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20 pages, 7095 KiB

Open AccessArticle

Multiple Thermal Parameter Inversion for Concrete Dams Using an Integrated Surrogate Model

by Fang Wang, Chunju Zhao, Yihong Zhou, Huawei Zhou, Zhipeng Liang, Feng Wang, Ebrahim Aman Seman and Anran Zheng

Appl. Sci. 2023, 13(9), 5407; https://doi.org/10.3390/app13095407 - 26 Apr 2023

Cited by 2 | Viewed by 1225

Abstract

An efficient and accurate method for concrete thermal parameter inversion is essential to guarantee the reliable and prompt thermal analysis results of dams. Traditional inversion methods either suffer from low analysis efficiency or are limited in accuracy. Thus, this paper presents a method [...] Read more.

An efficient and accurate method for concrete thermal parameter inversion is essential to guarantee the reliable and prompt thermal analysis results of dams. Traditional inversion methods either suffer from low analysis efficiency or are limited in accuracy. Thus, this paper presents a method for multiple thermal parameter inversion based on an integrated surrogate model (ISM) and the Jaya algorithm. This method replaces finite element analysis with an ISM incorporating three machine learning algorithms, Kriging, support vector regression (SVR), and radial basis function (RBF), to describe the mapping relationship between thermal parameters and structure temperature responses. The input datasets for model training and testing are generated by a uniform design approach. Subsequently, a simple and efficient global optimization algorithm, Jaya, is used to identify the thermal parameters by minimizing the error between calculated and monitored temperatures. The effectiveness and practicality of this method are verified by applying monitored data of two strength grades of concrete in a dam. The verification results indicate that the proposed approach can obtain more accurate inversion results than the above individual models. Compared with these models, the inversion errors using ISM are reduced by 8.45%, 3.93% and 20.85%, respectively for C35 concrete, and by 6.53%, 23.82% and 44.43%, respectively for C40 concrete. Additionally, this approach maintains the powerful computational efficiency of surrogate-based optimization, and compared to the methods that directly invert using swarm intelligence algorithms, the analysis efficiency is improved by about 111.7 times. Full article

(This article belongs to the Special Issue Machine Learning–Based Structural Health Monitoring)

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15 pages, 4214 KiB

Open AccessArticle

A Flight Parameter-Based Aircraft Structural Load Monitoring Method Using a Genetic Algorithm Enhanced Extreme Learning Machine

by Yanjun Zhang, Shancheng Cao, Bintuan Wang and Zhiping Yin

Appl. Sci. 2023, 13(6), 4018; https://doi.org/10.3390/app13064018 - 22 Mar 2023

Cited by 3 | Viewed by 2091

Abstract

High-precision operational flight loads are essential for monitoring fatigue of individual aircraft and are usually determined by flight parameters. To tackle the nonlinear relationship between flight loads and flight parameters for more accurate prediction of flight loads, artificial neural networks have been widely [...] Read more.

High-precision operational flight loads are essential for monitoring fatigue of individual aircraft and are usually determined by flight parameters. To tackle the nonlinear relationship between flight loads and flight parameters for more accurate prediction of flight loads, artificial neural networks have been widely studied. However, there are still two major problems, namely the training strategy and sensitivity analysis of the flight parameters. For the first problem, the gradient descent method is usually used, which is time-consuming and can easily converge to a local solution. To solve this problem, an extreme learning machine is proposed to determine the weights based on a Moore–Penrose generalized inverse. Moreover, a genetic algorithm method is proposed to optimize the weights between the input and hidden layers. For the second problem, a mean impact value (MIV) method is proposed to measure the sensitivity of the flight parameters, and the neuron number in the hidden layer is also optimized. Finally, based on the measured dataset of an aircraft, the proposed flight load prediction method is verified to be effective and efficient. In addition, a comparison is made with some well-known neural networks to demonstrate the advantages of the proposed method. Full article

(This article belongs to the Special Issue Machine Learning–Based Structural Health Monitoring)

