考虑残差学习的深层卷积神经网络在结构损伤识别中的应用研究

提出了一种考虑残差学习的深层卷积神经网络损伤识别方法,并将其应用到框架结构节点损伤识别中。采用试验研究方式对所提方法进行了深入探讨,结果表明该方法可以很好地解决网络深化带来的网络退化或梯度爆炸、弥散导致的收敛困难和识别准确率差等问题,能对结构损伤诊断中的损伤定位这一复杂问题进行有效识别。在对试验框架节点损伤位置识别的对比研究中,考虑残差学习的深层卷积神经网络收敛速度和准确率均高于常规浅层神经网络和深层神经网络,有极高的准确率和稳定性,从而使得对于工程中复杂结构损伤诊断所需要的更深层、更复杂网络的搭建成为可能。此外,为提升网络用训练样本的质量和数量,依据样本划分规律提出了一种新的数据样本扩增方法,该方法在相同条件下可以显著增加用以训练的样本量并能弱化数据截断带来的信息缺失,识别准确率和收敛速度也大幅提高,研究显示了该处理方式的有效性和适用性。     

基于卷积神经网络的结构损伤识别研究进展

为系统梳理基于卷积神经网络的工程结构损伤识别方法的发展脉络和研究现状,分别从结构损伤的识别目的和在不同类型结构中的应用两方面进行了归类、分析和评价。介绍了卷积神经网络的基本结构和评价指标,回顾了卷积神经网络的研究和应用历程。在损伤的识别目的方面,主要针对混凝土结构损伤的分类、定位和分割,详细介绍了基于不同类型卷积神经网络的结构损伤识别方法,即基于分类的方法、基于回归的方法和像素级的图像分割算法; 分析了各类方法所使用的卷积神经网络模型的结构特点、计算流程、训练方法和损伤识别性能。在不同类型结构的损伤识别方面,分析了卷积神经网络在砌体结构、钢结构桥梁和古建筑木结构裂缝识别中的应用。最后,基于对卷积神经网络优缺点的思考,提出了发展建议和展望。结果表明:训练样本中结构损伤的多样性对模型的损伤识别效果影响较大; 现有基于卷积神经网络的损伤分割方法模型参数较多,计算量大; 采用数据增广和迁移学习方法可有效防止模型过拟合,提高模型训练效率; 针对微小损伤和不同类型结构损伤的识别,此类方法的性能有待提高。     

基于谱单元方法的杆件导波数值模拟与损伤检测的时频分析

相比于传统的有限元方法,谱单元法是一种基于FFT算法的频域数值方法,它能够大大降低求解问题的阶数,在模拟高频导波在结构中传播、捕捉结构微小损伤方面尤为高效。以弯曲弹簧模型作为裂纹的数学模型,建立了裂纹杆件谱单元模型,求解高频导波激励下裂纹杆件的波动解。以短时傅里叶变换与Choi-Williams两种时频方法分析裂纹杆件的导波信号的能量时频分布。结果表明:短时傅里叶谱与ChoiWilliams谱均能够准确反映导波信号的时频分布特点,可以实现结构微小损伤的定位与定量分析;短时傅里叶变换的时域分辨率比Choi-Williams法低,但是其能量分布在时域上与导波信号的时域波形吻合更好;随着裂纹的加深,这两种时频分析方法对频域的分辨率更高。     

卷积神经网络在结构损伤检测中的应用

基于卷积神经网络(CNN)在损伤特征提取上的优势,本文开展了结构在不同激励类型作用下CNN损伤诊断精度的研究。通过有限元模拟试验,发现作用在结构上的激励类型不是影响CNN损伤识别精度的关键因素。同时,在测试CNN的抗噪能力并分析其特征提取的鲁棒性时,发现在不同噪声强度下CNN均取得了良好的识别效果。混合训练模式虽然对CNN识别精度有略微的影响,但却可以在一定程度上增强其识别稳定性。另外,在数值模拟试验中发现瞬态响应中包含了比稳态响应更为广阔的损伤特征,在输入数据中添加结构瞬态响应能够在一定程度上加快训练速率。     

基于时频分析的钢筋混凝土梁损伤识别方法研究

利用短时傅里叶变换方法对损伤前、后的钢筋混凝土试验梁振动响应数据进行处理及分析。对实验数据处理的结果表明,短时傅里叶变换方法能够有效地识别不同荷载状态下的钢筋混凝土梁响应数据时频谱中的能量变化,验证了该方法在结构损伤识别中的有效性。     

