Aminaton Marto

A Novel Approach for Blast-Induced Flyrock Prediction Based on Imperialist Competitive Algorithm and Artificial Neural Network

Flyrock is one of the major disturbances induced by blasting which may cause severe damage to nearby structures. This phenomenon has to be precisely predicted and subsequently controlled through the changing in the blast design to minimize potential risk of blasting. The scope of this study is to predict flyrock induced by blasting through a novel approach based on the combination of imperialist competitive algorithm (ICA) and artificial neural network (ANN). For this purpose, the parameters of 113 blasting operations were accurately recorded and flyrock distances were measured for each operation. By applying the sensitivity analysis, maximum charge per delay and powder factor were determined as the most influential parameters on flyrock. In the light of this analysis, two new empirical predictors were developed to predict flyrock distance. For a comparison purpose, a predeveloped backpropagation (BP) ANN was developed and the results were compared with those of the proposed ICA-ANN model and empirical predictors. The results clearly showed the superiority of the proposed ICA-ANN model in comparison with the proposed BP-ANN model and empirical approaches.

Blast-induced air and ground vibration prediction: a particle swarm optimization-based artificial neural network approach

Mines, quarries, and construction sites face environmental damages due to blasting environmental impacts such as ground vibration and air overpressure. These phenomena may cause damage to structures, groundwater, and ecology of the nearby area. Several empirical predictors have been proposed by various scholars to estimate ground vibration and air overpressure, but these methods are inapplicable in many conditions. However, prediction of ground vibration and air overpressure is complicated as a consequence of the fact that a large number of influential parameters are involved. In this study, a hybrid model of an artificial neural network and a particle swarm optimization algorithm was implemented to predict ground vibration and air overpressure induced by blasting. To develop this model, 88 datasets including the parameters with the greatest influence on ground vibration and air overpressure were collected from a granite quarry site in Malaysia. The results obtained by the proposed model were compared with the measured values as well as with the results of empirical predictors. The results indicate that the proposed model is an applicable and accurate tool to predict ground vibration and air overpressure induced by blasting.

Application of two intelligent systems in predicting environmental impacts of quarry blasting

Blasting, as the most frequently used method for hard rock fragmentation, is a hazardous aspect in mining industries. These operations produce several undesirable environmental impacts such as ground vibration, air-overpressure (AOp), and flyrock in the nearby environments. These environmental impacts may cause injury to human and damage to structures, groundwater, and ecology of the nearby area. This paper is aimed to predict the blasting environmental impacts in granite quarry sites through two intelligent systems, namely artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS). For this purpose, 166 blasting operations at four granite quarry sites in Malaysia were investigated and the values of peak particle velocity (PPV), AOp, and flyrock were precisely recorded in each blasting operation. Considering some model performance indices including coefficient of determination (R2), value account for (VAF), and root mean square error (RMSE), and also using simple ranking procedure, the best models for prediction of PPV, AOp, and flyrock were selected. The results demonstrated that the ANFIS models yield higher performance capacity compared to ANN models. In the case of testing datasets, the R2 values of 0.939, 0.947, and 0.959 for prediction of PPV, AOp, and flyrock, respectively, suggest the superiority of the ANFIS technique, while in predicting PPV, AOp, and flyrock using ANN technique, these values are 0.771, 0.864, and 0.834, respectively.

Effect of magnesium chloride solution on the physico-chemical characteristics of tropical peat

