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Multiscale computation on feedforward neural network and recurrent neural network

Bin LI, Xiaoying ZHUANG

《结构与土木工程前沿（英文）》 2020年 第14卷 第6期   页码 1285-1298 doi: 10.1007/s11709-020-0691-7

摘要： Homogenization methods can be used to predict the effective macroscopic properties of materials that are heterogenous at micro- or fine-scale. Among existing methods for homogenization, computational homogenization is widely used in multiscale analyses of structures and materials. Conventional computational homogenization suffers from long computing times, which substantially limits its application in analyzing engineering problems. The neural networks can be used to construct fully decoupled approaches in nonlinear multiscale methods by mapping macroscopic loading and microscopic response. Computational homogenization methods for nonlinear material and implementation of offline multiscale computation are studied to generate data set. This article intends to model the multiscale constitution using feedforward neural network (FNN) and recurrent neural network (RNN), and appropriate set of loading paths are selected to effectively predict the materials behavior along unknown paths. Applications to two-dimensional multiscale analysis are tested and discussed in detail.

关键词： multiscale method     constitutive model     feedforward neural network     recurrent neural network    

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Crack identification in concrete, using digital image correlation and neural network

《结构与土木工程前沿（英文）》 2024年 第18卷 第4期   页码 536-550 doi: 10.1007/s11709-024-1013-2

摘要： In engineering applications, concrete crack monitoring is very important. Traditional methods are of low efficiency, low accuracy, have poor timeliness, and are applicable in only a limited number of scenarios. Therefore, more comprehensive detection of concrete damage under different scenarios is of high value for practical engineering applications. Digital image correlation (DIC) technology can provide a large amount of experimental data, and neural network (NN) can process very rich data. Therefore, NN, including convolutional neural networks (CNN) and back propagation neural networks (BP), can be combined with DIC technology to analyze experimental data of three-point bending of plain concrete and four-point bending of reinforced concrete. In addition, strain parameters can be used for training, and displacement parameters can be added for comprehensive consideration. The data obtained by DIC technology are grouped for training, and the recognition results of NN show that the combination of strain and displacement parameters, i.e., the response of specimen surface and whole body, can make results more objective and comprehensive. The identification results obtained by CNN and BP show that these technologies can accurately identify cracks. The identification results for reinforced concrete specimens are less affected by noise than those of plain concrete specimens. CNN is more convenient because it can identify some features directly from images, recognizing the cracks formed by macro development. BP can issue early warning of the microscopic cracks, but it requires a large amount of data and computation. It can be seen that CNN is more intuitive and efficient in image processing, and is suitable when low accuracy is adequate, while BP is suitable for occasions with greater accuracy requirements. The two tools have advantages in different situations, and together they can play an important role in engineering monitoring.

关键词： digital image correlation     convolutional neural network     back propagation neural neural network     damage detection     concrete    

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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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Novel interpretable mechanism of neural networks based on network decoupling method

《工程管理前沿（英文）》 2021年 第8卷 第4期   页码 572-581 doi: 10.1007/s42524-021-0169-x

摘要： The lack of interpretability of the neural network algorithm has become the bottleneck of its wide application. We propose a general mathematical framework, which couples the complex structure of the system with the nonlinear activation function to explore the decoupled dimension reduction method of high-dimensional system and reveal the calculation mechanism of the neural network. We apply our framework to some network models and a real system of the whole neuron map of Caenorhabditis elegans. Result shows that a simple linear mapping relationship exists between network structure and network behavior in the neural network with high-dimensional and nonlinear characteristics. Our simulation and theoretical results fully demonstrate this interesting phenomenon. Our new interpretation mechanism provides not only the potential mathematical calculation principle of neural network but also an effective way to accurately match and predict human brain or animal activities, which can further expand and enrich the interpretable mechanism of artificial neural network in the future.

关键词： neural networks     interpretability     dynamical behavior     network decouple    

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Deep convolutional neural network for multi-level non-invasive tunnel lining assessment

《结构与土木工程前沿（英文）》 2022年 第16卷 第2期   页码 214-223 doi: 10.1007/s11709-021-0800-2

摘要： In recent years, great attention has focused on the development of automated procedures for infrastructures control. Many efforts have aimed at greater speed and reliability compared to traditional methods of assessing structural conditions. The paper proposes a multi-level strategy, designed and implemented on the basis of periodic structural monitoring oriented to a cost- and time-efficient tunnel control plan. Such strategy leverages the high capacity of convolutional neural networks to identify and classify potential critical situations. In a supervised learning framework, Ground Penetrating Radar (GPR) profiles and the revealed structural phenomena have been used as input and output to train and test such networks. Image-based analysis and integrative investigations involving video-endoscopy, core drilling, jacking and pull-out testing have been exploited to define the structural conditions linked to GPR profiles and to create the database. The degree of detail and accuracy achieved in identifying a structural condition is high. As a result, this strategy appears of value to infrastructure managers who need to reduce the amount and invasiveness of testing, and thus also to reduce the time and costs associated with inspections made by highly specialized technicians.

