Mojtaba Asadi

Bio: Mojtaba Asadi is an academic researcher from University of Texas at El Paso. The author has contributed to research in topics: Finite element method & Abrasion (geology). The author has an hindex of 6, co-authored 13 publications receiving 105 citations. Previous affiliations of Mojtaba Asadi include Shahid Bahonar University of Kerman & Sirjan University of Technology.

Evaluating the strength of intact rocks through genetic programming

Abstract: Good prediction of the strength of rocks has many theoretical and practical applications. Analysis, design and construction of underground openings and tunnels, open pit mines and rock-based foundations are some examples of applications in which prediction of the strength of rocks is of great importance. The prediction might be done using mathematical expressions called failure criteria. In most cases, failure criteria of jointed rocks contain the value of strength of intact rock, i.e. the rock without joints and cracks. Therefore, the strength of intact rock can be used directly in applications and indirectly to predict the strength of jointed rock masses. On the other part, genetic programming method is one of the most powerful methods in machine learning field and could be utilized for non-linear regression problems. The derivation of an appropriate equation for evaluating the strength of intact rock is the common objective of many researchers in civil and mining engineering; therefore, mathematical expressions were derived in this paper to predict the strength of the rock using a genetic programming approach. The data of 51 rock types were used and the efficiency of equations obtained was illustrated graphically through figures.

Modified criteria for sliding and non-sliding failure of anisotropic jointed rocks

Abstract: Modified failure criteria are proposed to estimate sliding and non-sliding strength of anisotropic rocks containing single or a set of parallel well-defined discontinuities. A newly modified form of Mohr–Coulomb relation is incorporated into two well-founded failure criteria namely Jaeger’s criterion and Tien–Kuo criterion. Some input parameters of the proposed criteria may be indirectly determined using empirical correlations extracted from the literature. Consequently, the strength of anisotropic jointed rocks can be evaluated more easily and accurately compared with current failure criteria. Another advantage of the proposed criteria is that their use does not require any curve fitting procedure since the parameters can be estimated through the given relations. The concept of critical confining pressure is also assessed in more detail and an equation is proposed to predict the confining pressure above which discontinuity has no effect on the strength. In addition, laboratory tests are carried out to evaluate the theoretical results and it is shown that the proposed criteria are of acceptable accuracy and applicability.

Optimized Mamdani fuzzy models for predicting the strength of intact rocks and anisotropic rock masses

Abstract: Development of accurate and reliable models for predicting the strength of rocks and rock masses is one of the most common interests of geologists, civil and mining engineers and many others. Due to uncertainties in evaluation of effective parameters and also complicated nature of geological materials, it is difficult to estimate the strength precisely using theoretical approaches. On the other hand, intelligent approaches have attracted much attention as novel and effective tools of solving complicated problems in engineering practice over the past decades. In this paper, a new method is proposed for mining descriptive Mamdani fuzzy inference systems to predict the strength of intact rocks and anisotropic rock masses containing well-defined through-going joint. The proposed method initially employs a genetic algorithm (GA) to pick important rules from a preliminary rule base produced by grid partitioning and, subsequently, selected rules are given weights using the GA. Moreover, an information criterion is used during the first phase to optimize the models in terms of accuracy and complexity. The proposed hybrid method can be considered as a robust optimization task which produces promising results compared with previous approaches.

Development of optimal fuzzy models for predicting the strength of intact rocks

Abstract: Fuzzy models have been used in a wide variety of applications particularly problems associated with the strength of rocks and rock masses. However, a systematic approach of modeling has not been presented thus far and developing appropriate fuzzy models is usually carried out by trial and error. In this paper a new soft-computing approach is introduced which benefits from searching capabilities of Multi-Objective Genetic Algorithm (MOGA) to develop fuzzy models optimized in terms of complexity and accuracy. The proposed method is then used to find optimal fuzzy models to predict the strength of intact rock specimens under conventional triaxial stresses. In addition, laboratory tests are conducted on specimens of three rock types to evaluate the models. It is shown that a relatively simple model, with few manageable rules, is able to estimate the strength of intact rocks properly and hence may be selected as the best fuzzy model.

