状态,跨中裂缝位移IL的变化也小于10%。当测点靠近裂纹时,位移和应变IL的变化都变大,应变IL的变化只有在测点靠近裂纹时才能检测到。由于裂缝的开闭效应,具有弯曲裂缝的智能混凝土梁桥在不同荷载作用下的位移和应变ls是不一致的。研究结果还可以用于基于il的预裂纹检测,使用通过检测车辆对il类型的选择,传感器布局的确定和检测车辆质量的动态响应。
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引用次数: 0
A Robust Displacement Monitoring Model for High-Arch Dams Integrating Signal Dimensionality Reduction and Deep Learning-Based Residual Correction 基于信号降维和深度学习残差校正的高拱坝鲁棒位移监测模型
IF 4.6 2区 工程技术 Q1 CONSTRUCTION & BUILDING TECHNOLOGY Pub Date : 2024-11-28 DOI: 10.1155/stc/3330769
Yantao Zhu, Xinqiang Niu, Tianyou Yan, Lifu Xu

Deformation is a critical indicator for the safety control of high-arch dams, yet traditional statistical regression methods often exhibit poor predictive performance when applied to long-sequence time series data. In this study, we develop a robust predictive model for deformation behavior in high-arch dams by integrating signal dimensionality reduction with deep learning (DL)-based residual correction techniques. First, the fast Fourier transform is employed to decompose air and water temperature sequences, enabling the extraction of temperature cycle characteristics at the dam boundary. A data-driven statistical monitoring model for dam deformation, based on actual temperature data, is then proposed. Subsequently, an improved Bayesian Ridge regression model is used to construct the dam deformation monitoring framework. The residuals that traditional statistical methods fail to capture are input into an enhanced Long Short-Term Memory (LSTM) network to effectively learn the temporal characteristics of the sequence. A high-arch dam with a history of long-term service is used as a case study. Experimental results indicate that the data dimensionality reduction method effectively extracts relevant information from observed temperature data, reducing the number of input variables. Comparative evaluation experiments show that the proposed hybrid predictive model outperforms existing state-of-the-art benchmark algorithms in terms of predictive efficiency and accuracy. Additionally, this approach combines the interpretability of statistical regression methods with the powerful nonlinear modeling capabilities of DL-based models, achieving a synergistic effect.

变形是高拱坝安全控制的重要指标,但传统的统计回归方法在处理长序列时间序列数据时往往表现出较差的预测性能。在这项研究中,我们通过将信号降维与基于深度学习(DL)的残差校正技术相结合,开发了高拱坝变形行为的鲁棒预测模型。首先,利用快速傅里叶变换对空气和水温序列进行分解,提取大坝边界温度循环特征;提出了一种基于实际温度数据的数据驱动的大坝变形统计监测模型。随后,采用改进的贝叶斯岭回归模型构建大坝变形监测框架。将传统统计方法无法捕获的残差输入到增强型长短期记忆(LSTM)网络中,以有效地学习序列的时间特征。以具有长期使用历史的高拱坝为例进行了研究。实验结果表明,数据降维方法有效地从观测温度数据中提取相关信息,减少了输入变量的数量。对比评估实验表明,所提出的混合预测模型在预测效率和准确性方面优于现有的最先进的基准算法。此外,该方法将统计回归方法的可解释性与基于dl的模型强大的非线性建模能力相结合,实现了协同效应。
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引用次数: 0
Evaluation Method for Bearing Capacity of Fine-Grained Soil Subgrade Based on Multiple Moduli 基于多模量的细粒土路基承载力评估方法
IF 4.6 2区 工程技术 Q1 CONSTRUCTION & BUILDING TECHNOLOGY Pub Date : 2024-11-25 DOI: 10.1155/stc/7735960
Danfeng Li, Weichao Liu, Guangming Zhang

The bearing capacity of the existing fine-grained soil subgrade is mainly achieved by measuring the modulus, and its testing methods can be divided into two categories including static method and dynamic method. The data connection between the two is still lacking in systematic research. The traditional BB (Benkelman Beam) static test has disadvantages such as slow detection speed, low accuracy, and poor reliability due to the use of deflection index, which is not fully applicable to the expressway. To this end, dynamic and static comparison tests were carried out and a rapid test method for subgrade bearing capacity based on Soil Stiffness Gauge (SSG) and Portable Falling Weight Deflectometer (PFWD) with dynamic modulus was proposed. To establish the connectivity of the static and dynamic modulus, modulus-dependent prediction model was developed. The results show that the multiplier power model with modulus () is superior to the usual linear model (EBB = 0.9415EPFWD − 6.1507, R2 = 0.9639; EBB = 0.7878ESSG − 0.4566,  R2 = 0.8894), and it can replace the traditional BB method. PFWD and SSG were found to be reliable devices with faster and more accurate monitoring of the modulus change of the fine-grained soil subgrade. But they have the property of overestimating material modulus, the modulus ranking of the three instruments is obtained. In this way, it provides a reference for the dynamic and accurate determination and scientific evaluation of the bearing capacity of the highway subgrade.

