Slope stability analysis using recent metaheuristic techniques: a comprehensive survey

Mishra, Mayank; Gunturi, Venkata Ramana; Miranda, Tiago F. S.

doi:10.1007/s42452-019-1707-6

Cited by 16 publications

(4 citation statements)

References 91 publications

(148 reference statements)

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“…Slopes are terrains that serve as a support base for the soil, and may have a natural or artificial origin (man-made). Non-natural slopes consist mostly of embankments built to prevent landslides in places where their stability is not efficient [19]. The topography elevation, aspect and angle of a slope are the most important factors in geomorphology, which directly influences slope stability.…”

Section: Slope Stabilitymentioning

confidence: 99%

Deep learning based slope erosion detection

Limão

Araújo

Francês

2023

IJ-AI

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Being increasingly present at the most diverse structure health monitoring (SHM) scenarios, many high-performance artificial intelligence techniques have been able to solve structural analysis problems. When it comes to image classification solutions, convolutional neural networks (CNNs) deliver the best results. This scenario encourages us to explore machine learning techniques, such as computer vision, and merge it with different technologies to achieve the best performance. This paper proposes a custom CNN architecture trained with slope erosion images that showed satisfactory results with an accuracy of 96.67%, enabling a precise and improved identification of instability indicators. These instabilities, when detected in advance, prevent disasters and enable proper maintenance to be carried out, given that its integrity directly affects structures built around and above it.

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Section: Slope Stabilitymentioning

confidence: 99%

Deep learning based slope erosion detection

Limão

Araújo

Francês

2023

IJ-AI

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“…However, some applications in the field of slope stability have also been recently presented using artificial intelligence approaches [31][32][33][34][35]. Mishra et al (2019) conducted slope stability assessment based on several recent meta-heuristic techniques. They compared the results with the antlion optimizer and made some recommendations for slope stability analysis [36].…”

Section: Introductionmentioning

confidence: 99%

“…Mishra et al (2019) conducted slope stability assessment based on several recent meta-heuristic techniques. They compared the results with the antlion optimizer and made some recommendations for slope stability analysis [36]. Yuan and Moayedi (2019) investigated slope stability analysis and failure recognition using several artificial intelligence techniques.…”

Section: Introductionmentioning

confidence: 99%

Application of Harmony Search Algorithm to Slope Stability Analysis

Haghshenas

Geem

et al. 2021

Land

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Slope stability analysis is undoubtedly one of the most complex problems in geotechnical engineering and its study plays a paramount role in mitigating the risk associated with the occurrence of a landslide. This problem is commonly tackled by using limit equilibrium methods or advanced numerical techniques to assess the slope safety factor or, sometimes, even the displacement field of the slope. In this study, as an alternative approach, an attempt to assess the stability condition of homogeneous slopes was made using a machine learning (ML) technique. Specifically, a meta-heuristic algorithm (Harmony Search (HS) algorithm) and K-means algorithm were employed to perform a clustering analysis by considering two different classes, depending on whether a slope was unstable or stable. To achieve the purpose of this study, a database made up of 19 case studies with 6 model inputs including unit weight, intercept cohesion, angle of shearing resistance, slope angle, slope height and pore pressure ratio and one output (i.e., the slope safety factor) was established. Referring to this database, 17 out of 19 slopes were categorized correctly. Moreover, the obtained results showed that, referring to the considered database, the intercept cohesion was the most significant parameter in defining the class of each slope, whereas the unit weight had the smallest influence. Finally, the obtained results showed that the Harmony Search algorithm is an efficient approach for training K-means algorithms.

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“…However, these algorithms face two primary difficulties. Firstly, most of them require the proper assignment of suitable values for algorithm-specific parameters which would be unique to those algorithms for a certain detection study [14]. For example, GA requires the provision of optimum values of crossover probability and mutation probability [15], ant-colony optimiser requiring its pheromone evaporation rate related parameters [16,17], while PSO requires to define proper constriction coefficients and inertia factor in order to obtain the best results [18].…”

Section: Introductionmentioning

confidence: 99%

Teaching–learning-based optimization algorithm for solving structural damage detection problem in frames via changes in vibration responses

Kundu

Mishra

Maity

2021

Archit. Struct. Constr.

Self Cite

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Optimization algorithms are primarily responsible for efficiency in vibration-based damage detection particularly when utilizing the inverse approach. A complex problem of damage detection tends to converge into local minima, generated by a false damaged state which produces a response that is almost similar to the actual damaged state. Hence, there is a need for an efficient and accurate soft computing technique that can find the global minima or the actual damaged state. Recently, the teaching-learning based optimization (TLBO) algorithm has become quite popular due to its superior performance especially when compared to other metaheuristic algorithms. In this paper, damage estimation capability of the TLBO for frame structures and a benchmark problem of cantilever beam is studied and comparisons are made with some established soft computing techniques. TLBO is observed to produce better results relative to the other artificial intelligence-based techniques used for structural health monitoring.

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Slope stability analysis using recent metaheuristic techniques: a comprehensive survey

Cited by 16 publications

References 91 publications

Deep learning based slope erosion detection

Deep learning based slope erosion detection

Application of Harmony Search Algorithm to Slope Stability Analysis

Teaching–learning-based optimization algorithm for solving structural damage detection problem in frames via changes in vibration responses

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