关键词： concrete structure     GPR     damage classification     convolutional neural network     transfer learning    

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Automated identification of steel weld defects, a convolutional neural network improved machine learning

《结构与土木工程前沿（英文）》 2024年 第18卷 第2期   页码 294-308 doi: 10.1007/s11709-024-1045-7

摘要： This paper proposes a machine-learning-based methodology to automatically classify different types of steel weld defects, including lack of the fusion, porosity, slag inclusion, and the qualified (no defects) cases. This methodology solves the shortcomings of existing detection methods, such as expensive equipment, complicated operation and inability to detect internal defects. The study first collected percussed data from welded steel members with or without weld defects. Then, three methods, the Mel frequency cepstral coefficients, short-time Fourier transform (STFT), and continuous wavelet transform were implemented and compared to explore the most appropriate features for classification of weld statuses. Classic and convolutional neural network-enhanced algorithms were used to classify, the extracted features. Furthermore, experiments were designed and performed to validate the proposed method. Results showed that STFT achieved higher accuracies (up to 96.63% on average) in the weld status classification. The convolutional neural network-enhanced support vector machine (SVM) outperformed six other algorithms with an average accuracy of 95.8%. In addition, random forest and SVM were efficient approaches with a balanced trade-off between the accuracies and the computational efforts.

关键词： steel weld     machine learning     convolutional neural network     weld defect detection     classification task     percussion    

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A new spatiotemporal convolutional neural network model for short-term crash prediction

《工程管理前沿（英文）》 doi: 10.1007/s42524-024-4040-8

摘要： Predicting short-term traffic crashes is challenging due to an imbalanced data set characterized by excessive zeros in noncrash counts, random crash occurrences, spatiotemporal correlation in crash counts, and inherent heterogeneity. Existing models struggle to effectively address these distinct characteristics in crash data. This paper proposes a new joint model by combining the time-series generalized regression neural network (TGRNN) model and the binomially weighted convolutional neural network (BWCNN) model. The joint model aims to capture all these characteristics in short-term crash prediction. The model was trained and tested using real-world, highly disaggregated traffic data collected with inductive loop detectors on the M1 motorway in the UK in 2019, along with crash data extracted from the UK National Accident Database for the same year. The short-term is defined as a 30-min interval, providing sufficient time for a traffic control center to implement interventions and mitigate potential hazards. The year was segmented into 30-min intervals, resulting in a highly imbalanced data set with over 99.99% noncrash samples. The joint model was applied to predict the probability of a crash occurrence by updating both the crash and traffic data every 30 min. The findings revealed that 75.3% of crashes and 81.6% of noncrash events were correctly predicted in the southbound direction. In the northbound direction, 78.1% of crashes and 80.2% of noncrash events were accurately captured. Causal analysis and model-based interpretation were used to analyze the relative importance of explanatory variables regarding their contribution to crashes. The results reveal that speed variance and speed are the most influential factors contributing to crash occurrence.

关键词： safety management     crash prediction     generalized regression neural network     binomial weighted CNN     variable importance    

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Online recognition of drainage type based on UV-vis spectra and derivative neural network algorithm

《环境科学与工程前沿（英文）》 2021年 第15卷 第6期 doi: 10.1007/s11783-021-1430-6

摘要：

• UV-vis absorption analyzer was applied in drainage type online recognition.

关键词： Drainage online recognition     UV-vis spectra     Derivative spectrum     Convolutional neural network    

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A neural network-based production process modeling and variable importance analysis approach in corn

《化学科学与工程前沿（英文）》 2023年 第17卷 第3期   页码 358-371 doi: 10.1007/s11705-022-2190-y

摘要： Corn to sugar process has long faced the risks of high energy consumption and thin profits. However, it’s hard to upgrade or optimize the process based on mechanism unit operation models due to the high complexity of the related processes. Big data technology provides a promising solution as its ability to turn huge amounts of data into insights for operational decisions. In this paper, a neural network-based production process modeling and variable importance analysis approach is proposed for corn to sugar processes, which contains data preprocessing, dimensionality reduction, multilayer perceptron/convolutional neural network/recurrent neural network based modeling and extended weights connection method. In the established model, dextrose equivalent value is selected as the output, and 654 sites from the DCS system are selected as the inputs. LASSO analysis is first applied to reduce the data dimension to 155, then the inputs are dimensionalized to 50 by means of genetic algorithm optimization. Ultimately, variable importance analysis is carried out by the extended weight connection method, and 20 of the most important sites are selected for each neural network. The results indicate that the multilayer perceptron and recurrent neural network models have a relative error of less than 0.1%, which have a better prediction result than other models, and the 20 most important sites selected have better explicable performance. The major contributions derived from this work are of significant aid in process simulation model with high accuracy and process optimization based on the selected most important sites to maintain high quality and stable production for corn to sugar processes.

