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MSWNet: A visual deep machine learning method adopting transfer learning based upon ResNet 50 for municipal

《环境科学与工程前沿（英文）》 2023年 第17卷 第6期 doi: 10.1007/s11783-023-1677-1

摘要：

● MSWNet was proposed to classify municipal solid waste.

关键词： Municipal solid waste sorting     Deep residual network     Transfer learning     Cyclic learning rate     Visualization    

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Prediction of bearing capacity of pile foundation using deep learning approaches

《结构与土木工程前沿（英文）》 2024年 第18卷 第6期   页码 870-886 doi: 10.1007/s11709-024-1085-z

摘要： The accurate prediction of bearing capacity is crucial in ensuring the structural integrity and safety of pile foundations. This research compares the Deep Neural Networks (DNN), Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), Long Short-Term Memory (LSTM), and Bidirectional LSTM (BiLSTM) algorithms utilizing a data set of 257 dynamic pile load tests for the first time. Also, this research illustrates the multicollinearity effect on DNN, CNN, RNN, LSTM, and BiLSTM models’ performance and accuracy for the first time. A comprehensive comparative analysis is conducted, employing various statistical performance parameters, rank analysis, and error matrix to evaluate the performance of these models. The performance is further validated using external validation, and visual interpretation is provided using the regression error characteristics (REC) curve and Taylor diagram. Results from the comparative analysis reveal that the DNN (Coefficient of determination (R2)training (TR) = 0.97, root mean squared error (RMSE)TR = 0.0413; R2testing (TS) = 0.9, RMSETS = 0.08) followed by BiLSTM (R2TR = 0.91, RMSETR = 0.782; R2TS = 0.89, RMSETS = 0.0862) model demonstrates the highest performance accuracy. It is noted that the BiLSTM model is better than LSTM because the BiLSTM model, which increases the amount of information for the network, is a sequence processing model made up of two LSTMs, one of which takes the input in a forward manner, and the other in a backward direction. The prediction of pile-bearing capacity is strongly influenced by ram weight (having a considerable multicollinearity level), and the effect of the considerable multicollinearity level has been determined for the model based on the recurrent neural network approach. In this study, the recurrent neural network model has the least performance and accuracy in predicting the pile-bearing capacity.

关键词： deep learning algorithms     high-strain dynamic pile test     bearing capacity of the pile    

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Deep learning based water leakage detection for shield tunnel lining

《结构与土木工程前沿（英文）》 2024年 第18卷 第6期   页码 887-898 doi: 10.1007/s11709-024-1071-5

摘要： Shield tunnel lining is prone to water leakage, which may further bring about corrosion and structural damage to the walls, potentially leading to dangerous accidents. To avoid tedious and inefficient manual inspection, many projects use artificial intelligence (AI) to detect cracks and water leakage. A novel method for water leakage inspection in shield tunnel lining that utilizes deep learning is introduced in this paper. Our proposal includes a ConvNeXt-S backbone, deconvolutional-feature pyramid network (D-FPN), spatial attention module (SPAM). and a detection head. It can extract representative features of leaking areas to aid inspection processes. To further improve the model’s robustness, we innovatively use an inversed low-light enhancement method to convert normally illuminated images to low light ones and introduce them into the training samples. Validation experiments are performed, achieving the average precision (AP) score of 56.8%, which outperforms previous work by a margin of 5.7%. Visualization illustrations also support our method’s practical effectiveness.

关键词： water leakage detection     deep learning     deconvolutional-feature pyramid     spatial attention    

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A new automatic convolutional neural network based on deep reinforcement learning for fault diagnosis

