Application of hybrid intelligent systems in predicting the unconfined compressive strength of clay material mixed with recycled additive

A reliable prediction of the soil properties mixed with recycled material is considered as an ultimate goal of many geotechnical laboratory works. In this study, after planning and conducting a series of laboratory works, some basic properties of marine clay treated with recycled tiles together with their unconfined compressive strength (UCS) values were obtained. Then, these basic properties were selected as input variables to predict the UCS values through the use of two hybrid intelligent systems i.e., the neuro-swarm and the neuro-imperialism. Actually, in these systems, respectively, the weights and biases of the artificial neural network (ANN) were optimized using the particle swarm optimization (PSO) and imperialism competitive algorithm (ICA) to get a higher accuracy compared to a pre-developed ANN model. The best neuro-swarm and neuro-imperialism models were selected based on several parametric studies on the most important and effective parameters of PSO and ICA. Afterward, these models were evaluated according to several well-known performance indices. It was found that the neuro-swarm predictive model provides a higher level of accuracy in predicting the UCS of clay soil samples treated with recycled tiles. However, both hybrid predictive models can be used in practice to predict the UCS values for initial design of geotechnical structures

Evaluation and prediction of blast-induced ground vibration at Shur River Dam, Iran, by artificial neural network

The purpose of this article is to evaluate and predict blast-induced ground vibration at Shur River Dam in Iran using different empirical vibration predictors and artificial neural network (ANN) model. Ground vibration is a seismic wave that spreads out from the blasthole when explosive charge is detonated in a confined manner. Ground vibrations were recorded and monitored in and around the Shur River Dam, Iran, at different vulnerable and strategic locations. A total of 20 blast vibration records were monitored, out of which 16 data sets were used for training of the ANN model as well as determining site constants of various vibration predictors. The rest of the 4 blast vibration data sets were used for the validation and comparison of the result of ANN and different empirical predictors. Performances of the different predictor models were assessed using standard statistical evaluation criteria. Finally, it was found that the ANN model is more accurate as compared to the various empirical models available. As such, a high conformity (R 2 = 0.927) was observed between the measured and predicted peak particle velocity by the developed ANN model. © 2012 Springer-Verlag London Limited

Finite Element Modeling for the Antivibration Pavement Used to Improve the Slope Stability of the Open-Pit Mine

Vibrations induced by traffic are of concern for the slope stability of the open-pit mine. Different solutions to mitigate this phenomenon are under investigation. In the field of pavement engineering, the so-called antivibration paving technologies are under investigation in order to avoid the generation of excessive vibration and contains propagation. To more fully examine the effectiveness and potential of the antivibration pavement in the application of vibration absorbing for the open-pit mines, numerical simulations based on a two-dimensional (2D) finite element (FE) model were conducted. Sensitivity analysis of varying monitored points and varying loads are performed. Several important parameters such as the damping layer position and thickness and damping ratio are evaluated as well. By using this FE simulation to model the vibration response induced by traffic, the costly construction mistakes and field experimentation can be avoided

Development of a precise model for prediction of blast-induced flyrock using regression tree technique

Drilling and blasting is the predominant rock fragmentation method in open-cast mines and civil construction works. Flyrock is one of the most hazardous effects caused by blasting operation. Therefore, the ability to make accurate predictions of the blast-induced flyrock is essential to reduce the environmental problems. This paper aimed to develop a precise and applicable model based on regression tree (RT) to predict blast-produced flyrock distance in Ulu Tiram quarry, Malaysia. In this regard, 65 blasting operations were investigated and the most influential factors on the flyrock, i.e. blast-hole length, spacing, burden, stemming, maximum charge used per delay and powder factor, were measured. Also, the flyrock distance values for the considered blasting events were carefully measured. In order to check the precision of the proposed RT model, multiple linear regression (MLR) model was also developed and both of the predictive models were compared. For this work, some statistical functions, i.e. median absolute error, coefficient of determination (R2) and root mean squared error, were used and computed. The results revealed that the RT can be introduced as a powerful technique to predict flyrock distance and the proposed RT model can estimate flyrock distance better than MLR model. Also, sensitivity analysis was performed and it was found that the powder factor is the most influential parameter on the flyrock in the studied case. © 2016, Springer-Verlag Berlin Heidelberg

Intelligent prediction of blasting-induced ground vibration using ANFIS optimized by GA and PSO

Ground vibration induced by rock blasting is one of the most crucial problems in surface mines and tunneling projects. Hence, accurate prediction of ground vibration is an important prerequisite in the minimization of its environmental impacts. This study proposes hybrid intelligent models to predict ground vibration using adaptive neuro-fuzzy inference system (ANFIS) optimized by particle swarm optimization (PSO) and genetic algorithms (GAs). To build prediction models using ANFIS, ANFIS–GA, and ANFIS–PSO, a database was established, consisting of 86 data samples gathered from two quarries in Iran. The input parameters of the proposed models were the burden, spacing, stemming, powder factor, maximum charge per delay (MCD), and distance from the blast points, while peak particle velocity (PPV) was considered as the output parameter. Based on the sensitivity analysis results, MCD was found as the most effective parameter of PPV. To check the applicability and efficiency of the proposed models, several traditional performance indices such as determination coefficient (R2) and root-mean-square error (RMSE) were computed. The obtained results showed that the proposed ANFIS–GA and ANFIS–PSO models were capable of statistically predicting ground vibration with excellent levels of accuracy. Compared to the ANFIS, the ANFIS–GA model showed an approximately 61% decrease in RMSE and 10% increase in R2. Also, the ANFIS–PSO model showed an approximately 53% decrease in RMSE and 9% increase in R2 compared to ANFIS. In other words, the ANFIS performance was optimized with the use of GA and PSO