Abstract The stabilization of soils with additives is a chemical method that can be used to improve soils with weak engineering properties. Although the effects of non-traditional additives on the geotechnical properties of tropical soils have been subject of investigation in recent years, the effects of magnesium chloride (MgCl2) on the macro- and micro-structural characteristics of peat soil have not been fully studied. This study investigates the effect of MgCl2 on the physico-chemical characteristics of tropical peat. Unconfined compression strength tests were performed as an index of soil improvement in treated samples. In addition, the micro-structural characteristics of untreated and treated peat were investigated using various spectroscopic and microscopic techniques such as X-ray diffractometry, energy-dispersive X-ray spectrometry, field emission scanning electron microscopy, Fourier transform infrared spectroscopy, and Brunauer, Emmett, and Teller surface area analysis. From an engineering point of view, the results indicated that the strength of MgCl2-stabilized peat improved significantly. The degree of improvement was approximately six times stronger than untreated peat, after a 7-day curing period. Additionally, the micro-structural study revealed that the stabilization process led to a few changes in the mineralogical, morphological, and molecular characteristics of the selected peat. The pores of the peat were filled by newly formed crystalline compounds known as magnesium aluminate hydrate (M–A–H).

Evaluation and prediction of flyrock resulting from blasting operations using empirical and computational methods

Mines, quarries and construction sites face environmental impacts, such as flyrock, due to blasting operations. Flyrock may cause damage to structures and injury to human. Therefore, flyrock prediction is required to determine safe blasting zone. In this regard, 232 blasting operations were investigated in five granite quarries, Malaysia. Blasting parameters comprising maximum charge per delay and powder factor were prepared to predict flyrock using empirical and intelligent methods. An empirical graph was proposed to predict flyrock distance for different powder factor values. In addition, using the same datasets, two intelligent systems, namely artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS) were used to predict flyrock. Considering some model performance indices including coefficient of determination (R2), value account for and root mean squared error and also using simple ranking procedure, the best flyrock prediction models were selected. It was found that the ANFIS model can predict flyrock with higher performance capacity compared to ANN predictive model. R2 values of testing datasets are 0.925 and 0.964 for ANN and ANFIS techniques, respectively, suggesting the superiority of the ANFIS technique in predicting flyrock.

Neuro-fuzzy technique to predict air-overpressure induced by blasting

In addition to all benefits of blasting in mining and civil engineering applications, blasting has some undesirable impacts on surrounding areas. Blast-induced air-overpressure (AOp) is one of the most important environmental impacts of blasting operation which may cause severe damage to nearby residents and structures. Hence, it is a major concern to predict and subsequently control the AOp due to blasting. This paper presents an adaptive neuro-fuzzy inference system (ANFIS) model for prediction of blast-induced AOp in quarry blasting sites. For this purpose, 128 blasting operations were monitored in three quarry sites, Malaysia. Several models were constructed to obtain the optimum model in which each model involved five inputs and one output. Values of maximum charge per delay, powder factor, burden to spacing ratio, stemming length, and distance between monitoring station and blast face were set as input parameters to predict AOp. For comparison purposes, considering the same data, AOp values were predicted through the pre-developed artificial neural network (ANN) model and multiple regression (MR) technique. The results demonstrated the superiority of the ANFIS model to predict AOp compared to other methods. Moreover, results of sensitivity analysis indicated that the maximum charge per delay and powder factor and distance from the blast face are the most influential parameters on AOp.

Strength behavior and microstructural characteristics of tropical laterite soil treated with sodium silicate-based liquid stabilizer

Although the effects of nontraditional stabilizers on the geotechnical properties of tropical soils has been the issue of investigation in recent years, the micro-structural characteristics of nontraditional soil additives and in particular selected additive (TX-85) have not been fully studied. Nontraditional soil stabilization additives are widely used for stabilizing marginal materials. These additives are low-cost alternatives to traditional construction materials and have different compositions. They also differ from one another while interacting with soil. In line with that, it was the objective of this research to investigate the strength properties and physicochemical mechanisms related to tropical laterite soil mixed with the liquid stabilizer TX-85. Macro-structure study, i.e., compaction, and unconfined compression strength test were used to assess the engineering and shear properties of the stabilized laterite soil. In addition, the possible mechanisms that contributed to the stabilization process were discussed using various spectroscopic and microscopic techniques such as X-ray diffractometry (XRD), energy-dispersive X-ray spectrometry, scanning electron microscopy, and Fourier transform infrared spectroscopy. From engineering point of view, the results indicated that the strength of TX-85 stabilized laterite soil improved significantly. The degree of improvement was approximately four times stronger than natural soil after a 7-day curing period. The XRD showed no crystalline products (gel form). Moreover, weathering effects were obvious in TX-85 treated samples in most of clay minerals’ peak intensities. These effects were reduced especially for kaolinite mineral inside the soil with curing time.

