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

Abstract

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.

An Approach Using Adaptive Weighted Least Squares Support Vector Machines Coupled with Modified Ant Lion Optimizer for Dam Deformation Prediction

“…It also includes the autogenous volume deformation and the irreversible displacement caused by dam cracks. e mathematical model of the aging component can be written by equation (11), where θ � (t/100):…”

“…(3) Construct the LSSVM model by equation 21, evaluate the fitness values of the ants and antlions by equation 34; (4) Determine the antlion with the minimum fitness value as the elite antlion; (5) While t < t max do (6) t � t + 1; (7) For each ant (8) Select an antlion based on the fitness value using the Roulette wheel principle; (9) Update the lower and upper bounds by equation 38; (10) Calculate the bounds around the selected antlion by equation 37; (11) Determine R t A and R t E , the Levy flights around the selected antlion by equations (31) and 41; (12) Update the new state of the ants using equation 39 Step 1…”

“…To date, various powerful prediction models have been applied in complex nonlinearity and optimization problems in pump turbine characteristics identified [7], flood interval prediction [8], stock price prediction [9], and wind speed forecasting [10]. In the field of dam deformation prediction based on prototypical observations, many prediction models have also been established, such as multiple linear regression [11], neural network [5], support vector machines [12], extreme learning machine [13], boosted regression trees [14], and Gaussian process regression [15]. Nevertheless, the above-mentioned models are single prediction models, and the information mining scale is limited [16].…”

Dam monitoring data analysis methods: A literature review

“…In multiple linear regression, the output is computed as a linear combination of the inputs . Hence, it is multilineal regression models, so the least square method is used to estimate model parameters, and the model is directly established based on the environmental variables and effect variables . However, there are multiple environmental variables in statistical model.…”

Application of combined model of stepwise regression analysis and artificial neural network in data calibration of miniature air quality detector

“…They chose to use a three-layer feedforward ANN trained by the backpropagation algorithm and successfully repaired some of the data in the model 9 .Spinelle, L. et al compared linear/multilinear regression and supervised learning techniques, and carried out on-site calibration of NO, CO and CO 2 pollutant sensors 10 . However, both linear regression and artificial neural network have shortcomings in air quality prediction models 26 . In this paper, by combining the prediction effects of the two methods in the air quality forecast model data, a calibration model of the main pollutants in the air is given to improve the interpretability and accuracy of the air quality calibration model.…”