《机械工程前沿（英文）》 2022年 第17卷 第2期 doi: 10.1007/s11465-022-0673-7

摘要： Convolutional neural network (CNN) has achieved remarkable applications in fault diagnosis. However, the tuning aiming at obtaining the well-trained CNN model is mainly manual search. Tuning requires considerable experiences on the knowledge on CNN training and fault diagnosis, and is always time consuming and labor intensive, making the automatic hyper parameter optimization (HPO) of CNN models essential. To solve this problem, this paper proposes a novel automatic CNN (ACNN) for fault diagnosis, which can automatically tune its three key hyper parameters, namely, learning rate, batch size, and L2-regulation. First, a new deep reinforcement learning (DRL) is developed, and it constructs an agent aiming at controlling these three hyper parameters along with the training of CNN models online. Second, a new structure of DRL is designed by combining deep deterministic policy gradient and long short-term memory, which takes the training loss of CNN models as its input and can output the adjustment on these three hyper parameters. Third, a new training method for ACNN is designed to enhance its stability. Two famous bearing datasets are selected to evaluate the performance of ACNN. It is compared with four commonly used HPO methods, namely, random search, Bayesian optimization, tree Parzen estimator, and sequential model-based algorithm configuration. ACNN is also compared with other published machine learning (ML) and deep learning (DL) methods. The results show that ACNN outperforms these HPO and ML/DL methods, validating its potential in fault diagnosis.

关键词： deep reinforcement learning     hyper parameter optimization     convolutional neural network     fault diagnosis    

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Forecasting measured responses of structures using temporal deep learning and dual attention

《结构与土木工程前沿（英文）》 2024年 第18卷 第6期   页码 832-850 doi: 10.1007/s11709-024-1092-0

摘要： The objective of this study is to develop a novel and efficient model for forecasting the nonlinear behavior of structures in response to time-varying random excitation. The key idea is to design a deep learning architecture to leverage the relationships, between external excitations and structure’s vibration signals, and between historical values and future values, within multiple time-series data. The proposed method consists of two main steps: the first step applies a global attention mechanism to combine multiple-measured time series and time-varying excitation into a weighted time series before feeding it to a temporal architecture; the second step utilizes a self-attention mechanism followed by a fully connected layer to predict multi-step future values. The viability of the proposed method is demonstrated via two case studies involving synthetic data from a three-dimensional (3D) reinforced concrete structure and experimental data from an 18-story steel frame. Furthermore, comparison and robustness studies are carried out, showing that the proposed method outperforms conventional methods and maintains high performance in the presence of noise with an amplitude of less than 10%.

关键词： structural dynamic     time-varying excitation     deep learning     signal processing     response forecasting    

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Dynamic prediction of moving trajectory in pipe jacking: GRU-based deep learning framework

《结构与土木工程前沿（英文）》 2023年 第17卷 第7期   页码 994-1010 doi: 10.1007/s11709-023-0942-5

摘要： The moving trajectory of the pipe-jacking machine (PJM), which primarily determines the end quality of jacked tunnels, must be controlled strictly during the entire jacking process. Developing prediction models to support drivers in performing rectifications in advance can effectively avoid considerable trajectory deviations from the designed jacking axis. Hence, a gated recurrent unit (GRU)-based deep learning framework is proposed herein to dynamically predict the moving trajectory of the PJM. In this framework, operational data are first extracted from a data acquisition system; subsequently, they are preprocessed and used to establish GRU-based multivariate multistep-ahead direct prediction models. To verify the performance of the proposed framework, a case study of a large pipe-jacking project in Shanghai and comparisons with other conventional models (i.e., long short-term memory (LSTM) network and recurrent neural network (RNN)) are conducted. In addition, the effects of the activation function and input time-step length on the prediction performance of the proposed framework are investigated and discussed. The results show that the proposed framework can dynamically and precisely predict the PJM moving trajectory during the pipe-jacking process, with a minimum mean absolute error and root mean squared error (RMSE) of 0.1904 and 0.5011 mm, respectively. The RMSE of the GRU-based models is lower than those of the LSTM- and RNN-based models by 21.46% and 46.40% at the maximum, respectively. The proposed framework is expected to provide an effective decision support for moving trajectory control and serve as a foundation for the application of deep learning in the automatic control of pipe jacking.