现有细粒土路基的承载力主要是通过测量模量来实现的,其测试方法可分为静力法和动力法两类。二者之间的数据联系还缺乏系统的研究。传统的 BB(Benkelman Beam)静态试验由于使用挠度指标,存在检测速度慢、精度低、可靠性差等缺点,并不完全适用于高速公路。为此,开展了动静态对比试验,并提出了基于土体刚度仪(SSG)和便携式落锤式挠度仪(PFWD)动态模量的路基承载力快速测试方法。为了建立静态模量和动态模量之间的联系,建立了模量相关预测模型。结果表明,带模量()的乘数功率模型优于通常的线性模型(EBB = 0.9415EPFWD - 6.1507,R2 = 0.9639;EBB = 0.7878ESSG - 0.4566,R2 = 0.8894),可以取代传统的 BB 方法。研究发现,PFWD 和 SSG 是可靠的设备,能更快、更准确地监测细粒土路基的模量变化。但它们都有高估材料模量的特性,因此得出了三种仪器的模量排序。从而为公路路基承载力的动态准确测定和科学评价提供参考。
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引用次数: 0
Deflection Prediction of a Rail-Cum-Road Suspension Bridge Under Multiple Operational Loads With Improved GPR and FSF 利用改进型 GPR 和 FSF 对多重运行荷载下的铁路-公路悬索桥进行挠度预测
IF 4.6 2区 工程技术 Q1 CONSTRUCTION & BUILDING TECHNOLOGY Pub Date : 2024-11-25 DOI: 10.1155/stc/8880157
Xingwang Liu, Zhen Sun, Tong Guo

The deformation of the main girder is an important manifestation of the overall stiffness of suspension bridges, which is essential for assessing bridge performance. Nevertheless, it is difficult to achieve satisfied prediction without fully considering the overall operational loads. To this end, this paper proposes a method to predict the deflection considering multiple operational loads using the monitoring data of a high-speed rail-cum-road suspension bridge. Initially, an improved Gaussian process regression (GPR) model utilizing Bayesian optimization was employed to predict the deformation of the main girder under the condition of nontrain loads. Furthermore, the distinct contributions of temperature, wind, and vehicle load were analyzed. Subsequently, based on the strain and deflection induced by train loads, the sum of sinusoids method was proposed to construct fitting and shape function (FSF) for predicting the main girder deformation under the influence of train loads. Ultimately, the deformation considering overall loads was obtained by adding the deformation under the nontrain and train loads, and the predicted deformation result was verified using the measured data. When compared to other state-of-the-art machine learning algorithms, namely, artificial neural network (ANN), support vector machine (SVM), and decision tree (DT), the improved GPR demonstrates the highest accuracy in predicting the deformation of the main girder under nontrain loads with R2 of 0.9478. In addition, the proposed sum of sinusoids FSF method accurately predicted the deformation of the main girder caused by train loads, with R2 of 0.934. The deformation of the main girder under the influence of overall loads can lay a foundation for the early warning and evaluation of the suspension bridges.

主梁变形是悬索桥整体刚度的重要体现,对于评估桥梁性能至关重要。然而,如果不充分考虑整体运行荷载,就很难获得满意的预测结果。为此,本文利用高速铁路兼公路悬索桥的监测数据,提出了一种考虑多种运行荷载的挠度预测方法。首先,利用贝叶斯优化的改进型高斯过程回归(GPR)模型来预测非列车荷载条件下主梁的变形。此外,还分析了温度、风力和车辆荷载的不同贡献。随后,根据列车荷载引起的应变和挠度,提出了正弦和方法来构建拟合和形状函数(FSF),用于预测列车荷载影响下的主梁变形。最终,通过将非列车荷载和列车荷载下的变形相加,得到了考虑整体荷载的变形,并利用测量数据验证了预测的变形结果。与其他最先进的机器学习算法(即人工神经网络 (ANN)、支持向量机 (SVM) 和决策树 (DT))相比,改进后的 GPR 在预测非列车载荷下主梁的变形方面具有最高的准确性,R2 为 0.9478。此外,提出的正弦之和 FSF 方法准确预测了列车荷载引起的主梁变形,R2 为 0.934。整体荷载影响下的主梁变形可为悬索桥的预警和评估奠定基础。
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引用次数: 0
3D Laser Scanning-Based Tension Assessment for Bridge Cables Considering Point Cloud Density 基于 3D 激光扫描的桥梁电缆张力评估(考虑点云密度
IF 4.6 2区 工程技术 Q1 CONSTRUCTION & BUILDING TECHNOLOGY Pub Date : 2024-11-19 DOI: 10.1155/stc/8094924
Chengyin Liu, Cheng Yan, Sheng Yu, Jinping Ou, Jiaming Chen

To address the limitations in accuracy, reliability, and efficiency of traditional cable tension measurement methods, this paper proposes a cable tension assessment method based on 3D laser scanning technology that considers point cloud density. This study first employed a point cloud plane projection algorithm to reduce a 3D point cloud model to a 2D plane, fitting the actual cable shape by considering point cloud density. Subsequently, the parabolic and catenary cable mechanics models were derived to characterize the relationship between cable tension and shape based on force analysis of cable segments and differential segments. The Broyden–Fletcher–Goldfarb–Shanno (BFGS) algorithm was applied to calculate cable tensions using the measured cable shape and the mechanic’s models, and the proposed cable tension assessment method was validated using practical cable point cloud models. Finally, the cable tension assessment method was applied to a specific sea-crossing bridge and compared with the traditional frequency method. The results indicated that the 3D laser scanning cable tension assessment method, considering point cloud density, could quickly and accurately identify cable tensions, offering greater accuracy, reliability, and efficiency compared to the traditional frequency method.

