Applying various hybrid intelligent systems to evaluate and predict slope stability under static and dynamic conditions

Koopialipoor, Mohammadreza; Armaghani, Danial Jahed; Hedayat, Ahmadreza; Marto, Aminaton; Gordan, Behrouz

doi:10.1007/s00500-018-3253-3

Cited by 179 publications

(74 citation statements)

References 59 publications

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“…ANN is an efficient parallel processing architecture containing at least a single hidden layer as well as input and output variables. It effectively handles fuzzy information where the functional relationships are not clear [80,83,84]. In ANN, through training, preceding samples, and experiences, the models can be created.…”

Section: Figure 2 Seepage Line Determinationmentioning

confidence: 99%

“…In this way, bias and weight in ANNs can adjusted, which results in higher efficiency and accuracy level when performing prediction tasks. In recent years, such hybrid systems have been applied to a number of geotechnical problems [78][79][80]. Based on the above discussion, the present paper attempts to accomplish the following objectives:…”

Section: Introductionmentioning

confidence: 99%

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Seepage Analysis in Short Embankments Using Developing a Metaheuristic Method Based on Governing Equations

et al. 2020

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Seepage is one of the most challenging issues in some procedures such as design, construction, and operation of embankment or earth fill dams. The purpose of this research is to develop a new solution based on governing equations to solve the seepage problem in an effective way. Therefore, by implementing the equations in the programming environment, more than 24,000 models were designed to be applicable to different conditions. Input data included different parameters such as slopes in upstream and downstream, embankment width, soil permeability coefficient, height, and freeboard. With the use of this big data, a new process was developed to provide simple mathematical models for the seepage rate analysis. The study first used intelligent models to simulate the seepage behavior. Finally, the accuracy of the models was optimized using a new metaheuristic algorithm. This led to the ultimate flexibility of the final model presented as a new solution capable of evaluating different conditions. Finally, using the best model, new mathematical relationships were developed based on this methodology. This new solution can be used as a proper alternative to the governing equations of seepage rate estimation. Another advantage of the proposed model is its high flexibility that can be well applied to engineering design in this field, which was not possible using the initial equations.

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Section: Figure 2 Seepage Line Determinationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Seepage Analysis in Short Embankments Using Developing a Metaheuristic Method Based on Governing Equations

et al. 2020

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“…Wang and Salehi [32] used artificial intelligence for prediction of optimum mud hydraulics during drilling operations and performed sensitivity analysis using forward regression. A variety of artificial intelligence works have recently been conducted in civil and oil engineering [33][34][35][36].…”

Section: Literature On Drilling Operationsmentioning

confidence: 99%

Applications of Artificial Intelligence Techniques in Optimizing Drilling

Koopialipoor¹,

Noorbakhsh²

2020

Emerging Trends in Mechatronics

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Artificial intelligence has transformed the industrial operations. One of the important applications of artificial intelligence is reducing the computational costs of optimization. Various algorithms based on their assumptions to solve problems have been presented and investigated, each of which having assumptions to solve the problems. In this chapter, firstly, the concept of optimization is fully explained. Then, an artificial bee colony (ABC) algorithm is used on a case study in the drilling industry. This algorithm optimizes the problem of study in combination with ANN modeling. At the end, various models are fully developed and discussed. The results of the algorithm show that by better understanding the drilling data, the conditions can be improved.

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“…For design of the models, 80% and 20% of dataset were assigned to training and testing sections, respectively. In this study, the authors decided to utilize a technique of classification namely the color intensity ranking (CIR) suggested by Koopialipoor et al [33] for each row of Table 2. According to this method, intensity of the color increases by increasing the rank values and similarly, it decreases by decreasing the rank values.…”

Section: Imperialist Competitive Algorithm-artificial Neural Network mentioning

confidence: 99%

“…Due to the high ability of the global search for these OAs, weights and deviations of an ANN can be determined to improve its performance forecast. The above-mentioned hybrid models have been widely-used to solve nonlinear and complex engineering problems [28][29][30][31][32][33].…”

Section: Introductionmentioning

confidence: 99%

Assessing Dynamic Conditions of the Retaining Wall: Developing Two Hybrid Intelligent Models

et al. 2019

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The precise estimation and forecast of the safety factor (SF) in civil engineering applications is considered as an important issue to reduce engineering risk. The present research investigates new artificial intelligence (AI) techniques for the prediction of SF values of retaining walls, as important and resistant structures for ground forces. These structures have complicated performances in dynamic conditions. Consequently, more than 8000 designs of these structures were dynamically evaluated. Two AI models, namely the imperialist competitive algorithm (ICA)-artificial neural network (ANN), and the genetic algorithm (GA)-ANN were used for the forecasting of SF values. In order to design intelligent models, parameters i.e., the wall thickness, stone density, wall height, soil density, and internal soil friction angle were examined under different dynamic conditions and assigned as inputs to predict SF of retaining walls. Various models of these systems were constructed and compared with each other to obtain the best one. Results of models indicated that although both hybrid models are able to predict SF values with a high accuracy and they can be introduced as new models in the field, the retaining wall performance could be properly predicted in dynamic conditions using the ICA-ANN model. Under these conditions, a combination of engineering design and artificial intelligence techniques can be used to control and secure retaining walls in dynamic conditions.

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Applying various hybrid intelligent systems to evaluate and predict slope stability under static and dynamic conditions

Cited by 179 publications

References 59 publications

Seepage Analysis in Short Embankments Using Developing a Metaheuristic Method Based on Governing Equations

Seepage Analysis in Short Embankments Using Developing a Metaheuristic Method Based on Governing Equations

Applications of Artificial Intelligence Techniques in Optimizing Drilling

Assessing Dynamic Conditions of the Retaining Wall: Developing Two Hybrid Intelligent Models

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