Artificial neural networks in slope of road embankment stability applications: a review and future perspectives

Mamat, Rufaizal Che; Ramli, Azuin; Samad, Abd Manan; Kasa, Anuar; Razali, Siti Fatin Mohd; Omar, Mohd Badrul Hafiz Che

doi:10.19101/ijatee.2020.762127

Cited by 8 publications

(2 citation statements)

References 27 publications

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“…The ANFIS model utilises the TSK FIS (Jang, 1993;Soroush et al, 2019). In the review of Mamat et al (2021), the ANN models' efficacy in embankment stability modelling and prediction with acceptable accuracy underlined the need to extend ANNs to ANFIS models in further research. The ANFIS model was adopted to predict the students' course success with an obtained RMSE equal to 0.36 and represented a decision support system on which students can base to calculate their success on courses (Kaynak et al, 2014).…”

Section: Neuro-fuzzy Modelsmentioning

confidence: 99%

A Sugeno ANFIS Model Based on Fuzzy Factor Analysis for IS/IT Project Portfolio Risk Prediction

Zaidouni,

Janati Idrissi,

Bellabdaoui

2024

JICT

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Risk inherence jeopardises Information System (IS) and Information Technology (IT) Project Portfolio Management (PPM) to realise thestrategic objectives. Previous studies have mainly provided Artificial Intelligence (AI) and statistical models to predict the overall riskof IS/IT project portfolio, whereas neuro-fuzzy models were rarely used. This paper proposes a Sugeno Adaptive Neuro-Fuzzy InferenceSystem (ANFIS) model based on Fuzzy Factor Analysis (FFA) named ANFIS-OPR to predict the overall risk of IS/IT project portfolio fromhistorical IS/IT project risk data. The ANFIS-OPR inputs are the relevant factor loadings resulting from the FFA application on the IS/IT projects risks set to cope with the curse of dimensionality. Then, the Sugeno ANFIS model is adopted to give strategic interpretability to the predicted IS/IT project portfolio overall risk by implementing the IS/IT Project Management Office (PMO) expert knowledge, represented by fuzzy rules, on the relationship between IS/IT project portfolio strategic alignment and the IS/IT projects risks. The ANFISOPR outputs are the predicted Overall Portfolio Risk (OPR) and Root Mean Square Error (RMSE). The paper also presents an IS/IT PMO case study that shows the proposed ANFIS-OPR efficacy, which predicted the OPR values closely to the OPR estimates with an accepted RMSE of 0.108. The proposed ANFIS-OPR is a novel intelligent decision-making tool that enables the IS/IT PMO to monitor the OPR, considering its linkage with strategic alignment; thus, contingency plans can be carried out appropriately while ensuring that the IS/IT project portfolio is strategically aligned.

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Section: Neuro-fuzzy Modelsmentioning

confidence: 99%

A Sugeno ANFIS Model Based on Fuzzy Factor Analysis for IS/IT Project Portfolio Risk Prediction

Zaidouni,

Janati Idrissi,

Bellabdaoui

2024

JICT

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“…This is regarded as among the most significant benefits of the standard soft computing methods used in today's world. In the literature on slope stability, successful implementations of these soft computing approaches may be identified (Wang et al, 2005;Choobbasti et al, 2009;Li et al, 2009;Chakraborty and Goswami, 2017;Kumar and Basudhar, 2018;Qian et al, 2019;Ahour et al, 2020;Li et al, 2020;Ray et al, 2020;Zheng et al, 2020;Che Mamat et al, 2021;Palazzolo et al, 2021); (Das et al, 2011;Erzin and Cetin, 2012;Erzin and Cetin, 2014;Abdalla et al, 2015;Ai and Zsaki, 2017;Chakraborty and Goswami, 2018;Rukhaiyar et al, 2018;Moayedi et al, 2019;Bui et al, 2020;Chen et al, 2020;He et al, 2020;Liao and Liao, 2020;Che Mamat et al, 2020;Markovic Brankovic et al, 2021;Meng et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

Evaluation Circular Failure of Soil Slopes Using Classification and Predictive Gene Expression Programming Schemes

Hanandeh

2022

Front. Built Environ.

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This article presents an analysis of a dataset related to slope stability circular failure cases using the GeneXproTools program. Two models were developed: The first model was utilized to find a classification model that shows the stability status (S), and the second model was utilized to estimate the factor of safety and predict the mathematical model to serve as an indicator of slope stability. The proposed models include cohesion, unit weight, slope angle, coefficient of pore water pressure, internal friction angle, slope height, internal friction angle, and safety factor. The results showed that the values of accuracy and error were 93.1 and 6.9%, respectively, for classification model 1. The relationship between the actual values of the factor of safety and the output of model 2 was an R2 value of 0.96. The models developed were subjected to sensitivity analysis to determine the input variables’ impact on output for the two models. The selected slope locations with different material characterizations were introduced into geox5 software, and the results were compared to those of the two developed models. Furthermore, when the proposed models were compared to the available models in the literature, the results showed that the proposed models in this study performed better than the available models.

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Self-Evolution LSTM Network with Knowledge-Guided Multi-Objective Particle Swarm Optimization for Road Gradient Prediction

Zhang,

Han,

Qin

et al. 2024

2024 IEEE 13th Data Driven Control and Learning Systems Conference (DDCLS)

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Artificial neural networks in slope of road embankment stability applications: a review and future perspectives

Cited by 8 publications

References 27 publications

A Sugeno ANFIS Model Based on Fuzzy Factor Analysis for IS/IT Project Portfolio Risk Prediction

A Sugeno ANFIS Model Based on Fuzzy Factor Analysis for IS/IT Project Portfolio Risk Prediction

Evaluation Circular Failure of Soil Slopes Using Classification and Predictive Gene Expression Programming Schemes

Self-Evolution LSTM Network with Knowledge-Guided Multi-Objective Particle Swarm Optimization for Road Gradient Prediction

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