针对传统电缆张力测量方法在精度、可靠性和效率方面的局限性,本文提出了一种基于三维激光扫描技术、考虑点云密度的电缆张力评估方法。本研究首先采用点云平面投影算法将三维点云模型还原为二维平面,通过考虑点云密度来拟合实际的电缆形状。随后,根据对缆索分段和差分段的受力分析,推导出抛物线缆索力学模型和导管缆索力学模型,以描述缆索拉力与形状之间的关系。应用 Broyden-Fletcher-Goldfarb-Shanno (BFGS) 算法,利用测得的缆索形状和力学模型计算缆索张力,并利用实际缆索点云模型验证了所提出的缆索张力评估方法。最后,将缆索张力评估方法应用于一座特定的跨海大桥,并与传统的频率法进行了比较。结果表明,三维激光扫描缆索张力评估方法考虑了点云密度,能够快速准确地识别缆索张力,与传统的频率法相比,具有更高的准确性、可靠性和效率。
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引用次数: 0
Damage Identification in Large-Scale Bridge Girders Using Output-Only Modal Flexibility–Based Deflections and Span-Similar Virtual Beam Models 使用基于输出模态柔性的挠度和跨度相似的虚拟梁模型识别大型桥梁梁的损伤
IF 4.6 2区 工程技术 Q1 CONSTRUCTION & BUILDING TECHNOLOGY Pub Date : 2024-11-16 DOI: 10.1155/2024/4087831
N. T. Le, A. Nguyen, T. H. T. Chan, D. P. Thambiratnam

Damage identification (DI) methods using changes in static and modal flexibility (MF)–based deflections are effective tools to assess the damage in beam-like structures due to the explicit relationships between deflection change and stiffness reduction caused by damage. However, current methods developed for statically determinate beams require the calculation of mathematical scalar functions which do not exist in statically indeterminate beams and limit their application mainly to single-span bridges and cantilever structures. This paper presents an enhanced deflection-based damage identification (DBDI) method that can be applied to both statically determinate and indeterminate beams, including multispan girder bridges. The proposed method utilises the deflections obtained either from static tests or proportional defections extracted from output-only vibration tests. Specifically, general mathematical relationships between deflection change and relative deflection change with respect to the damage characteristics are established. From these, additional damage-locating criteria are proposed to help distinguish undamaged spans from the damaged ones and to identify the damage location within the damaged span. Notably, a span-similar virtual beam (SSVB) model concept is introduced to quantify the damage and make this task straightforward without the need to calculate complicated mathematical formulae. This model only requires information of the beam span length, which can be conveniently and accurately obtained from a real structure. The robustness of the method is tested through a series of case studies from a numerical two-span beam to a benchmark real slab-on-girder bridge as well as a complex large-scale box girder bridge (BGB). The results of these studies, including the minimal verification errors within five percent observed in the real bridge scenario, demonstrate that the proposed method is robust and can serve as a practical tool for structural health monitoring (SHM) of important highway bridges.

基于静态和模态柔度(MF)挠度变化的损伤识别(DI)方法是评估类梁结构损伤的有效工具,因为损伤导致的挠度变化和刚度降低之间存在明确的关系。然而,目前针对静定梁开发的方法需要计算数学标量函数,而这些函数在静不定梁中并不存在,这就限制了这些方法主要在单跨桥梁和悬臂结构中的应用。本文提出了一种增强的基于挠度的损伤识别(DBDI)方法,可同时应用于定常梁和不定常梁,包括多跨梁桥。该方法利用从静力试验中获得的挠度或从纯输出振动试验中提取的比例缺陷。具体来说,建立了挠度变化和相对挠度变化与损伤特征之间的一般数学关系。在此基础上,提出了更多的损坏定位标准,以帮助区分未损坏的跨度和损坏的跨度,并确定损坏跨度内的损坏位置。值得注意的是,引入了跨度相似虚拟梁(SSVB)模型概念来量化损伤,使这项任务变得简单明了,无需计算复杂的数学公式。该模型只需要梁跨度的信息,而这些信息可以从实际结构中方便、准确地获得。该方法的稳健性通过一系列案例研究进行了测试,从数值双跨梁到基准实际梁板桥以及复杂的大型箱梁桥(BGB)。这些研究的结果,包括在实际桥梁场景中观察到的 5% 以内的最小验证误差,证明了所提出的方法是稳健的,可以作为重要公路桥梁结构健康监测 (SHM) 的实用工具。
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引用次数: 0
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Structural Control & Health Monitoring
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