Applications of Particle Swarm Optimization in Geotechnical Engineering: A Comprehensive Review

Particle swarm optimization (PSO) is an evolutionary computation approach to solve nonlinear global optimization problems. The PSO idea was made based on simulation of a simplified social system, the graceful but unpredictable choreography of birds flock. This system is initialized with a population of random solutions that are updated during iterations. Over the last few years, PSO has been extensively applied in various geotechnical engineering aspects such as slope stability analysis, pile and foundation engineering, rock and soil mechanics, and tunneling and underground space design. A review on the literature shows that PSO has utilized more widely in geotechnical engineering compared with other civil engineering disciplines. This is due to comprehensive uncertainty and complexity of problems in geotechnical engineering which can be solved by using the PSO abilities in solving the complex and multi-dimensional problems. This paper provides a comprehensive review on the applicability, advantages and limitation of PSO in different disciplines of geotechnical engineering to provide an insight to an alternative and superior optimization method compared with the conventional optimization techniques for geotechnical engineers

Genetic programing and non-linear multiple regression techniques to predict backbreak in blasting operation

In addition to all benefits of blasting in mining and civil engineering applications, it has some undesirable environmental impacts. Backbreak is an unwanted phenomenon of blasting which can cause instability of mine walls, decreasing efficiency of drilling, falling down of machinery, etc. Recently, the use of new approaches such as artificial intelligence (AI) is greatly recommended by many researchers. In this paper, a new AI technique namely genetic programing (GP) was developed to predict BB. To prepare a sufficient database, 175 blasting works were investigated in Sungun copper mine, Iran. In these operations, the most influential parameters on BB including burden, spacing, stemming length, powder factor and stiffness ratio were measured and used to develop BB predictive models. To demonstrate capability of GP technique, a non-linear multiple regression (NLMR) model was also employed for prediction of BB. Value account for (VAF), root mean square error (RMSE) and coefficient of determination (R2) were used to control the capacity performance of the predictive models. The performance indices obtained by GP approach indicate the higher reliability of GP compared to NLMR model. RMSE and VAF values of 0.327 and 97.655, respectively, for testing datasets of GP approach reveal the superiority of this model in predicting BB, while these values were obtained as 0.865 and 81.816, respectively, for NLMR model

Development of a new model for predicting flyrock distance in quarry blasting: a genetic programming technique

This research was aimed at developing a new model to predict flyrock distance based on a genetic programming (GP) technique. For this purpose, six granite quarry mines in the Johor area of Malaysia were investigated, for which various controllable blasting parameters were recorded. A total of 262 datasets consisting of six variables (i.e., powder factor, stemming length, burden-to-spacing ratio, blast-hole diameter, maximum charge per delay, and blast-hole depth) were collected applied to developing the flyrock predictive model. To identify the optimum model, several GP models were developed to predict flyrock. In the same way, using non-linear multiple regression (NLMR) analysis, various models were established to predict flyrock. Finally, to compare the performance of the developed models, regression coefficient (R2), root mean square error (RMSE), variance account for (VAF), and simple ranking methods were computed. According to the results obtained from the test dataset, the best flyrock predictive model was found to be the GP based model, with R2Â =Â 0.908, RMSEÂ =Â 17.638 and VAFÂ =Â 89.917, while the corresponding values for R2, RMSE and VAF for the NLMR model were 0.816, 26.194, and 81.041, respectively

Prediction of seismic slope stability through combination of particle swarm optimization and neural network

One of the main concerns in geotechnical engineering is slope stability prediction during the earthquake. In this study, two intelligent systems namely artificial neural network (ANN) and particle swarm optimization (PSO)–ANN models were developed to predict factor of safety (FOS) of homogeneous slopes. Geostudio program based on limit equilibrium method was utilized to obtain 699 FOS values with different conditions. The most influential factors on FOS such as slope height, gradient, cohesion, friction angle and peak ground acceleration were considered as model inputs in the present study. A series of sensitivity analyses were performed in modeling procedures of both intelligent systems. All 699 datasets were randomly selected to 5 different datasets based on training and testing. Considering some model performance indices, i.e., root mean square error, coefficient of determination (R2) and value account for (VAF) and using simple ranking method, the best ANN and PSO–ANN models were selected. It was found that the PSO–ANN technique can predict FOS with higher performance capacities compared to ANN. R2 values of testing datasets equal to 0.915 and 0.986 for ANN and PSO–ANN techniques, respectively, suggest the superiority of the PSO–ANN technique

Prediction of the uniaxial compressive strength of sandstone using various modeling techniques

Sandstone blocks were collected from Dengkil site in Malaysia and brought to laboratory, and then intact samples prepared for testing. Rock tests, including Schmidt hammer rebound number, P-wave velocity, point load index, and UCS were conducted. The established dataset is composed of 108 cases. Consequently, the established dataset was utilized for developing the simple regression, linear, non-linear multiple regressions, artificial neural network, and a hybrid model, developed by integrating imperialist competitive algorithm with ANN. After performing the relevant models, several performance indices i.e. root mean squared error, coefficient of determination, variance account for, and total ranking, are examined for selecting the best model and comparing the obtained results. It is obtained that the ICA-ANN model is superior to the others. It is concluded that the hybrid of ICA-ANN could be used for predicting UCS of similar rock type in practice. © 2016 Elsevier Ltd

Modification and prediction of blast-induced ground vibrations based on both empirical and computational techniques