关键词： dynamic prediction     moving trajectory     pipe jacking     GRU     deep learning    

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Digital image correlation-based structural state detection through deep learning

《结构与土木工程前沿（英文）》 2022年 第16卷 第1期   页码 45-56 doi: 10.1007/s11709-021-0777-x

摘要： This paper presents a new approach for automatical classification of structural state through deep learning. In this work, a Convolutional Neural Network (CNN) was designed to fuse both the feature extraction and classification blocks into an intelligent and compact learning system and detect the structural state of a steel frame; the input was a series of vibration signals, and the output was a structural state. The digital image correlation (DIC) technology was utilized to collect vibration information of an actual steel frame, and subsequently, the raw signals, without further pre-processing, were directly utilized as the CNN samples. The results show that CNN can achieve 99% classification accuracy for the research model. Besides, compared with the backpropagation neural network (BPNN), the CNN had an accuracy similar to that of the BPNN, but it only consumes 19% of the training time. The outputs of the convolution and pooling layers were visually displayed and discussed as well. It is demonstrated that: 1) the CNN can extract the structural state information from the vibration signals and classify them; 2) the detection and computational performance of the CNN for the incomplete data are better than that of the BPNN; 3) the CNN has better anti-noise ability.

关键词： structural state detection     deep learning     digital image correlation     vibration signal     steel frame    

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Survey on deep learning for pulmonary medical imaging

Jiechao Ma, Yang Song, Xi Tian, Yiting Hua, Rongguo Zhang, Jianlin Wu

《医学前沿（英文）》 2020年 第14卷 第4期   页码 450-469 doi: 10.1007/s11684-019-0726-4

摘要： As a promising method in artificial intelligence, deep learning has been proven successful in several domains ranging from acoustics and images to natural language processing. With medical imaging becoming an important part of disease screening and diagnosis, deep learning-based approaches have emerged as powerful techniques in medical image areas. In this process, feature representations are learned directly and automatically from data, leading to remarkable breakthroughs in the medical field. Deep learning has been widely applied in medical imaging for improved image analysis. This paper reviews the major deep learning techniques in this time of rapid evolution and summarizes some of its key contributions and state-of-the-art outcomes. The topics include classification, detection, and segmentation tasks on medical image analysis with respect to pulmonary medical images, datasets, and benchmarks. A comprehensive overview of these methods implemented on various lung diseases consisting of pulmonary nodule diseases, pulmonary embolism, pneumonia, and interstitial lung disease is also provided. Lastly, the application of deep learning techniques to the medical image and an analysis of their future challenges and potential directions are discussed.

关键词： deep learning     neural networks     pulmonary medical image     survey    

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Investigation of crack segmentation and fast evaluation of crack propagation, based on deep learning

《结构与土木工程前沿（英文）》 2024年 第18卷 第4期   页码 516-535 doi: 10.1007/s11709-024-1040-z

摘要： Identifying crack and predicting crack propagation are critical processes for the risk assessment of engineering structures. Most traditional approaches to crack modeling are faced with issues of high computational costs and excessive computing time. To address this issue, we explore the potential of deep learning (DL) to increase the efficiency of crack detection and forecasting crack growth. However, there is no single algorithm that can fit all data sets well or can apply in all cases since specific tasks vary. In the paper, we present DL models for identifying cracks, especially on concrete surface images, and for predicting crack propagation. Firstly, SegNet and U-Net networks are used to identify concrete cracks. Stochastic gradient descent (SGD) and adaptive moment estimation (Adam) algorithms are applied to minimize loss function during iterations. Secondly, time series algorithms including gated recurrent unit (GRU) and long short-term memory (LSTM) are used to predict crack propagation. The experimental findings indicate that the U-Net is more robust and efficient than the SegNet for identifying crack segmentation and achieves the most outstanding results. For evaluation of crack propagation, GRU and LSTM are used as DL models and results show good agreement with the experimental data.

关键词： deep learning     crack segmentation     crack propagation     encoder−decoder     recurrent neural network    

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Hybrid deep learning model for risk prediction of fracture in patients with diabetes and osteoporosis

《医学前沿（英文）》 2022年 第16卷 第3期   页码 496-506 doi: 10.1007/s11684-021-0828-7

摘要： The fracture risk of patients with diabetes is higher than those of patients without diabetes due to hyperglycemia, usage of diabetes drugs, changes in insulin levels, and excretion, and this risk begins as early as adolescence. Many factors including demographic data (such as age, height, weight, and gender), medical history (such as smoking, drinking, and menopause), and examination (such as bone mineral density, blood routine, and urine routine) may be related to bone metabolism in patients with diabetes. However, most of the existing methods are qualitative assessments and do not consider the interactions of the physiological factors of humans. In addition, the fracture risk of patients with diabetes and osteoporosis has not been further studied previously. In this paper, a hybrid model combining XGBoost with deep neural network is used to predict the fracture risk of patients with diabetes and osteoporosis, and investigate the effect of patients’ physiological factors on fracture risk. A total of 147 raw input features are considered in our model. The presented model is compared with several benchmarks based on various metrics to prove its effectiveness. Moreover, the top 18 influencing factors of fracture risks of patients with diabetes are determined.