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17 pages, 1983 KiB

Open AccessArticle

Long-Term Structural State Trend Forecasting Based on an FFT–Informer Model

by Jihao Ma and Jingpei Dan

Appl. Sci. 2023, 13(4), 2553; https://doi.org/10.3390/app13042553 - 16 Feb 2023

Cited by 7 | Viewed by 2294

Abstract

Machine learning has been widely applied in structural health monitoring. While most existing methods, which are limited to forecasting structural state evolution of large infrastructures. forecast the structural state in a step-by-step manner, extracting feature of structural state trends and the negative effects [...] Read more.

Machine learning has been widely applied in structural health monitoring. While most existing methods, which are limited to forecasting structural state evolution of large infrastructures. forecast the structural state in a step-by-step manner, extracting feature of structural state trends and the negative effects of data collection under abnormal conditions are big challenges. To address these issues, a long-term structural state trend forecasting method based on long sequence time-series forecasting (LSTF) with an improved Informer model integrated with Fast Fourier transform (FFT) is proposed, named the FFT–Informer model. In this method, by using FFT, structural state trend features are represented by extracting amplitude and phase of a certain period of data sequence. Structural state trend, a long sequence, can be forecasted in a one-forward operation by the Informer model that can achieve high inference speed and accuracy of prediction based on the Transformer model. Furthermore, a Hampel filter that filters the abnormal deviation of the data sequence is integrated into the Multi-head ProbSparse self-attention in the Informer model to improve forecasting accuracy by reducing the effect of abnormal data points. Experimental results on two classical data sets show that the FFT–Informer model achieves high and stable accuracy and outperforms the comparative models in forecasting accuracy. It indicates that this model can effectively forecast the long-term state trend change of a structure and is proposed to be applied to structural state trend forecasting and early damage warning. Full article

(This article belongs to the Special Issue Machine Learning–Based Structural Health Monitoring)

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21 pages, 8383 KiB

Open AccessArticle

Bolt Loosening Detection Using Key-Point Detection Enhanced by Synthetic Datasets

by Qizhe Lu, Yicheng Jing and Xuefeng Zhao

Appl. Sci. 2023, 13(3), 2020; https://doi.org/10.3390/app13032020 - 3 Feb 2023

Cited by 5 | Viewed by 3807

Abstract

Machine vision based on deep learning is gaining more and more applications in structural health monitoring (SHM) due to the rich information that can be achieved in the images. Bolts are widely used in the connection of steel structures, and their loosening can [...] Read more.

Machine vision based on deep learning is gaining more and more applications in structural health monitoring (SHM) due to the rich information that can be achieved in the images. Bolts are widely used in the connection of steel structures, and their loosening can compromise the safety of steel structures and lead to serious accidents. Therefore, this paper proposes a method for the automatic detection of the bolt loosening angle based on the latest key point detection technology using machine vision and deep learning. First, we built a virtual laboratory in Unreal Engine5 that could automatically label and generate synthetic datasets, and the datasets with bolts were collected. Second, the datasets were trained using the YOLOv7-pose framework, and the resulting model was able to accurately detect key points of bolts in images obtained under different angles and lighting conditions. Third, a bolt loosening angle calculation method was proposed according to the detected key points and the position relationship between neighboring bolts. Our results demonstrate that the proposed method is effective at detecting the bolt loosening angle and that the use of synthetic datasets significantly improves the efficiency of datasets establishment while also improving the performance of model training. Full article

(This article belongs to the Special Issue Machine Learning–Based Structural Health Monitoring)

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15 pages, 5198 KiB

Open AccessArticle

A Comprehensive Safety Analysis Study for Concrete Core Dams

by Meng Yang, Dong Wang and Chongshi Gu

Appl. Sci. 2023, 13(3), 1679; https://doi.org/10.3390/app13031679 - 28 Jan 2023

Cited by 2 | Viewed by 1590

Abstract

The number of earth-rock dams and their failures are both the highest of all dam types. For a large number of dangerous situations, multi-angle and multi-level effective safety analysis is urgently required. In this paper, a series of studies on seepage and slope [...] Read more.