利用小波分析方法识别结构模态参数

在高层建筑抗震、抗风、健康监测及损伤诊断等研究中,结构模态参数是非常重要的参数之一。介绍了环境激励下基于小波分析的模态参数识别方法,并对实际结构振动响应信号进行小波分析。有效地识别了结构的固有模态参数,并同传统傅里叶变换、短时傅里叶变换的结构相比较,证实了小波分析方法在处理随机信号方面的优越性。另外,使用小波分析方法对测试信号进行降噪处理。分析结果表明,小波方法降噪能有效抑制噪音,还原真实信号。     

城市燃气管道泄漏诊断方法

为了提高管道泄漏诊断的正确率和稳定性,提出一种将卷积神经网络用于管道泄漏诊断领域,并与softmax分类器结合进行管道泄漏诊断的方法。选取合适的声发射特征参数,进行声发射信号的提取,用时频和DFT处理将得到的声发射传感器信号转换为时频图和频谱图,将原始信号、时频图和频谱图输入卷积神经网络进行分类诊断。通过分析比较诊断结果可知,频谱图作为输入信息时CNN模型正确率高达99.5%,高于其他两种样本及BP神经网络的管道泄漏诊断率。     

基于传递率函数的结构损伤识别研究

基于动力特性的结构损伤识别方法通常需要已知激励信息,而激励信息一般难以准确测得。为此,直接对加速度响应进行傅里叶变换并计算相邻两点的传递率函数,利用传递率函数构造相应的损伤指标,通过该指标来识别结构的损伤位置,这种方法不需要已知激励信息,不需要进行模态分析。海洋平台结构的数值模拟和振动台试验表明,该方法是可行的,且具有一定的抗噪声能力。     

延拓小波变换识别的桥梁模态参数研究

振动系统模态识别是当今桥梁结构动力特性研究的热点之一。从复模态理论的一般阻尼系统的模态参数分析入手,利用径向神经网络插值技术,对含有噪声的振动信号进行信号预测延拓降噪处理,借助连续的Morlet小波变换,识别出了振动结构系统的模态。以重庆大佛寺长江大桥为研究背景,使用模态叠加法和Morlet小波分析识别结构,二者吻合程度较高。研究结果表明,基于径向神经网络的延拓预测的信号降噪效果好;Morlet小波变换识别模态参数精度满足工程要求。     

基于短时傅里叶变换的快速贝叶斯模态参数识别方法

为反映结构非平稳响应信号的时频特性，提出基于短时傅里叶变换的快速贝叶斯模态参数识别(FBST)方法。该方法采用短时傅里叶变换代替傅里叶变换进行模态参数识别，使模态参数的识别同时具有时频特性，同时能够给出识别结果的不确定性。利用时域分解解耦技术，将多自由度多模态响应信号转变为单自由度单模态响应信号以提升计算效率，推导得到高信噪比下负对数似然函数的表达式。采用数值算例验证了FBST方法在时变频率和阻尼比识别上的有效性。在此基础上，针对某大跨柔性光伏支架结构气弹模型的风洞试验数据和某高层建筑风振实测加速度响应数据，利用FBST方法识别了对应结构的阻尼比、频率，并与连续小波变换和Hilbert-Huang变换等经典方法的识别结果进行对比。数值算例分析结果显示，对于时变、非时变信号，FBST方法均能识别与理论值较为一致的阻尼比和频率结果。对于大跨柔性光伏支架结构的气弹试验数据和高层建筑实测加速度响应，FBST方法识别得到的频率结果与连续小波变换以及Hilbert-Huang的结果较为一致，而识别出的阻尼比存在较大变异系数。     

Slope stability analysis based on big data and convolutional neural network

The Limit Equilibrium Method (LEM) is commonly used in traditional slope stability analyses, but it is time-consuming and complicated. Due to its complexity and nonlinearity involved in the evaluation process, it cannot provide a quick stability estimation when facing a large number of slopes. In this case, the convolutional neural network (CNN) provides a better alternative. A CNN model can process data quickly and complete a large amount of data analysis in a specific situation, while it needs a large number of training samples. It is difficult to get enough slope data samples in practical engineering. This study proposes a slope database generation method based on the LEM. Samples were amplified from 40 typical slopes, and a sample database consisting of 20000 slope samples was established. The sample database for slopes covered a wide range of slope geometries and soil layers’ physical and mechanical properties. The CNN trained with this sample database was then applied to the stability prediction of 15 real slopes to test the accuracy of the CNN model. The results show that the slope stability prediction method based on the CNN does not need complex calculation but only needs to provide the slope coordinate information and physical and mechanical parameters of the soil layers, and it can quickly obtain the safety factor and stability state of the slopes. Moreover, the prediction accuracy of the CNN trained by the sample database for slope stability analysis reaches more than 99%, and the comparisons with the BP neural network show that the CNN has significant superiority in slope stability evaluation. Therefore, the CNN can predict the safety factor of real slopes. In particular, the combination of typical actual slopes and generated slope data provides enough training and testing samples for the CNN, which improves the prediction speed and practicability of the CNN-based evaluation method in engineering practice.     