Numerical and intelligent modeling of triaxial strength of anisotropic jointed rock specimens

Abstract: The strength of anisotropic rock masses can be evaluated through either theoretical or experimental methods. The latter is more precise but also more expensive and time-consuming especially due to difficulties of preparing high-quality samples. Numerical methods, such as finite element method (FEM), finite difference method (FDM), distinct element method (DEM), etc. have been regarded as precise and low-cost theoretical approaches in different fields of rock engineering. On the other hand, applicability of intelligent approaches such as fuzzy systems, neural networks and decision trees in rock mechanics problems has been recognized through numerous published papers. In current study, it is aimed to theoretically evaluate the strength of anisotropic rocks with through-going discontinuity using numerical and intelligent methods. In order to do this, first, strength data of such rocks are collected from the literature. Then FlAC, a commercially well-known software for FDM analysis, is applied to simulate the situation of triaxial test on anisotropic jointed specimens. Reliability of this simulation in predicting the strength of jointed specimens has been verified by previous researches. Therefore, the few gaps of the experimental data are filled by numerical simulation to prevent unexpected learning errors. Furthermore, a sensitivity analysis is carried out based on the numerical process applied herein. Finally, two intelligent methods namely feed forward neural network and a newly developed fuzzy modeling approach are utilized to predict the strength of above-mentioned specimens. Comparison of the results with experimental data demonstrates that the intelligent models result in desirable prediction accuracy.

Foundations of engineering geology

Abstract: Geology Igneous Rocks Surface Processes Sedimentary Rocks Metamorphic Rocks Geological Structures Geological Maps Map Interpretation Tectonics Boundary Hazards Rocks of Britain Rocks of the US Weathering and Soils Floodplains and Alluvium Glacial Deposits Climatic Variants Coastal Processes Groundwater Ground Investigation Desk Study Ground Investigation Boreholes Geophysical Surveys Assessment of Difficult Ground Rock Strength Rock Mass Strength Soil Strength Ground Subsidence Subsidence on Clays Subsidence on Limestone Subsidence Over Old Mines Mining Subsidence Slope Failure and Landslides Water in Landslides Soil Failures and Flowslides Landslide Hazards Slope Stabilization Ground Conditions Rock Excavation Tunnels in Rock Stone and Aggregate Appendices Rock Mass Quality Q System Abbreviations and Notation Further Reading Index

An adaptive neuro-fuzzy inference system for predicting unconfined compressive strength and Young’s modulus: a study on Main Range granite

Abstract: Engineering properties of rocks such as unconfined compressive strength (UCS) and Young’s modulus (E) are among the essential parameters for the design of tunnel excavations. Many attempts have been made to develop indirect methods of estimating UCS and E. This is generally attributed to the difficulty of preparing and conducting the aforementioned tests in a laboratory. In essence, this study aims to present two predictive models of UCS and E for granite using an adaptive neuro-fuzzy inference system (ANFIS). The required rock samples for model development (45 granite sample sets) were obtained from site investigation work at the Pahang-Selangor raw water transfer tunnel, which was excavated across the Main Range of Peninsular Malaysia. In developing the predictive models, dry density, ultrasonic velocity, quartz content and plagioclase were set as model inputs. These parameters were selected based on simple and multiple regression analyses presented in the article. However, for the sake of comparison, the prediction performances of the ANFIS models were checked against multiple regression analysis (MRA) and artificial neural network (ANN) predictive models of UCS and E. The capacity performances of the predictive models were assessed based on the value account for (VAF), root mean squared error (RMSE) and coefficient of determination (R 2). It was found that the ANFIS predictive model of UCS, with R 2, RMSE and VAF equal to 0.985, 6.224 and 98.455 %, respectively, outperforms the MRA and ANN models. A similar conclusion was drawn for the ANFIS predictive model of E where the values of R 2, RMSE and VAF were 0.990, 3.503 and 98.968 %, respectively.