The number of earth-rock dams and their failures are both the highest of all dam types. For a large number of dangerous situations, multi-angle and multi-level effective safety analysis is urgently required. In this paper, a series of studies on seepage and slope stability of the dangerous clay core dam with danger control and reinforcement (CCDDCR) are investigated by a proposed finite element analysis method. A verification process is finished for this proposed method. A new calculation model is proposed based on an iterative algorithm, and a successful example is then taken on. A reasonable conclusion is given based on the analysis of the three-dimensional finite element model of the dangerous CCDDCR. In view of the conventional concrete, the elastic modulus of the wall is higher, and large deformation and stress and concentration will appear under the water loading, which then affects the anti-seepage effect. Its purpose is to investigate the effect of diaphragm wall material in concrete with low elastic modulus on the anti-seepage wall and its significance in similar reinforcement engineering. The first tentative comparative analysis is taken on by this paper for slope stability analysis between the Lizheng method and FEM method. More useful conclusions can be drawn in future for reference in similar reinforcement projects. Full article

(This article belongs to the Special Issue Machine Learning–Based Structural Health Monitoring)

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17 pages, 5018 KiB

Open AccessArticle

ANN Prediction Model of Concrete Fatigue Life Based on GRW-DBA Data Augmentation

by Jinna Shi, Wenxiu Zhang and Yanru Zhao

Appl. Sci. 2023, 13(2), 1227; https://doi.org/10.3390/app13021227 - 16 Jan 2023

Cited by 2 | Viewed by 2577

Abstract

In order to improve the prediction accuracy of the machine learning model for concrete fatigue life using small datasets, a group calculation and random weight dynamic time warping barycentric averaging (GRW-DBA) data augmentation method is proposed. First, 27 sets of real experimental data [...] Read more.

In order to improve the prediction accuracy of the machine learning model for concrete fatigue life using small datasets, a group calculation and random weight dynamic time warping barycentric averaging (GRW-DBA) data augmentation method is proposed. First, 27 sets of real experimental data were augmented by 10 times, 20 times, 50 times, 100 times, 200 times, 500 times, and 1000 times, respectively, using the GRW-DBA method, and the optimal factor was determined by comparing the model’s training time and prediction accuracy under different augmentation multiples. Then, a concrete fatigue life prediction model was established based on artificial neural network (ANN), and the hyperparameters of the model were determined through experiments. Finally, comparisons were made with data augmentation methods such as generative adversarial network (GAN) and regression prediction models such as support vector machine (SVM), and the generalization of the method was verified using another fatigue life dataset collected on the Internet. The result shows that the GRW-DBA algorithm can significantly improve the prediction accuracy of the ANN model when using small datasets (the R² index increased by 20.1% compared with the blank control, reaching 98.6%), and this accuracy improvement is also verified in different data distributions. Finally, a graphical user interface is created based on the developed model to facilitate application in engineering. Full article

(This article belongs to the Special Issue Machine Learning–Based Structural Health Monitoring)

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17 pages, 6720 KiB

Open AccessArticle

Crack Location and Degree Detection Method Based on YOLOX Model

by Linlin Wang, Junjie Li and Fei Kang

Appl. Sci. 2022, 12(24), 12572; https://doi.org/10.3390/app122412572 - 8 Dec 2022

Cited by 7 | Viewed by 2613

Abstract

Damage detection and evaluation are concerns in structural health monitoring. Traditional damage detection techniques are inefficient because of the need for damage detection before evaluation. To address these problems, a novel crack location and degree detector based on YOLOX is proposed, which directly [...] Read more.