Structural health monitoring using extremely compressed data through deep learning

This study introduces a novel convolutional neural network (CNN)‐based approach for structural health monitoring (SHM) that exploits a form of measured compressed response data through transfer learning (TL)‐based techniques. The implementation of the proposed methodology allows damage identification and localization within a realistic large‐scale system. To validate the proposed method, first, a well‐known benchmark model is numerically simulated. Using acceleration response histories, as well as compressed response data in terms of discrete histograms, CNN models are trained, and the robustness of the CNN architectures is evaluated. Finally, pretrained CNNs are fine‐tuned to be adaptable for three‐parameter, extremely compressed response data, based on the response mean, standard deviation, and a scale factor. The performance of each CNN implementation is assessed using training accuracy histories as well as confusion matrices, along with other performance metrics. In addition to the numerical study, the performance of the proposed method is demonstrated using experimental vibration response data for verification and validation. The results indicate that deep TL can be implemented effectively for SHM of similar structural systems with different types of sensors.     

Autonomous damage segmentation and measurement of glazed tiles in historic buildings via deep learning

Detecting and measuring the damage on historic glazed tiles plays an important role in the maintenance and protection of historic buildings. However, the current visual inspection method for identifying and assessing superficial damage on historic buildings is time and labor intensive. In this article, a novel two‐level object detection, segmentation, and measurement strategy for large‐scale structures based on a deep‐learning technique is proposed. The data in this study are from the roof images of the Palace Museum in China. The first level of the model, which is based on the Faster region‐based convolutional neural network (Faster R‐CNN), automatically detects and crops two types of glazed tile photographs from 100 roof images (2,488 × 3,264 pixels). The average precision values (AP) for roll roofing and pan tiles are 0.910 and 0.890, respectively. The cropped images are used to form a dataset for training a Mask R‐CNN model. The second level of the model, which is based on Mask R‐CNN, automatically segments and measures the damage based on the cropped historic tile images; the AP for the damage segmentation is 0.975. Based on Mask R‐CNN, the predicted pixel‐level damage segmentation result is used to quantitatively measure the morphological features of the damage, such as the damage topology, area, and ratio. To verify the performance of the proposed method, a comparative study was conducted with Mask R‐CNN and a fully convolutional network. This is the first attempt at employing a two‐level strategy to automatically detect, segment, and measure large‐scale superficial damage on historic buildings based on deep learning, and it achieved good results.     

Vibration‐based structural state identification by a 1‐dimensional convolutional neural network

Deep learning has ushered in many breakthroughs in vision‐based detection via convolutional neural networks (CNNs), but the vibration‐based structural damage detection by CNN remains being refined. Thus, this study proposes a simple one‐dimensional CNN that detects tiny local structural stiffness and mass changes, and validates the proposed CNN on actual structures. Three independent acceleration databases are established based on a T‐shaped steel beam, a short steel girder bridge (in test field), and a long steel girder bridge (in service). The raw acceleration data are not pre‐processed and are directly used as the training and validation data. The well‐trained CNN almost perfectly identifies the locations of small local changes in the structural mass and stiffness, demonstrating the high sensitivity of the proposed simple CNN to tiny structural state changes in actual structures. The convolutional kernels and outputs of the convolutional and max pooling layers are visualized and discussed as well.     

Tunnel boring machine vibration-based deep learning for the ground identification of working faces

