Optimizing ANN models with PSO for predicting short building seismic response

Nguyen, Hoang; Moayedi, Hossein; Foong, Loke Kok; Najjar, Husam Abdulrasool H. Al; Jusoh, Wan Amizah Wan; Rashid, Ahmad Safuan A.; Jamali, Jamaloddin

doi:10.1007/s00366-019-00733-0

Cited by 117 publications

(38 citation statements)

References 56 publications

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“…During the search process, they share information and experience to update better locations [44]. Thus, it was also considered as an evolutionary computation technique in the statistical community [44][45][46][47][48][49]. The PSO algorithm implements five steps for optimal searching: -Step 1: Initialize the aboriginal population and velocity of particles.…”

Section: Particle Swarm Optimization (Pso) Algorithmmentioning

confidence: 99%

Estimating the Heating Load of Buildings for Smart City Planning Using a Novel Artificial Intelligence Technique PSO-XGBoost

et al. 2019

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In this study, a novel technique to support smart city planning in estimating and controlling the heating load (HL) of buildings, was proposed, namely PSO-XGBoost. Accordingly, the extreme gradient boosting machine (XGBoost) was developed to estimate HL first; then, the particle swarm optimization (PSO) algorithm was applied to optimize the performance of the XGBoost model. The classical XGBoost model, support vector machine (SVM), random forest (RF), Gaussian process (GP), and classification and regression trees (CART) models were also investigated and developed to predict the HL of building systems, and compared with the proposed PSO-XGBoost model; 837 investigations of buildings were considered and analyzed with many influential factors, such as glazing area distribution (GAD), glazing area (GA), orientation (O), overall height (OH), roof area (RA), wall area (WA), surface area (SA), and relative compactness (RC). Mean absolute percentage error (MAPE), root-mean-squared error (RMSE), variance account for (VAF), mean absolute error (MAE), and determination coefficient (R2), were used as the statistical criteria for evaluating the performance of the above models. The color intensity, as well as the ranking method, were also used to compare and evaluate the models. The results showed that the proposed PSO-XGBoost model was the most robust technique for estimating the HL of building systems. The remaining models (i.e., XGBoost, SVM, RF, GP, and CART) yielded more mediocre performance through RMSE, MAE, R2, VAF, and MAPE metrics. Another finding of this study also indicated that OH, RA, WA, and SA were the most critical parameters for the accuracy of the proposed PSO-XGBoost model. They should be particularly interested in smart city planning as well as the optimization of smart cities.

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Section: Particle Swarm Optimization (Pso) Algorithmmentioning

confidence: 99%

Estimating the Heating Load of Buildings for Smart City Planning Using a Novel Artificial Intelligence Technique PSO-XGBoost

et al. 2019

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“…It was introduced and developed by Eberhart and Kennedy [90] and classified as one of the metaheuristic techniques. It was considered as an evolutionary computation technique in the statistical community [41]. The PSO algorithm implements six steps for optimal searching as the following procedure.…”

Section: Particle Swarm Optimization (Pso) Algorithmmentioning

confidence: 99%

“…In recent years, quantitative models have been proven as effective methods to predict environmental issues and control atmospheric pollution. Artificial intelligence (AI) and its applications were considered as the robust tools for predicting and controlling environmental issues, especially in mining operations [28][29][30][31][32][33][34][35][36][37][38][39][40][41][42][43][44]. For estimating PM 10 concentration, Chelani and Gajghate [45] used an artificial neural network (ANN) based on the back-propagation (BP) algorithm to predict PM 10 concentration.…”

Section: Introductionmentioning

confidence: 99%

Estimating PM10 Concentration from Drilling Operations in Open-Pit Mines Using an Assembly of SVR and PSO