关键词： XGBoost     deep neural network     healthcare     risk prediction    

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Efficient Identification of water conveyance tunnels siltation based on ensemble deep learning

Xinbin WU; Junjie LI; Linlin WANG

《结构与土木工程前沿（英文）》 2022年 第16卷 第5期   页码 564-575 doi: 10.1007/s11709-022-0829-x

摘要： The inspection of water conveyance tunnels plays an important role in water diversion projects. Siltation is an essential factor threatening the safety of water conveyance tunnels. Accurate and efficient identification of such siltation can reduce risks and enhance safety and reliability of these projects. The remotely operated vehicle (ROV) can detect such siltation. However, it needs to improve its intelligent recognition of image data it obtains. This paper introduces the idea of ensemble deep learning. Based on the VGG16 network, a compact convolutional neural network (CNN) is designed as a primary learner, called Silt-net, which is used to identify the siltation images. At the same time, the fully-connected network is applied as the meta-learner, and stacking ensemble learning is combined with the outputs of the primary classifiers to obtain satisfactory classification results. Finally, several evaluation metrics are used to measure the performance of the proposed method. The experimental results on the siltation dataset show that the classification accuracy of the proposed method reaches 97.2%, which is far better than the accuracy of other classifiers. Furthermore, the proposed method can weigh the accuracy and model complexity on a platform with limited computing resources.

关键词： water conveyance tunnels     siltation images     remotely operated vehicles     deep learning     ensemble learning     computer vision    

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Multi-model ensemble deep learning method for intelligent fault diagnosis with high-dimensional samples

Xin ZHANG, Tao HUANG, Bo WU, Youmin HU, Shuai HUANG, Quan ZHOU, Xi ZHANG

《机械工程前沿（英文）》 2021年 第16卷 第2期   页码 340-352 doi: 10.1007/s11465-021-0629-3

摘要： Deep learning has achieved much success in mechanical intelligent fault diagnosis in recent years. However, many deep learning methods cannot fully extract fault information to recognize mechanical health states when processing high-dimensional samples. Therefore, a multi-model ensemble deep learning method based on deep convolutional neural network (DCNN) is proposed in this study to accomplish fault recognition of high-dimensional samples. First, several 1D DCNN models with different activation functions are trained through dimension reduction learning to obtain different fault features from high-dimensional samples. Second, the obtained features are constructed into 2D images with multiple channels through a conversion method. The integrated 2D feature images can effectively represent the fault characteristic contained in raw high-dimension vibration signals. Lastly, a 2D DCNN model with multi-layer convolution and pooling is used to automatically learn features from the 2D images and identify the fault mode of the mechanical equipment by adopting a softmax classifier. The proposed method, which is validated using the bearing public dataset of Case Western Reserve University, USA and a one-stage reduction gearbox dataset, has high recognition accuracy. Compared with other classical deep learning methods, the proposed fault diagnosis method has considerable improvements.

关键词： fault intelligent diagnosis     deep learning     deep convolutional neural network     high-dimensional samples    

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A novel deep learning framework with variational auto-encoder for indoor air quality prediction

《环境科学与工程前沿（英文）》 2024年 第18卷 第1期 doi: 10.1007/s11783-024-1768-7

摘要：

● PLS-VAER is proposed for modeling of PM2.5 concentration.