Indirect measure of shale shear strength parameters by means of rock index tests through an optimized artificial neural network

Abstract: Shear strength is one of the most important features in engineering design of geotechnical structures such as embankments, earth dams, tunnels and foundations. Shear strength parameters describe how rock material resists deformation induced by shear stress. Rock shear strength parameters are usually measured through laboratory tests, and these methods are destructive, time consuming and expensive. In addition, providing good-quality core samples is difficult especially in highly fractured and weathered rocks. This paper presents an indirect measure of shear strength parameters of shale by means of rock index tests. In this regard, 230 shale samples were collected from an excavation site in Malaysia and shear strength parameters of samples were obtained using triaxial compression test. Furthermore, rock index tests including dry density, point load index, Brazilian tensile strength, ultrasonic velocity, and Schmidt hammer test were conducted for each sample. A particle swarm optimization-artificial neural network (PSO-ANN) integrated model was developed by setting the results of rock index tests as inputs and shear strength parameters as outputs of the model. The obtained correlation of determination of 0.966 and 0.944 for training and testing datasets show the applicability of the proposed model to predict shale shear strength parameters with high accuracy.

A new soft computing model for estimating and controlling blast-produced ground vibration based on Hierarchical K-means clustering and Cubist algorithms

Abstract: Blasting is an essential task in open-pit mines for rock fragmentation. However, its dangerous side effects need to be accurately estimated and controlled, especially ground vibration as measured in the form of peak particle velocity (PPV). The accuracy for estimating blast-induced PPV can be improved by hybrid artificial intelligence approach. In this study, a new hybrid model was developed based on Hierarchical K-means clustering (HKM) and Cubist algorithm (CA), code name HKM-CA model. The HKM clustering hybrid technique was used to separate data according to their characteristics. Subsequently, the Cubist model was trained and developed on the clusters generated by HKM. Empirical technique, the benchmark algorithms [random forest (RF), support vector machine (SVM), classification and regression tree (CART)], and single CA model were also established for benchmarking the HKM-CA model. Root-mean-square error (RMSE), determination coefficient (R2), and mean absolute error (MAE) were the key indicators used for evaluating the model performance. The results revealed that the proposed HKM-CA model was a powerful tool for improving the accuracy of the CA model. Specifically, the HKM-CA model yielded a superior result with an RMSE of 0.475, R2 of 0.995, and MAE of 0.373 in comparison to other models. The proposed HKM-CA model has the potential to be used for predicting blast-induced PPV on-site to control undesirable effects on the surrounding environment.

Developing novel models using neural networks and fuzzy systems for the prediction of strength of rocks from key geomechanical properties

Abstract: This research study was conducted to predict the unconfined compressive strength (UCS) of the rocks by applying the adaptive neuro-fuzzy inference system (ANFIS), and the outcomes were compared with the traditional statistical model of multiple regression (MR) analysis and artificial neural network (ANN). 13 types of rock samples collected from 5 geological horizons in India were tested in the laboratory as per the International Society for Rock Mechanics (ISRM) standards. In developing the predictive models, ultrasonic P-wave velocity, density and slake durability index were considered as model inputs, whereas UCS was the output parameter. The prediction performance of ANFIS model was checked against the MR and the ANN predictive models. It was found that the constructed ANFIS model exhibited relatively high prediction performance of UCS than the MR and the ANN models. The performance capacity of the predictive models were evaluated based on the coefficient of determination (R2), the mean absolute percentage error (MAPE), the root mean square error (RMSE) and the variance account for (VAF). The ANFIS predictive model had R2, MAPE, RMSE and VAF equal to 0.978, 10.15%, 6.29 and 97.66%, respectively, superseding the performance of the MR and the ANN models. The performance comparison revealed that soft computing is a good approach for minimizing the uncertainties and inconsistency of correlations in geotechnical engineering.