Ground vibration (GV) is a blasting consequence and is an important parameter to control in mining and civil projects. Previous GV predictor models have mainly been developed considering two factors; charge per delay and distance from the blast-face. However, mostly the presence of the water as an influential factor has been neglected. In this paper, an attempt has been made to modify United State Bureau of Mines model (USBM) by incorporating the effect of water. For this purpose, 35 blasting operations were investigated in Chadormalu iron mine, Iran and required blasting parameters were recorded in each blasting operation. Eventually, a coefficient was calculated and added in USBM model for effect of water. To demonstrate the capability of the suggested equation, several empirical models were also used to predict measured values of PPV. Results showed that the modified USBM model can perform better compared to previous models. By establishing new parameter in the USBM model, a new predictive model based on gene expression programming (GEP) was utilized and developed to predict GV. To show capability of GEP model in estimating GV, linear multiple regression (LMR) and non-linear multiple regression (NLMR) techniques were also performed and developed using the same datasets. The results demonstrated that the newly proposed model is able to predict blast-induced GV more accurately than other developed techniques

Several non-linear models in estimating air-overpressure resulting from mine blasting

This research presents several non-linear models including empirical, artificial neural network (ANN), fuzzy system and adaptive neuro-fuzzy inference system (ANFIS) to estimate air-overpressure (AOp) resulting from mine blasting. For this purpose, Miduk copper mine, Iran was investigated and results of 77 blasting works were recorded to be utilized for AOp prediction. In the modeling procedure of this study, results of distance from the blast-face and maximum charge per delay were considered as predictors. After constructing the non-linear models, several performance prediction indices, i.e. root mean squared error (RMSE), variance account for (VAF), and coefficient of determination (R2) and total ranking method are examined to choose the best predictive models and evaluation of the obtained results. It is obtained that the ANFIS model is superior to other utilized techniques in terms of R2, RMSE, VAF and ranking herein. As an example, RMSE values of 5.628, 3.937, 3.619 and 2.329 were obtained for testing datasets of empirical, ANN, fuzzy and ANFIS models, respectively, which indicate higher performance capacity of the ANFIS technique to estimate AOp compared to other implemented methods

Rock strength estimation: a PSO-based BP approach

Application of back-propagation (BP) artificial neural network (ANN) as an accurate, practical and quick tool in indirect estimation of uniaxial compressive strength (UCS) of rocks has recently been highlighted in the literature. This is mainly due to difficulty in direct determination of UCS in laboratory as preparing the core samples for this test is troublesome and time-consuming. However, ANN technique has some limitations such as getting trapped in local minima. These limitations can be minimized by combining the ANNs with robust optimization algorithms like particle swarm optimization (PSO). This paper gives insight into development of a hybrid PSO–BP predictive model of UCS. For this reason, dataset comprising the results of 228 laboratory tests including dry density, moisture content, P wave velocity, point load index test, slake durability index and UCS was prepared. These tests were conducted on 38 sandstone samples which were taken from two excavation sites in Malaysia. Findings showed that PSO–BP model performs well in predicting UCS. Nevertheless, to compare the prediction performance of the PSO–BP model, the UCS is predicted using ANN-based PSO and BP models. The correlation coefficient, R, values equal to 0.988 and 0.999 for training and testing datasets, respectively, suggest that the PSO–BP model outperforms the other predictive models

Characteristics of weathering zones of granitic rocks in Malaysia for geotechnical engineering design

Tropical climatic conditions cause severe weathering to rock masses resulting in thick weathering profiles consisting of different weathering zones with complex characteristics. Feasible geotechnical engineering design requires a detailed understanding of the characteristics of each weathering zone. A typical weathering profile is usually characterized by field-scale observation and geological study. This paper presents the characteristics of weathering zones of various granitic rocks in the south of Malaysia. This region has a tropical climatic condition. The study was carried out in four active quarries located in Johor province, Malaysia, and forty sections were investigated in the study areas. A typical granitic weathering profile was characterized based on the geological and structural parameters (i.e. joint characteristics, corestone occurrence, rock/soil ratio, mass homogeneity, color of rock, and discoloration at joints' surfaces). The profile includes common zones ranging from fresh to residual soil; however, two subzones are introduced based on corestone (boulder) occurrence in the highly weathered and completely weathered zones. This study, based on detailed field observations at the mentioned quarries followed by analysis, presents an amalgamated picture of weathering zones in granitic profiles of a tropical region like Malaysia. Moreover, it is believed that the characterization of the typical weathering profile allows further classification of generally complex weathered rock masses for a specific geotechnical engineering design. (C) 2015 Elsevier B.V. All rights reserved

A new hybrid method for predicting ripping production in different weathering zones through in situ tests

Due to blasting's limitations, ripping as a breaking technique of rock mass is one of the most popular methods in mining and civil engineering applications. The typical practice is that ripping is used for loosening the soils and weak rocks while blasting is used for breaking stronger rocks. With the regulatory restrictions on blasting, there is a growing interest in ripping rocks that traditionally have been blasted. The ripping is typically cheaper than blasting but predicting whether ripping can be done on a particular rock and the estimation of the excavation cost are challenging and a function of rock properties. This study aims at predicting the ripping production based on an extensive database obtained from three sites in Malaysia. The site observations for production rate and the relations with the sandstone and shale rocks were presented. In situ observations/tests (sonic velocity, joint spacing, Schmitdt hammer, weathering zone) were conducted by the site engineers and the results were used as input data for training and proposing a new model for estimating the ripping production. Many hybrid particle swarm optimization-artificial neural network (PSO-ANN) models were created and the best model was identified based on a ranking system. Then, the best PSO-ANN model with coefficient of determination values of 0.982 and 0.978 and root mean square error values of 0.038 and 0.045 for training and testing datasets, respectively, was selected and introduced to predict ripping production. This study documented that the new PSO-ANN achieved higher performance than the ANN method