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

《能源前沿（英文）》 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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A constrained neural network model for soil liquefaction assessment with global applicability

Yifan ZHANG, Rui WANG, Jian-Min ZHANG, Jianhong ZHANG

《结构与土木工程前沿（英文）》 2020年 第14卷 第5期   页码 1066-1082 doi: 10.1007/s11709-020-0651-2

摘要： A constrained back propagation neural network (C-BPNN) model for standard penetration test based soil liquefaction assessment with global applicability is developed, incorporating existing knowledge for liquefaction triggering mechanism and empirical relationships. For its development and validation, a comprehensive liquefaction data set is compiled, covering more than 600 liquefaction sites from 36 earthquakes in 10 countries over 50 years with 13 complete information entries. The C-BPNN model design procedure for liquefaction assessment is established by considering appropriate constraints, input data selection, and computation and calibration procedures. Existing empirical relationships for overburden correction and fines content adjustment are shown to be able to improve the prediction success rate of the neural network model, and are thus adopted as constraints for the C-BPNN model. The effectiveness of the C-BPNN method is validated using the liquefaction data set and compared with that of several liquefaction assessment methods currently adopted in engineering practice. The C-BPNN liquefaction model is shown to have improved prediction accuracy and high global adaptability.

关键词： soil liquefaction assessment     case history dataset     constrained neural network model     existing knowledge    

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Development of deep neural network model to predict the compressive strength of FRCM confined columns

Khuong LE-NGUYEN; Quyen Cao MINH; Afaq AHMAD; Lanh Si HO

《结构与土木工程前沿（英文）》 2022年 第16卷 第10期   页码 1213-1232 doi: 10.1007/s11709-022-0880-7

摘要： The present study describes a reliability analysis of the strength model for predicting concrete columns confinement influence with Fabric-Reinforced Cementitious Matrix (FRCM). through both physical models and Deep Neural Network model (artificial neural network (ANN) with double and triple hidden layers). The database of 330 samples collected for the training model contains many important parameters, i.e., section type (circle or square), corner radius rc, unconfined concrete strength fco, thickness nt, the elastic modulus of fiber Ef , the elastic modulus of mortar Em. The results revealed that the proposed ANN models well predicted the compressive strength of FRCM with high prediction accuracy. The ANN model with double hidden layers (APDL-1) was shown to be the best to predict the compressive strength of FRCM confined columns compared with the ACI design code and five physical models. Furthermore, the results also reveal that the unconfined compressive strength of concrete, type of fiber mesh for FRCM, type of section, and the corner radius ratio, are the most significant input variables in the efficiency of FRCM confinement prediction. The performance of the proposed ANN models (including double and triple hidden layers) had high precision with R higher than 0.93 and RMSE smaller than 0.13, as compared with other models from the literature available.

关键词： FRCM     deep neural networks     confinement effect     strength model     confined concrete    

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Modeling oblique load carrying capacity of batter pile groups using neural network, random forest regressionand M5 model tree

Tanvi SINGH, Mahesh PAL, V. K. ARORA

《结构与土木工程前沿（英文）》 2019年 第13卷 第3期   页码 674-685 doi: 10.1007/s11709-018-0505-3

摘要： M5 model tree, random forest regression (RF) and neural network (NN) based modelling approaches were used to predict oblique load carrying capacity of batter pile groups using 247 laboratory experiments with smooth and rough pile groups. Pile length ( ), angle of oblique load ( ), sand density ( ), number of batter piles ( ), and number of vertical piles ( ) as input and oblique load ( ) as output was used. Results suggest improved performance by RF regression for both pile groups. M5 model tree provides simple linear relation which can be used for the prediction of oblique load for field data also. Model developed using RF regression approach with smooth pile group data was found to be in good agreement for rough piles data. NN based approach was found performing equally well with both smooth and rough piles. Sensitivity analysis using all three modelling approaches suggest angle of oblique load ( ) and number of batter pile ( ) affect the oblique load capacity for both smooth and rough pile groups.

