Mohsen Hajihassani

Bio: Mohsen Hajihassani is an academic researcher from Urmia University. The author has contributed to research in topics: Particle swarm optimization & Slope stability analysis. The author has an hindex of 21, co-authored 51 publications receiving 2094 citations. Previous affiliations of Mohsen Hajihassani include Universiti Teknologi Malaysia & Duy Tan University.

Blasting-induced flyrock and ground vibration prediction through an expert artificial neural network based on particle swarm optimization

Abstract: Blasting is a major component of the construction and mining industries in terms of rock fragmentation and concrete demolition. Blast designers are constantly concerned about flyrock and ground vibration induced by blasting as adverse and unintended effects of explosive usage on the surrounding areas. In recent years, several researches have been done to predict flyrock and ground vibration by means of conventional backpropagation (BP) artificial neural network (ANN). However, the convergence rate of the BP-ANN is relatively slow and solutions can be trapped at local minima. Since particle swarm optimization (PSO) is a robust global search algorithm, it can be used to improve ANNs' performance. In this study, a novel approach of incorporating PSO algorithm with ANN has been proposed to eliminate the limitation of the BP-ANN. This approach was applied to simulate the flyrock distance and peak particle velocity (PPV) induced by blasting. PSO parameters and optimal network architecture were determined using sensitivity analysis and trial and error method, respectively. Finally, a model was selected, and the proposed model was trained and tested using 44 datasets obtained from three granite quarry sites in Malaysia. Each dataset involved ten inputs, including the most influential parameters on flyrock distance and PPV, and two outputs. The results indicate that the proposed method is able to predict flyrock distance and PPV induced by blasting with a high degree of accuracy. Sensitivity analysis was also conducted to determine the influence of each parameter on flyrock distance and PPV. The results show that the powder factor and charge per delay are the most effective parameters on flyrock distance, whereas sub-drilling and charge per delay are the most effective parameters on PPV.

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

Abstract: 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.

Ground vibration prediction in quarry blasting through an artificial neural network optimized by imperialist competitive algorithm

Abstract: This paper presents a new hybrid artificial neural network (ANN) optimized by imperialist competitive algorithm (ICA) to predict peak particle velocity (PPV) resulting from quarry blasting. For this purpose, 95 blasting works were precisely monitored in a granite quarry site in Malaysia and PPV values were accurately recorded in each operation. Furthermore, the most influential parameters on PPV were measured and used to train the ICA-ANN model. Considering the measured data from the granite quarry site, a new empirical equation was developed to predict PPV. For comparison, a pre-developed ANN model was developed for PPV prediction. The results demonstrated that the proposed ICA-ANN model is able to predict blasting-induced PPV better than other presented techniques.

A Comprehensive Survey on Particle Swarm Optimization Algorithm and Its Applications

Abstract: Particle swarm optimization (PSO) is a heuristic global optimization method, proposed originally by Kennedy and Eberhart in 1995. It is now one of the most commonly used optimization techniques. This survey presented a comprehensive investigation of PSO. On one hand, we provided advances with PSO, including its modifications (including quantum-behaved PSO, bare-bones PSO, chaotic PSO, and fuzzy PSO), population topology (as fully connected, von Neumann, ring, star, random, etc.), hybridization (with genetic algorithm, simulated annealing, Tabu search, artificial immune system, ant colony algorithm, artificial bee colony, differential evolution, harmonic search, and biogeography-based optimization), extensions (to multiobjective, constrained, discrete, and binary optimization), theoretical analysis (parameter selection and tuning, and convergence analysis), and parallel implementation (in multicore, multiprocessor, GPU, and cloud computing forms). On the other hand, we offered a survey on applications of PSO to the following eight fields: electrical and electronic engineering, automation control systems, communication theory, operations research, mechanical engineering, fuel and energy, medicine, chemistry, and biology. It is hoped that this survey would be beneficial for the researchers studying PSO algorithms.

Structural analysis

Abstract: Structures in Other Domains The methodology of structural analysis discussed in this article has been applied beyond the narrow realm of natural language syntax that we have discussed in this article. Within the study of language, similar methods of analysis have been pervasively applied to the study of sounds (phonology), words (morphology), and meanings (semantics), yielding a range of of abstract structural representations whose properties bear considerable explanatory burden. There are a wealth of cases in each of these domains analogous to those discussed here, though space prevents us from going in these (see Akmajian, Demers, Farmer and Harnish 1995 for a traditional overview, and Jackendo 1994 for one more focused on connections with cognitive science). Additionally, these representations have shed substantial light on the processes of language acquisition and language change. It has been found that variation in the types of sentences that are used, whether during the course of children's acquisition of their native languages or in the centuries-long periods of linguistic change, are best characterized not as super cial and haphazard alterations, but rather in terms of parametric modi cations to the fundamental underlying grammatical rules and constraints. Moving outside the domain of language, one application of these same methods has been in the study of music cognition. Just as the representations of linguistic theory arise out of an attempt to model speakers' intuitions about well-formedness and possible meanings of the sentences of their

Decision Support System

Abstract: Ein Decision Support System umfast die Komponenten Daten, Dialog und Modell, weshalb in diesem Kontext auch von DDM-Paradigma gesprochen wird.1 Die Komponente Daten sollte derart gestaltet sein, das die benotigten Informationen adaquat bereitgehalten werden. Durch die Komponente Dialog ist sicherzustellen, das die Interaktion des Anwenders mit dem Decision Support System und damit auch der Zugriff auf die Daten einfach und leicht verstandlich gestaltet ist. Die Komponente Modell sollte der Aufgabenstellung angemessene Modellierungs- und Analysemoglichkeiten zur Verfugung stellen. Ein Decision Support System, welches dem DDM-Paradigma Rechnung tragt, fugt die beschriebenen Komponenten geeignet zusammen.