Statistical model optimized random forest regression model for concrete dam deformation monitoring

Dai, Bo; Gu, Chongshi; Zhao, Erfeng; Qin, Xiangnan

doi:10.1002/stc.2170

Cited by 133 publications

(61 citation statements)

References 19 publications

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“…(3) Four supervised learning methods (DT, GP, MLR, and SVM) and a metaheuristic optimization algorithm (PSO) were used for nonlinear regression modeling in this paper. Other advanced MLAs can be introduced to predict the ultimate axial capacity, such as multilayer perceptron [65,66] and random forest [67][68][69]. Some new optimization algorithms can also be used to improve model performance while reducing the operation time.…”

Section: Discussion: Future Model Improvementsmentioning

confidence: 99%

Prediction of Ultimate Axial Capacity of Square Concrete-Filled Steel Tubular Short Columns Using a Hybrid Intelligent Algorithm

Ren

Zhang

et al. 2019

Applied Sciences

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It is crucial to study the axial compression behavior of concrete-filled steel tubular (CFST) columns to ensure the safe operation of engineering structures. The restriction between steel tubular and core concrete in CFSTs is complex and the relationship between geometric and material properties and axial compression behavior is highly nonlinear. These challenges have prompted the use of soft computing methods to predict the ultimate bearing capacity (abbreviated as Nu) under axial compression. Taking the square CFST short column as an example, a mass of experimental data is obtained through axial compression tests. Combined with support vector machine (SVM) and particle swarm optimization (PSO), this paper presents a new method termed PSVM (SVM optimized by PSO) for Nu value prediction. The nonlinear relationship in Nu value prediction is efficiently represented by SVM, and PSO is used to select the model parameters of SVM. The experimental dataset is utilized to verify the reliability of the PSVM model, and the prediction performance of PSVM is compared with that of traditional design methods and other benchmark models. The proposed PSVM model provides a better prediction of the ultimate axial capacity of square CFST short columns. As such, PSVM is an efficient alternative method other than empirical and theoretical formulas.

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Section: Discussion: Future Model Improvementsmentioning

confidence: 99%

Prediction of Ultimate Axial Capacity of Square Concrete-Filled Steel Tubular Short Columns Using a Hybrid Intelligent Algorithm

Ren

Zhang

et al. 2019

Applied Sciences

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“…And the hydraulic displacement component 1 ( ), temperature displacement component 2 ( ), and aging displacement component 3 ( ) can expressed as follows [19]:…”

Section: Statistical Modelmentioning

confidence: 99%

An Empirical Comparison of Multiple Linear Regression and Artificial Neural Network for Concrete Dam Deformation Modelling

Wang

2019

Mathematical Problems in Engineering

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Deformation predicting models are essential for evaluating the health status of concrete dams. Nevertheless, the application of the conventional multiple linear regression model has been limited due to the particular structure, random loading, and strong nonlinear deformation of concrete dams. Conversely, the artificial neural network (ANN) model shows good adaptability to complex and highly nonlinear behaviors. This paper aims to evaluate the specific performance of the multiple linear regression (MLR) and artificial neural network (ANN) model in characterizing concrete dam deformation under environmental loads. In this study, four models, namely, the multiple linear regression (MLR), stepwise regression (SR), backpropagation (BP) neural network, and extreme learning machine (ELM) model, are employed to simulate dam deformation from two aspects: single measurement point and multiple measurement points, approximately 11 years of historical dam operation records. Results showed that the prediction accuracy of the multipoint model was higher than that of the single point model except the MLR model. Moreover, the prediction accuracy of the ELM model was always higher than the other three models. All discussions would be conducted in conjunction with a gravity dam study.

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“…Most studies are defined in a deterministic setting and regressive methods are used to calibrate dam predictive models. Different approaches have been proposed to improve the performance of predictive models: the hybrid simplex artificial bee colony algorithm (HSABCA) [22], boosted regression trees [23], multilevel-recursive method [17], dynamic time warping (DTW) method, local outlier factor (LOF) [24], chaotic residual errors [19], Random Forest Regression (RFR) [25] and Bayesian inference [3] have been successfully applied in the scientific literature.…”

Section: Structural Health Monitoring Systems For Concrete Gravity Damsmentioning

confidence: 99%

Dynamic structural health monitoring for concrete gravity dams based on the Bayesian inference

Sevieri

Falco

2020

J Civil Struct Health Monit

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The preservation of concrete dams is a key issue for researchers and practitioners in dam engineering because of the important role played by these infrastructures in the sustainability of our society. Since most of existing concrete dams were designed without considering their dynamic behaviour, monitoring their structural health is fundamental in achieving proper safety levels. Structural Health Monitoring systems based on ambient vibrations are thus crucial. However, the high computational burden related to numerical models and the numerous uncertainties affecting the results have so far prevented structural health monitoring systems for concrete dams from being developed. This study presents a framework for the dynamic structural health monitoring of concrete gravity dams in the Bayesian setting. The proposed approach has a relatively low computational burden, and detects damage and reduces uncertainties in predicting the structural behaviour of dams, thus improving the reliability of the structural health monitoring system itself. The application of the proposed procedure to an Italian concrete gravity dam demonstrates its feasibility in real cases.

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Statistical model optimized random forest regression model for concrete dam deformation monitoring

Cited by 133 publications

References 19 publications

Prediction of Ultimate Axial Capacity of Square Concrete-Filled Steel Tubular Short Columns Using a Hybrid Intelligent Algorithm

Prediction of Ultimate Axial Capacity of Square Concrete-Filled Steel Tubular Short Columns Using a Hybrid Intelligent Algorithm

An Empirical Comparison of Multiple Linear Regression and Artificial Neural Network for Concrete Dam Deformation Modelling

Dynamic structural health monitoring for concrete gravity dams based on the Bayesian inference

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