Damage detection and evaluation are concerns in structural health monitoring. Traditional damage detection techniques are inefficient because of the need for damage detection before evaluation. To address these problems, a novel crack location and degree detector based on YOLOX is proposed, which directly realizes damage detection and evaluation. Moreover, the detector presents a superior detection effect and speed to other advanced deep learning models. Additionally, rather than at the pixel level, the detection results are determined in actual scales according to resolution. The results demonstrate that the proposed model can detect and evaluate damage accurately and automatically. Full article

(This article belongs to the Special Issue Machine Learning–Based Structural Health Monitoring)

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16 pages, 11698 KiB

Open AccessArticle

A Combined Safety Monitoring Model for High Concrete Dams

by Chongshi Gu, Yanbo Wang, Hao Gu, Yating Hu, Meng Yang, Wenhan Cao and Zheng Fang

Appl. Sci. 2022, 12(23), 12103; https://doi.org/10.3390/app122312103 - 26 Nov 2022

Cited by 8 | Viewed by 1470

Abstract

When applying reliability analysis to the monitoring of structural health, it is very important that gross errors–which affect prediction accuracy–are included within the monitoring information. An approach using gross errors identification and a dam safety monitoring model for deformation monitoring data of concrete [...] Read more.

When applying reliability analysis to the monitoring of structural health, it is very important that gross errors–which affect prediction accuracy–are included within the monitoring information. An approach using gross errors identification and a dam safety monitoring model for deformation monitoring data of concrete dams is proposed in this paper. It can solve the problems of strong nonlinearity and the difficulty of identifying and eliminating gross errors in deformation monitoring data in concrete dams. This new method combines the advantages of an incremental extreme learning machine (I-ELM) method to seek an optimal network structure, the Least Median Squares (LMS) method with strong robustness to multiple failure points, the robust estimation IGG method with the good robustness to outliers (gross errors) and extreme learning machine (ELM) method with high prediction efficiency and handling of nonlinear problems. The proposed method can eliminate gross errors and be utilized to predict the behavior of concrete dams. The deformation monitoring data of an existing 305 m-high concrete arch dam is acquired by combining remote sensing technology with other monitoring methods. The LMS-IGG-ELM method is utilized to eliminate outliers from the dam monitoring sequence and is compared with the processing result from a DBSCAN clustering algorithm, Romanovsky criterion and the 3σ method. The results show that the proposed method has the highest gross errors identification rate, the strongest generalization ability and the best prediction effect. Full article

(This article belongs to the Special Issue Machine Learning–Based Structural Health Monitoring)

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30 pages, 10538 KiB

Open AccessArticle

Non-Contact Crack Visual Measurement System Combining Improved U-Net Algorithm and Canny Edge Detection Method with Laser Rangefinder and Camera

by Sizeng Zhao, Fei Kang and Junjie Li

Appl. Sci. 2022, 12(20), 10651; https://doi.org/10.3390/app122010651 - 21 Oct 2022

Cited by 16 | Viewed by 3419

Abstract

Cracks are the main damages of concrete structures. Since cracks may occur in areas that are difficult to reach, non-contact measurement technology is required to accurately measure the width of cracks. This study presents an innovative computer vision system combining a camera and [...] Read more.

Cracks are the main damages of concrete structures. Since cracks may occur in areas that are difficult to reach, non-contact measurement technology is required to accurately measure the width of cracks. This study presents an innovative computer vision system combining a camera and laser rangefinder to measure crack width from any angle and at a long distance. To solve the problem of pixel distortion caused by non-vertical photographing, geometric transformation formulas that can calculate the unit pixel length of the image captured at any angle are proposed. The complexity of crack edge calculation and the imbalance of data in the image are other problems that affect measurement accuracy, and a combination of the improved U-net convolutional networks algorithm and Canny edge detection method is adopted to accurately extract the cracks. The measurement results on the different concrete wall indicate that the proposed system can measure the crack in a non-vertical position, and the proposed algorithm can extract the crack from different background images. Although the proposed system cannot achieve fully automated measurement, the results also confirm the ability to obtain the crack width accurately and conveniently. Full article

(This article belongs to the Special Issue Machine Learning–Based Structural Health Monitoring)

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