关键词： big data     corn to sugar factory     neural network     variable importance analysis    

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PID neural network control of a membrane structure inflation system

Qiushuang LIU, Xiaoli XU

《机械工程前沿（英文）》 2010年 第5卷 第4期   页码 418-422 doi: 10.1007/s11465-010-0117-7

摘要： Because it is difficult for the traditional PID algorithm for nonlinear time-variant control objects to obtain satisfactory control results, this paper studies a neuron PID controller. The neuron PID controller makes use of neuron self-learning ability, complies with certain optimum indicators, and automatically adjusts the parameters of the PID controller and makes them adapt to changes in the controlled object and the input reference signals. The PID controller is used to control a nonlinear time-variant membrane structure inflation system. Results show that the neural network PID controller can adapt to the changes in system structure parameters and fast track the changes in the input signal with high control precision.

关键词： PID     neural network     membrane structure    

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A new neural-network-based method for structural damage identification in single-layer reticulated shells

《结构与土木工程前沿（英文）》 2024年 第18卷 第1期   页码 104-121 doi: 10.1007/s11709-024-1031-0

摘要： Single-layer reticulated shells (SLRSs) find widespread application in the roofs of crucial public structures, such as gymnasiums and exhibition center. In this paper, a new neural-network-based method for structural damage identification in SLRSs is proposed. First, a damage vector index, NDL, that is related only to the damage localization, is proposed for SLRSs, and a damage data set is constructed from NDL data. On the basis of visualization of the NDL damage data set, the structural damaged region locations are identified using convolutional neural networks (CNNs). By cross-dividing the damaged region locations and using parallel CNNs for each regional location, the damaged region locations can be quickly and efficiently identified and the undamaged region locations can be eliminated. Second, a damage vector index, DS, that is related to the damage location and damage degree, is proposed for SLRSs. Based on the damaged region identified previously, a fully connected neural network (FCNN) is constructed to identify the location and damage degree of members. The effectiveness and reliability of the proposed method are verified by considering a numerical case of a spherical SLRS. The calculation results showed that the proposed method can quickly eliminate candidate locations of potential damaged region locations and precisely determine the location and damage degree of members.

关键词： single-layer reticulated shell     damage identification     neural network     convolutional neural network     cross-partitioning method    

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Fault diagnosis of axial piston pumps with multi-sensor data and convolutional neural network

《机械工程前沿（英文）》 2022年 第17卷 第3期 doi: 10.1007/s11465-022-0692-4

摘要： Axial piston pumps have wide applications in hydraulic systems for power transmission. Their condition monitoring and fault diagnosis are essential in ensuring the safety and reliability of the entire hydraulic system. Vibration and discharge pressure signals are two common signals used for the fault diagnosis of axial piston pumps because of their sensitivity to pump health conditions. However, most of the previous fault diagnosis methods only used vibration or pressure signal, and literatures related to multi-sensor data fusion for the pump fault diagnosis are limited. This paper presents an end-to-end multi-sensor data fusion method for the fault diagnosis of axial piston pumps. The vibration and pressure signals under different pump health conditions are fused into RGB images and then recognized by a convolutional neural network. Experiments were performed on an axial piston pump to confirm the effectiveness of the proposed method. Results show that the proposed multi-sensor data fusion method greatly improves the fault diagnosis of axial piston pumps in terms of accuracy and robustness and has better diagnostic performance than other existing diagnosis methods.

关键词： axial piston pump     fault diagnosis     convolutional neural network     multi-sensor data fusion    

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Machine learning and neural network supported state of health simulation and forecasting model for lithium-ion

《能源前沿（英文）》 2024年 第18卷 第2期   页码 223-240 doi: 10.1007/s11708-023-0891-7

摘要： As the intersection of disciplines deepens, the field of battery modeling is increasingly employing various artificial intelligence (AI) approaches to improve the efficiency of battery management and enhance the stability and reliability of battery operation. This paper reviews the value of AI methods in lithium-ion battery health management and in particular analyses the application of machine learning (ML), one of the many branches of AI, to lithium-ion battery state of health (SOH), focusing on the advantages and strengths of neural network (NN) methods in ML for lithium-ion battery SOH simulation and prediction. NN is one of the important branches of ML, in which the application of NNs such as backpropagation NN, convolutional NN, and long short-term memory NN in SOH estimation of lithium-ion batteries has received wide attention. Reports so far have shown that the utilization of NN to model the SOH of lithium-ion batteries has the advantages of high efficiency, low energy consumption, high robustness, and scalable models. In the future, NN can make a greater contribution to lithium-ion battery management by, first, utilizing more field data to play a more practical role in health feature screening and model building, and second, by enhancing the intelligent screening and combination of battery parameters to characterize the actual lithium-ion battery SOH to a greater extent. The in-depth application of NN in lithium-ion battery SOH will certainly further enhance the science, reliability, stability, and robustness of lithium-ion battery management.