et al. 2019

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Dust is one of the components causing heavy environmental pollution in open-pit mines, especially PM10. Some pathologies related to the lung, respiratory system, and occupational diseases have been identified due to the effects of PM10 in open-pit mines. Therefore, the prediction and control of PM10 concentration in the production process are necessary for environmental and health protection. In this study, PM10 concentration from drilling operations in the Coc Sau open-pit coal mine (Vietnam) was investigated and considered through a database including 245 datasets collected. A novel hybrid artificial intelligence model was developed based on support vector regression (SVR) and a swarm optimization algorithm (i.e., particle swarm optimization (PSO)), namely PSO-SVR, for estimating PM10 concentration from drilling operations at the mine. Polynomial (P), radial basis function (RBF), and linear (L) kernel functions were considered and applied to the development of the PSO-SVR models in the present study, abbreviated as PSO-SVR-P, PSO-SVR-RBF, and PSO-SVR-L. Also, three benchmark artificial intelligence techniques, such as k-nearest neighbors (KNN), random forest (RF), and classification and regression trees (CART), were applied and developed for estimating PM10 concentration and then compared with the PSO-SVR models. Root-mean-squared error (RMSE) and determination coefficient (R2) were used as the statistical criteria for evaluating the performance of the developed models. The results exhibited that the PSO algorithm had an essential role in the optimization of the hyper-parameters of the SVR models. The PSO-SVR models (i.e., PSO-SVR-L, PSO-SVR-P, and PSO-SVR-RBF) had higher performance levels than the other models (i.e., RF, CART, and KNN) with an RMSE of 0.040, 0.042, and 0.043; and R2 of 0.954, 0.948, and 0.946; for the PSO-SVR-L, PSO-SVR-P, and PSO-SVR-RBF models, respectively. Of these PSO-SVR models, the PSO-SVR-L model was the most dominant model with an RMSE of 0.040 and R2 of 0.954. The remaining three benchmark models (i.e., RF, CART, and KNN) yielded a more unsatisfactory performance with an RMSE of 0.060, 0.052, and 0.067; and R2 of 0.894, 0.924, and 0.867, for the RF, CART, and KNN models, respectively. Furthermore, the findings of this study demonstrated that the density of rock mass, moisture content, and the penetration rate of the drill were essential parameters on the PM10 concentration caused by drilling operations in open-pit mines.

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“…In this regard, artificial intelligence (AI) applications are considered useful, not only as robust techniques in the mining field but also in many other areas (e.g., civil engineering, fuel, and energy, and environment) [2,4,[6][7][8][9][10][11][12][13][14][15][16][17][18][19][20][21][22][23][24]. An overview of the literature related to PPV prediction showed that many AI models have been developed and proposed, as listed in Table 1.…”

Section: Introductionmentioning

confidence: 99%

Predicting Blast-Induced Ground Vibration in Open-Pit Mines Using Vibration Sensors and Support Vector Regression-Based Optimization Algorithms

Nguyen

Choi

Bui

et al. 2019

Sensors

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In this study, vibration sensors were used to measure blast-induced ground vibration (PPV). Different evolutionary algorithms were assessed for predicting PPV, including the particle swarm optimization (PSO) algorithm, genetic algorithm (GA), imperialist competitive algorithm (ICA), and artificial bee colony (ABC). These evolutionary algorithms were used to optimize the support vector regression (SVR) model. They were abbreviated as the PSO-SVR, GA-SVR, ICA-SVR, and ABC-SVR models. For each evolutionary algorithm, three forms of kernel function, linear (L), radial basis function (RBF), and polynomial (P), were investigated and developed. In total, 12 new hybrid models were developed for predicting PPV in this study, named ABC-SVR-P, ABC-SVR-L, ABC-SVR-RBF, PSO-SVR-P, PSO-SVR-L, PSO-SVR-RBF, ICA-SVR-P, ICA-SVR-L, ICA-SVR-RBF, GA-SVR-P, GA-SVR-L and GA-SVR-RBF. There were 125 blasting results gathered and analyzed at a limestone quarry in Vietnam. Statistical criteria like R2, RMSE, and MAE were used to compare and evaluate the developed models. Ranking and color intensity methods were also applied to enable a more complete evaluation. The results revealed that GA was the most dominant evolutionary algorithm for the current problem when combined with the SVR model. The RBF was confirmed as the best kernel function for the GA-SVR model. The GA-SVR-RBF model was proposed as the best technique for PPV estimation.

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Optimizing ANN models with PSO for predicting short building seismic response

Cited by 117 publications

References 56 publications

Estimating the Heating Load of Buildings for Smart City Planning Using a Novel Artificial Intelligence Technique PSO-XGBoost

Estimating the Heating Load of Buildings for Smart City Planning Using a Novel Artificial Intelligence Technique PSO-XGBoost

Estimating PM10 Concentration from Drilling Operations in Open-Pit Mines Using an Assembly of SVR and PSO

Predicting Blast-Induced Ground Vibration in Open-Pit Mines Using Vibration Sensors and Support Vector Regression-Based Optimization Algorithms

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