Feasibility of ICA in approximating ground vibration resulting from mine blasting

Precise prediction of blast-induced ground vibration is an essential task to reduce the environmental effects in the surface mines, civil and tunneling works. This research investigates the potential of imperialist competitive algorithm (ICA) in approximating ground vibration as a result of blasting at three quarry sites, namely Ulu Tiram, Pengerang and Masai in Malaysia. In ICA modeling, two forms of equations, namely power and quadratic, were developed. For comparison aims, several empirical models were also used. In order to develop the ICA and empirical models, maximum charge weight used per delay (W) and the distance between blasting sites and monitoring stations (D) were utilized as the independent variables, while, peak particle velocity (PPV), as a blast-induced ground vibration descriptor, was utilized as the dependent variable. Totally, 73 blasting events were monitored, and the values of W, D and PPV were carefully measured. Two statistical functions, i.e., root mean square error and coefficient of multiple determination (R2) were used to compare the performance capability of those prediction models. Simulation results show that the proposed ICA quadratic form can get more accurate predicting results than the ICA power form and empirical models

A svr-gwo technique to minimize flyrock distance resulting from blasting

Flyrock is one of the most important environmental and hazardous issues in mine blasting, which can affect equipment and people, and may lead to fatal accidents. Therefore, prediction and minimization of this phenomenon are crucial objectives of many rock removal projects. This study is aimed to predict the flyrock distance with the use of machine learning techniques. The most effective parameters of flyrock were measured during blasting operations in six mines. In total, 262 data samples of blasting operations were accurately measured and used for approximation purposes. Then, flyrock was evaluated and estimated using three machine learning methods: principle component regression (PCR), support vector regression (SVR), and multivariate adaptive regression splines (MARS). Many models of PCR, SVR, and MARS were constructed for the flyrock distance prediction. The modeling process of each method is elaborated separately in a way to be used by other researchers. The most important parameters affecting these models were assessed to obtain the best performance for the developed models. Eventually, a preferable model of each machine learning technique was used for comparison purposes. According to the used performance indices, coefficient of determination (R2), and root mean square error, the SVR model showed a better performance capacity in predicting flyrock distance compared with the other proposed models. Thus, the SVR prediction model can be used to accurately predict flyrock distance, thereby properly determining the blast safety area. Additionally, the SVR model was optimized by new optimization algorithm namely gray wolf optimization (GWO) for minimizing the flyrock resulting from blasting operation. By developing optimization technique of GWO, the value of flyrock can be decreased 4% compared with the minimum flyrock distance

Risk assessment and prediction of flyrock distance by combined multiple regression analysis and Monte Carlo simulation of quarry blasting

Flyrock is considered as one of the main causes of human injury, fatalities, and structural damage among all undesirable environmental impacts of blasting. Therefore, it seems that the proper prediction/simulation of flyrock is essential, especially in order to determine blast safety area. If proper control measures are taken, then the flyrock distance can be controlled, and, in return, the risk of damage can be reduced or eliminated. The first objective of this study was to develop a predictive model for flyrock estimation based on multiple regression (MR) analyses, and after that, using the developed MR model, flyrock phenomenon was simulated by the Monte Carlo (MC) approach. In order to achieve objectives of this study, 62 blasting operations were investigated in Ulu Tiram quarry, Malaysia, and some controllable and uncontrollable factors were carefully recorded/calculated. The obtained results of MC modeling indicated that this approach is capable of simulating flyrock ranges with a good level of accuracy. The mean of simulated flyrock by MC was obtained as 236.3 m, while this value was achieved as 238.6 m for the measured one. Furthermore, a sensitivity analysis was also conducted to investigate the effects of model inputs on the output of the system. The analysis demonstrated that powder factor is the most influential parameter on fly rock among all model inputs. It is noticeable that the proposed MR and MC models should be utilized only in the studied area and the direct use of them in the other conditions is not recommended

Developing a New Computational Intelligence Approach for Approximating the Blast-Induced Ground Vibration

Ground vibration induced by blasting operations is an important undesirable effect in surface mines and has significant environmental impacts on surrounding areas. Therefore, the precise prediction of blast-induced ground vibration is a challenging task for engineers and for managers. This study explores and evaluates the use of two stochastic metaheuristic algorithms, namely biogeography-based optimization (BBO) and particle swarm optimization (PSO), as well as one deterministic optimization algorithm, namely the DIRECT method, to improve the performance of an artificial neural network (ANN) for predicting the ground vibration. It is worth mentioning this is the first time that BBO-ANN and DIRECT-ANN models have been applied to predict ground vibration. To demonstrate model reliability and effectiveness, a minimax probability machine regression (MPMR), extreme learning machine (ELM), and three well-known empirical methods were also tested. To collect the required datasets, two quarry mines in the Shur river dam region, located in the southwest of Iran, were monitored, and the values of input and output parameters were measured. Five statistical indicators, namely the percentage root mean square error (%RMSE), coefficient of determination (R2), Ratio of RMSE to the standard deviation of the observations (RSR), mean absolute error (MAE), and degree of agreement (d) were taken into account for the model assessment. According to the results, BBO-ANN provided a better generalization capability than the other predictive models. As a conclusion, BBO, as a robust evolutionary algorithm, can be successfully linked to the ANN for better performance