Tunnel boring machine (TBM) vibration induced by cutting complex ground contains essential information that can help engineers evaluate the interaction between a cutterhead and the ground itself. In this study, deep recurrent neural networks (RNNs) and convolutional neural networks (CNNs) were used for vibration-based working face ground identification. First, field monitoring was conducted to obtain the TBM vibration data when tunneling in changing geological conditions, including mixed-face, homogeneous, and transmission ground. Next, RNNs and CNNs were utilized to develop vibration-based prediction models, which were then validated using the testing dataset. The accuracy of the long short-term memory (LSTM) and bidirectional LSTM (Bi-LSTM) models was approximately 70% with raw data; however, with instantaneous frequency transmission, the accuracy increased to approximately 80%. Two types of deep CNNs, GoogLeNet and ResNet, were trained and tested with time-frequency scalar diagrams from continuous wavelet transformation. The CNN models, with an accuracy greater than 96%, performed significantly better than the RNN models. The ResNet-18, with an accuracy of 98.28%, performed the best. When the sample length was set as the cutterhead rotation period, the deep CNN and RNN models achieved the highest accuracy while the proposed deep CNN model simultaneously achieved high prediction accuracy and feedback efficiency. The proposed model could promptly identify the ground conditions at the working face without stopping the normal tunneling process, and the TBM working parameters could be adjusted and optimized in a timely manner based on the predicted results.     

综合GAN与CNN的应急疏散快速风险评估方法

针对大型公共场馆疏散风险评估问题,提出一种综合生成对抗网络(GAN)与卷积神经网络(CNN)的应急疏散深度学习评估模型,通过WGAN(Wasserstein GAN)进行数据增强,解决疏散数据不足的问题,并基于CNN,分别采用LeNet以及ResNet两种网络结构进行数据训练.以某大型体育馆为例,应用该方法进行疏散风险...     

Hybrid semantic segmentation for tunnel lining cracks based on Swin Transformer and convolutional neural network

In the field of tunnel lining crack identification, the semantic segmentation algorithms based on convolution neural network (CNN) are extensively used. Owing to the inherent locality of CNN, these algorithms cannot make full use of context semantic information, resulting in difficulty in capturing the global features of crack. Transformer-based networks can capture global semantic information, but this method also has the deficiencies of strong data dependence and easy loss of local features. In this paper, a hybrid semantic segmentation algorithm for tunnel lining crack, named SCDeepLab, is proposed by fusing Swin Transformer and CNN in the encoding and decoding framework of DeepLabv3+ to address the above issues. In SCDeepLab, a joint backbone network is introduced with CNN-based Inverse Residual Block and Swin Transformer Block. The former is used to extract the local detailed information of the crack to generate the shallow feature layer, whereas the latter is used to extract the global semantic information to obtain the deep feature layer. In addition, Efficient Channel Attention enhanced Feature Fusion Module is proposed to fuse the shallow and deep features to combine the advantages of the two types of features. Furthermore, the strategy of transfer learning is adopted to solve the data dependency of Swin Transformer. The results show that the mean intersection over union (mIoU) and mean pixel accuracy (mPA) of SCDeepLab on the data sets constructed in this paper are 77.41% and 84.42%, respectively, which have higher segmentation accuracy than previous CNN-based and transformer-based semantic segmentation algorithms.     

Convolutional neural network‐based wind‐induced response estimation model for tall buildings

This study presents a convolutional neural network (CNN)‐based response estimation model for structural health monitoring (SHM) of tall buildings subject to wind loads. In this model, the wind‐induced responses are estimated by CNN trained with previously measured sensor signals; this enables the SHM system to operate stably even when a sensor fault or data loss occurs. In the presented model, top‐level wind‐induced displacement in the time and frequency domains, and wind data in the frequency domain are configured into the input map of the CNN to reflect the resisting capacity of a tall building, the change in the dynamic characteristics of the building due to wind loads, and the relationship between wind load and the building. To evaluate stress, which is used as a safety indicator for structural members in the building, the maximum and minimum strains of columns are set as the output layer of the CNN. The CNN is trained using measured wind and wind response data to predict the column strains during a future wind load. The presented model is validated using data from a wind tunnel test of a building model. The performance of the presented model is verified through strain estimation with data that were not used in the CNN training. To assess the validity of the presented input map configuration, the estimation performance is compared with a CNN that considered only the time domain responses as input. Furthermore, the effects of the variations in the configuration of the CNN on the wind response estimation performance are examined.     

Structural Damage Detection with Automatic Feature‐Extraction through Deep Learning

Structural damage detection is still a challenging problem owing to the difficulty of extracting damage‐sensitive and noise‐robust features from structure response. This article presents a novel damage detection approach to automatically extract features from low‐level sensor data through deep learning. A deep convolutional neural network is designed to learn features and identify damage locations, leading to an excellent localization accuracy on both noise‐free and noisy data set, in contrast to another detector using wavelet packet component energy as the input feature. Visualization of the features learned by hidden layers in the network is implemented to get a physical insight into how the network works. It is found the learned features evolve with the depth from rough filters to the concept of vibration mode, implying the good performance results from its ability to learn essential characteristics behind the data.