关键词： Indoor air quality     PM2.5 concentration     Variational auto-encoder     Latent variable     Soft measurement modeling    

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Bridging finite element and deep learning: High-resolution stress distribution prediction in structural

Hamed BOLANDI; Xuyang LI; Talal SALEM; Vishnu Naresh BODDETI; Nizar LAJNEF

《结构与土木工程前沿（英文）》 2022年 第16卷 第11期   页码 1365-1377 doi: 10.1007/s11709-022-0882-5

摘要： Finite-element analysis (FEA) for structures has been broadly used to conduct stress analysis of various civil and mechanical engineering structures. Conventional methods, such as FEA, provide high fidelity results but require the solution of large linear systems that can be computationally intensive. Instead, Deep Learning (DL) techniques can generate results significantly faster than conventional run-time analysis. This can prove extremely valuable in real-time structural assessment applications. Our proposed method uses deep neural networks in the form of convolutional neural networks (CNN) to bypass the FEA and predict high-resolution stress distributions on loaded steel plates with variable loading and boundary conditions. The CNN was designed and trained to use the geometry, boundary conditions, and load as input to predict the stress contours. The proposed technique’s performance was compared to finite-element simulations using a partial differential equation (PDE) solver. The trained DL model can predict the stress distributions with a mean absolute error of 0.9% and an absolute peak error of 0.46% for the von Mises stress distribution. This study shows the feasibility and potential of using DL techniques to bypass FEA for stress analysis applications.

关键词： Deep Learning     finite element analysis     stress contours     structural components    

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Deep learning in digital pathology image analysis: a survey

Shujian Deng, Xin Zhang, Wen Yan, Eric I-Chao Chang, Yubo Fan, Maode Lai, Yan Xu

《医学前沿（英文）》 2020年 第14卷 第4期   页码 470-487 doi: 10.1007/s11684-020-0782-9

摘要： deep learning (DL) has achieved state-of-the-art performance in many digital pathology analysis tasks. Traditional methods usually require hand-crafted domain-specific features, and DL methods can learn representations without manually designed features. In terms of feature extraction, DL approaches are less labor intensive compared with conventional machine learning methods. In this paper, we comprehensively summarize recent DL-based image analysis studies in histopathology, including different tasks (e.g., classification, semantic segmentation, detection, and instance segmentation) and various applications (e.g., stain normalization, cell/gland/region structure analysis). DL methods can provide consistent and accurate outcomes. DL is a promising tool to assist pathologists in clinical diagnosis.

关键词： pathology     deep learning     segmentation     detection     classification    

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标题 作者 时间 类型 操作

MSWNet: A visual deep machine learning method adopting transfer learning based upon ResNet 50 for municipal

期刊论文

Prediction of bearing capacity of pile foundation using deep learning approaches

期刊论文

Deep learning based water leakage detection for shield tunnel lining

期刊论文

A new automatic convolutional neural network based on deep reinforcement learning for fault diagnosis

期刊论文

Forecasting measured responses of structures using temporal deep learning and dual attention

期刊论文

Dynamic prediction of moving trajectory in pipe jacking: GRU-based deep learning framework

期刊论文

Digital image correlation-based structural state detection through deep learning

期刊论文

Survey on deep learning for pulmonary medical imaging

Jiechao Ma, Yang Song, Xi Tian, Yiting Hua, Rongguo Zhang, Jianlin Wu

期刊论文

Investigation of crack segmentation and fast evaluation of crack propagation, based on deep learning

期刊论文

Hybrid deep learning model for risk prediction of fracture in patients with diabetes and osteoporosis

期刊论文

Efficient Identification of water conveyance tunnels siltation based on ensemble deep learning

Xinbin WU; Junjie LI; Linlin WANG

期刊论文

Multi-model ensemble deep learning method for intelligent fault diagnosis with high-dimensional samples

Xin ZHANG, Tao HUANG, Bo WU, Youmin HU, Shuai HUANG, Quan ZHOU, Xi ZHANG

期刊论文

A novel deep learning framework with variational auto-encoder for indoor air quality prediction

期刊论文

Bridging finite element and deep learning: High-resolution stress distribution prediction in structural

Hamed BOLANDI; Xuyang LI; Talal SALEM; Vishnu Naresh BODDETI; Nizar LAJNEF

期刊论文

Deep learning in digital pathology image analysis: a survey

Shujian Deng, Xin Zhang, Wen Yan, Eric I-Chao Chang, Yubo Fan, Maode Lai, Yan Xu

期刊论文