关键词： batter piles     oblique load test     neural network     M5 model tree     random forest regression     ANOVA    

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A multi-sensor relation model for recognizing and localizing faults of machines based on network analysis

《机械工程前沿（英文）》 2023年 第18卷 第2期 doi: 10.1007/s11465-022-0736-9

摘要： Recently, advanced sensing techniques ensure a large number of multivariate sensing data for intelligent fault diagnosis of machines. Given the advantage of obtaining accurate diagnosis results, multi-sensor fusion has long been studied in the fault diagnosis field. However, existing studies suffer from two weaknesses. First, the relations of multiple sensors are either neglected or calculated only to improve the diagnostic accuracy of fault types. Second, the localization for multi-source faults is seldom investigated, although locating the anomaly variable over multivariate sensing data for certain types of faults is desirable. This article attempts to overcome the above weaknesses by proposing a global method to recognize fault types and localize fault sources with the help of multi-sensor relations (MSRs). First, an MSR model is developed to learn MSRs automatically and further obtain fault recognition results. Second, centrality measures are employed to analyze the MSR graphs learned by the MSR model, and fault sources are therefore determined. The proposed method is demonstrated by experiments on an induction motor and a centrifugal pump. Results show the proposed method’s validity in diagnosing fault types and sources.

关键词： fault recognition     fault localization     multi-sensor relations     network analysis     graph neural network    

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Optimal design of double-layer barrel vaults using genetic and pattern search algorithms and optimized neuralnetwork as surrogate model

《结构与土木工程前沿（英文）》 2023年 第17卷 第3期   页码 378-395 doi: 10.1007/s11709-022-0899-9

摘要： This paper presents a combined method based on optimized neural networks and optimization algorithms to solve structural optimization problems. The main idea is to utilize an optimized artificial neural network (OANN) as a surrogate model to reduce the number of computations for structural analysis. First, the OANN is trained appropriately. Subsequently, the main optimization problem is solved using the OANN and a population-based algorithm. The algorithms considered in this step are the arithmetic optimization algorithm (AOA) and genetic algorithm (GA). Finally, the abovementioned problem is solved using the optimal point obtained from the previous step and the pattern search (PS) algorithm. To evaluate the performance of the proposed method, two numerical examples are considered. In the first example, the performance of two algorithms, OANN + AOA + PS and OANN + GA + PS, is investigated. Using the GA reduces the elapsed time by approximately 50% compared with using the AOA. Results show that both the OANN + GA + PS and OANN + AOA + PS algorithms perform well in solving structural optimization problems and achieve the same optimal design. However, the OANN + GA + PS algorithm requires significantly fewer function evaluations to achieve the same accuracy as the OANN + AOA + PS algorithm.

关键词： optimization     surrogate models     artificial neural network     SAP2000     genetic algorithm    

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An artificial neural network model on tensile behavior of hybrid steel-PVA fiber reinforced concrete

Fangyu LIU, Wenqi DING, Yafei QIAO, Linbing WANG

《结构与土木工程前沿（英文）》 2020年 第14卷 第6期   页码 1299-1315 doi: 10.1007/s11709-020-0712-6

摘要： The tensile behavior of hybrid fiber reinforced concrete (HFRC) is important to the design of HFRC and HFRC structure. This study used an artificial neural network (ANN) model to describe the tensile behavior of HFRC. This ANN model can describe well the tensile stress-strain curve of HFRC with the consideration of 23 features of HFRC. In the model, three methods to process output features (no-processed, mid-processed, and processed) are discussed and the mid-processed method is recommended to achieve a better reproduction of the experimental data. This means the strain should be normalized while the stress doesn’t need normalization. To prepare the database of the model, both many direct tensile test results and the relevant literature data are collected. Moreover, a traditional equation-based model is also established and compared with the ANN model. The results show that the ANN model has a better prediction than the equation-based model in terms of the tensile stress-strain curve, tensile strength, and strain corresponding to tensile strength of HFRC. Finally, the sensitivity analysis of the ANN model is also performed to analyze the contribution of each input feature to the tensile strength and strain corresponding to tensile strength. The mechanical properties of plain concrete make the main contribution to the tensile strength and strain corresponding to tensile strength, while steel fibers tend to make more contributions to these two items than PVA fibers.