关键词： machine learning     lithium-ion battery     state of health     neural network     artificial intelligence    

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Deep convolutional tree-inspired network: a decision-tree-structured neural network for hierarchical

《机械工程前沿（英文）》 2021年 第16卷 第4期   页码 814-828 doi: 10.1007/s11465-021-0650-6

摘要： The fault diagnosis of bearings is crucial in ensuring the reliability of rotating machinery. Deep neural networks have provided unprecedented opportunities to condition monitoring from a new perspective due to the powerful ability in learning fault-related knowledge. However, the inexplicability and low generalization ability of fault diagnosis models still bar them from the application. To address this issue, this paper explores a decision-tree-structured neural network, that is, the deep convolutional tree-inspired network (DCTN), for the hierarchical fault diagnosis of bearings. The proposed model effectively integrates the advantages of convolutional neural network (CNN) and decision tree methods by rebuilding the output decision layer of CNN according to the hierarchical structural characteristics of the decision tree, which is by no means a simple combination of the two models. The proposed DCTN model has unique advantages in 1) the hierarchical structure that can support more accuracy and comprehensive fault diagnosis, 2) the better interpretability of the model output with hierarchical decision making, and 3) more powerful generalization capabilities for the samples across fault severities. The multiclass fault diagnosis case and cross-severity fault diagnosis case are executed on a multicondition aeronautical bearing test rig. Experimental results can fully demonstrate the feasibility and superiority of the proposed method.

关键词： bearing     cross-severity fault diagnosis     hierarchical fault diagnosis     convolutional neural network     decision tree    

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A deep neural network based surrogate model for damage identification in full-scale structures with incomplete

《结构与土木工程前沿（英文）》 2024年 第18卷 第3期   页码 393-410 doi: 10.1007/s11709-024-1060-8

摘要： The paper introduces a novel approach for detecting structural damage in full-scale structures using surrogate models generated from incomplete modal data and deep neural networks (DNNs). A significant challenge in this field is the limited availability of measurement data for full-scale structures, which is addressed in this paper by generating data sets using a reduced finite element (FE) model constructed by SAP2000 software and the MATLAB programming loop. The surrogate models are trained using response data obtained from the monitored structure through a limited number of measurement devices. The proposed approach involves training a single surrogate model that can quickly predict the location and severity of damage for all potential scenarios. To achieve the most generalized surrogate model, the study explores different types of layers and hyperparameters of the training algorithm and employs state-of-the-art techniques to avoid overfitting and to accelerate the training process. The approach’s effectiveness, efficiency, and applicability are demonstrated by two numerical examples. The study also verifies the robustness of the proposed approach on data sets with sparse and noisy measured data. Overall, the proposed approach is a promising alternative to traditional approaches that rely on FE model updating and optimization algorithms, which can be computationally intensive. This approach also shows potential for broader applications in structural damage detection.

关键词： vibration-based damage detection     deep neural network     full-scale structures     finite element model updating     noisy incomplete modal data    

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标题 作者 时间 类型 操作

Multiscale computation on feedforward neural network and recurrent neural network

Bin LI, Xiaoying ZHUANG

期刊论文

Crack identification in concrete, using digital image correlation and neural network

期刊论文

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

期刊论文

Novel interpretable mechanism of neural networks based on network decoupling method

期刊论文

Deep convolutional neural network for multi-level non-invasive tunnel lining assessment

期刊论文

Automated identification of steel weld defects, a convolutional neural network improved machine learning

期刊论文

A new spatiotemporal convolutional neural network model for short-term crash prediction

期刊论文

Online recognition of drainage type based on UV-vis spectra and derivative neural network algorithm

期刊论文

A neural network-based production process modeling and variable importance analysis approach in corn

期刊论文

PID neural network control of a membrane structure inflation system

Qiushuang LIU, Xiaoli XU

期刊论文

A new neural-network-based method for structural damage identification in single-layer reticulated shells

期刊论文

Fault diagnosis of axial piston pumps with multi-sensor data and convolutional neural network

期刊论文

Machine learning and neural network supported state of health simulation and forecasting model for lithium-ion

期刊论文

Deep convolutional tree-inspired network: a decision-tree-structured neural network for hierarchical

期刊论文

A deep neural network based surrogate model for damage identification in full-scale structures with incomplete

期刊论文