The application of particle swarm optimization in slope stability analysis of homogeneous soil slopes

This paper applies particle swarm optimization (PSO) to find the most possible failure surface in stability analysis of homogeneous soil slopes. The stability of slopes is an important issue on geotechnical engineering. This problem includes two general concepts, factor of safety (FOS) and the critical slip surface (CSS). The resultant ratio of dividing strengthens forces by driving forces is called FOS. The critical slip surface is defined as a failure surface with the minimum value of FOS among all candidates. Regarding to the vast number of trial slip surfaces and the non-linear nature of equation of FOS, a global optimization algorithm is needed to locate CSS. As an optimization technique, the original version of PSO with little improvements in initial parameters is used. With this aim in view, we developed a computer code to find CSS by particle swarm optimization. Moreover, a sensitivity analysis is conducted to find the optimum values of initial parameters of PSO. Finally the effectiveness and efficiency of PSO code is verified and compared with the benchmark examples from the literature. The results demonstrated the ability of PSO to find CSS with better outcomes than former methods

Simulation of longitudinal surface settlement due to tunnelling using artificial neural network

A series of artificial neural networks modelling was conducted to investigate the ground deformation induced by tunnelling along the line 2 of Karaj urban railway, Iran. The tunnels were excavated using New Austrian Tunnelling Method. During excavation, surface settlement was monitored using optical survey points installed on the centre, left and right sides of the tunnel axis. The measured data have been used to establish an artificial neural network model to predict longitudinal surface settlement. This paper focuses on the prediction of ground deformation due to tunnelling using artificial neural networks, particularly longitudinal settlements in relation to the ground condition and tunnelling method. The obtained results demonstrate that artificial neural networks are applicable techniques for predicting longitudinal surface settlement due to tunnelling

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

The effects of method of generating circular slip surfaces on determining the critical slip surface by particle swarm optimization

The main objectives of slope stability analysis are evaluating factor of safety for a given slip surface and determining the critical slip surface for a given slope. Factor of safety is usually calculated by limit equilibrium method. The main steps to determine the critical slip surface are generating trial slip surfaces as probable solutions and searching among them to determine the one with the lowest factor of safety. Although the process of searching the critical slip surface received much attention between researchers, the significance of method of generating slip surfaces is seldom addressed in the literature. The authors believe that this ignorance can affect the accuracy of the results of slope stability analysis even in the simplest problems with circular slip surfaces. Consequently, this paper focused on the method of generating circular trial slip surfaces as the simplest mechanism of sliding and considered its effect on determining the critical slip surface. A new method of generating circular slip surface was presented, which is more efficient and less restricted than the conventional method. A computer program was also developed to determine the critical slip surface of slopes by using particle swarm optimization. The performances of the proposed method and developed computer program were verified during comparative studies and sensitivity analysis. Based on the results, the effect of method of generating circular slip surfaces on determining the critical slip surface was confirmed successfully. In all considered problems, the proposed method of generating circular slip surfaces led to the lower values of factor of safety compare with the conventional method

3D prediction of tunneling-induced ground movements based on a hybrid ann and empirical methods

Tunnel construction in urban areas causes ground displacement which may distort and damage overlying buildings and municipal utilities. It is therefore extremely important to predict tunneling-induced ground movements in tunneling projects. To predict the tunneling-induced ground movements, artificial neural networks (ANNs) have been used as flexible non-linear approximation functions. These methods, however, have significant limitations that decrease their accuracy and applicability. To overcome these problems, the use of optimization algorithms to train ANNs is of advantage. In this paper, a hybrid particle swarm optimization (PSO) algorithm-based ANN is developed to predict the maximum surface settlement and inflection points in transverse and longitudinal directions. Subsequently, the transverse and longitudinal troughs were obtained by means of empirical equations and 3D surface settlement troughs were ploted. For this purpose, extensive data consisting of measured settlements from 123 settlement markers, geotechnical properties and tunneling parameters were collected from the Karaj Urban Railway Project in Iran. The optimum values of PSO parameters were determined with the help of sensitivity analysis. On the other hand, to find the optimal architecture of the network, trial-and-error method was used. The final hybrid model including eight inputs, a hidden layer and three outputs was used to predict transverse and longitudinal tunneling-induced ground movements. The results demonstrated that the proposed model can very accurately predict three-dimensional ground movements induced by tunneling

Determination of soil specific gravity by using partially vacuum and shaking methods

This research was carried out to develop a new testing method that capable to accelerate the testing period for determining the specific gravity of clayey and sandy soils. In this research, partially vacuum and shaking methods were employed in order to enhance the effectiveness of the method. An oven dried kaolin clay and sand were used as sample materials. The testing programme was conducted on the kaolin clay sample first and continues with the sand sample. The materials then were poured inside a special set of fabricated flask and distilled water was added carefully on the top of the sample. Four sets of flask were mounted on the shaker machine by the used of the clip system. Special tubes were used to connect the flask to the vacuum device. Partially vacuum and shaking methods were used to remove the air out from the sample while air bubbles were released from the sample by shaking the flask. It was found that the specific gravity value obtained from the partially vacuum and shaking methods provide a reliable value of specific gravity as compare to pycnometer/density bottle method. In addition, the testing period has accelerated up to three times as compare to the standard method

Assessment of weathering effects on rock mass structure

This paper presents an assessment of weathering effect to the rock mass structure by studying the joint characteristics of tropically weathered granite. Joint survey was performed by scanline method and the results were analyzed statistically by stereographic projection plots. The overall trend of mean joint spacing followed a sharp decrement from fresh to moderately weathered zone and then a slight increment to highly and completely weathered zones, whereas the overall trend of mean joint trace length showed a gradual decrement over progress of weathering. In addition, the degree of joints inclination and weathering zones revealed an increasing trend in the percentage of horizontal joints from fresh to completely weathered rocks, while no specific relation was found between the numbers of major joint set and different weathering zones. The results of this study may contribute to understanding the behavior and better classification of weathered granitic rock mass as a heterogeneous mass in engineering works