关键词： artificial neural network     hybrid fiber reinforced concrete     tensile behavior     sensitivity analysis     stress-strain curve    

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Two-phase early prediction method for remaining useful life of lithium-ion batteries based on a neuralnetwork and Gaussian process regression

《能源前沿（英文）》 doi: 10.1007/s11708-023-0906-4

摘要： Lithium-ion batteries (LIBs) are widely used in transportation, energy storage, and other fields. The prediction of the remaining useful life (RUL) of lithium batteries not only provides a reference for health management but also serves as a basis for assessing the residual value of the battery. In order to improve the prediction accuracy of the RUL of LIBs, a two-phase RUL early prediction method combining neural network and Gaussian process regression (GPR) is proposed. In the initial phase, the features related to the capacity degradation of LIBs are utilized to train the neural network model, which is used to predict the initial cycle lifetime of 124 LIBs. The Pearson coefficient’s two most significant characteristic factors and the predicted normalized lifetime form a 3D space. The Euclidean distance between the test dataset and each cell in the training dataset and validation dataset is calculated, and the shortest distance is considered to have a similar degradation pattern, which is used to determine the initial Dual Exponential Model (DEM). In the second phase, GPR uses the DEM as the initial parameter to predict each test set’s early RUL (ERUL). By testing four batteries under different working conditions, the RMSE of all capacity estimation is less than 1.2%, and the accuracy percentage (AP) of remaining life prediction is more than 98%. Experiments show that the method does not need human intervention and has high prediction accuracy.

关键词： lithium-ion batteries     RUL prediction     double exponential model     neural network     Gaussian process regression (GPR)    

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A hybrid Wavelet-CNN-LSTM deep learning model for short-term urban water demand forecasting

《环境科学与工程前沿（英文）》 2023年 第17卷 第2期 doi: 10.1007/s11783-023-1622-3

摘要：

● A novel deep learning framework for short-term water demand forecasting.

关键词： Short-term water demand forecasting     Long-short term memory neural network     Convolutional Neural Network     Wavelet multi-resolution analysis     Data-driven models    

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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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基于人工神经网络的多处损伤加筋板剩余强度预测

杨茂胜,陈跃良,郁大照

《中国工程科学》 2008年 第10卷 第5期   页码 46-50

摘要：

用BP神经网络算法对多处损伤加筋板的剩余强度数据进行训练学习，将预测值和3种经典分析方法的计算值与实验值进行对比，结果表明，ANN法预测值与实验值吻合得最好，LMC修正法和WSU3修正法次之，Swift塑性区连通法最差。最后用所建立的BP网络对不同主裂纹半长和韧带长度的剩余强度进行了预测，结果发现，在其他参数不变的情况下，不管是双筋条还是三筋条加筋板，剩余强度总是随主裂纹半长的增加而成线性降低，随韧带长度的增加而成线性增加，但双筋条加筋板比三筋条加筋板对主裂纹半长和韧带长度的变化更加敏感。

关键词： 神经网络     多处损伤     加筋板     剩余强度    

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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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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

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

期刊论文

A constrained neural network model for soil liquefaction assessment with global applicability

Yifan ZHANG, Rui WANG, Jian-Min ZHANG, Jianhong ZHANG

期刊论文

Development of deep neural network model to predict the compressive strength of FRCM confined columns

Khuong LE-NGUYEN; Quyen Cao MINH; Afaq AHMAD; Lanh Si HO

期刊论文

Modeling oblique load carrying capacity of batter pile groups using neural network, random forest regressionand M5 model tree

Tanvi SINGH, Mahesh PAL, V. K. ARORA

期刊论文

A multi-sensor relation model for recognizing and localizing faults of machines based on network analysis

期刊论文

Optimal design of double-layer barrel vaults using genetic and pattern search algorithms and optimized neuralnetwork as surrogate model

期刊论文

An artificial neural network model on tensile behavior of hybrid steel-PVA fiber reinforced concrete

Fangyu LIU, Wenqi DING, Yafei QIAO, Linbing WANG

期刊论文

Two-phase early prediction method for remaining useful life of lithium-ion batteries based on a neuralnetwork and Gaussian process regression

期刊论文

A hybrid Wavelet-CNN-LSTM deep learning model for short-term urban water demand forecasting

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

基于人工神经网络的多处损伤加筋板剩余强度预测

杨茂胜,陈跃良,郁大照

期刊论文

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

期刊论文