Analysis of strength development in non-traditional liquid additive-stabilized laterite soil from macro- and micro-structural considerations

AbstractThe stabilization of soils with additives is a chemically modified method that can be used to improve soils with weak engineering properties. It has been well established that the size, shape, and arrangement of soil particles will affect the treatment process of natural soil with stabilizers. Also, the degree of enhancement is dependent on the morphology of the new formed products that bond the soil particles together. In this paper, unconfined compressive strength (UCS) test was performed as an index of soil improvement on liquid-stabilized (TX-85) mix designs. The time-dependent change in shear properties and compressibility behavior of treated soil was also studied using standard direct shear and consolidation tests. To better understand the structure and surface morphology of treated particles, FESEM, N2-BET and particle size distribution analysis were performed on soil-stabilizer matrix. From engineering point of view, the UCS results indicated that the degree of improvement for TX-85-stabilized laterite soil was approximately four times greater than the natural soil in a 7-day curing time period. Also, increased compressibility resistance of treated samples with curing time was evident. Based on the results, it was found that the stabilization process modifies the porous network of laterite soil. In addition, new white layers of reaction products were formed on the surface of clay particles.

Prediction of blast-induced air overpressure: a hybrid AI-based predictive model

Blast operations in the vicinity of residential areas usually produce significant environmental problems which may cause severe damage to the nearby areas. Blast-induced air overpressure (AOp) is one of the most important environmental impacts of blast operations which needs to be predicted to minimize the potential risk of damage. This paper presents an artificial neural network (ANN) optimized by the imperialist competitive algorithm (ICA) for the prediction of AOp induced by quarry blasting. For this purpose, 95 blasting operations were precisely monitored in a granite quarry site in Malaysia and AOp values were recorded in each operation. Furthermore, the most influential parameters on AOp, including the maximum charge per delay and the distance between the blast-face and monitoring point, were measured and used to train the ICA-ANN model. Based on the generalized predictor equation and considering the measured data from the granite quarry site, a new empirical equation was developed to predict AOp. For comparison purposes, conventional ANN models were developed and compared with the ICA-ANN results. The results demonstrated that the proposed ICA-ANN model is able to predict blast-induced AOp more accurately than other presented techniques.

Sustainable Improvement of Clays Using Low-Carbon Nontraditional Additive

Nontraditional low-carbon additives are widely used in the sustainable treatment of problematic soils for construction and pavement materials. This study investigated the mechanical and microstructural properties of white kaolin (low strength clay) and green bentonite (high swelling clay) treated with a low-carbon sodium silicate-based liquid additive. The mechanical tests included unconfined compressive strength (UCS), direct shear and one-dimensional compression tests. Microscale assessments, including a field emission scanning electron microscopic (FESEM) test, nitrogen-based Brunauer, Emmett, and Teller (N2-BET) surface area analysis and particle size analysis (PSA), were performed on the treated specimens to investigate the modification of soil structure, including soil fabric and interparticle forces. The performance of the proposed additive is demonstrated by the improvement of shear strength and compressibility of both tested soils. The optimum additive content was found to be 6%, and a significant improvement occurred in the first 7 days of curing. The mechanical property improvement is attributed to the formation of cementitious products and, subsequently, the modification of the soil structure. These cementitious products filled the pores and bonded the soil particles, resulting in an increase in interparticle forces. The sodium silicate-based additive can offer a low-carbon alternative to traditional additives such as cement and lime, which is significant from the engineering and environmental perspectives.