Artificial intelligence application for predicting slope stability on soft ground: a comparative study

Omar, Mohd Badrul Hafiz Che; Mamat, Rufaizal Che; Rasam, Abdul Rauf Abdul; Ramli, Azuin; Samad, Abd Manan

doi:10.19101/ijatee.2020.762139

Cited by 11 publications

(6 citation statements)

References 13 publications

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“…In this analysis, ANFIS has been the most popular and successful neuro-fuzzy system used in diverse applications and different research domains, including business, medicine, IS, construction, and engineering. Omar et al (2021) compared ANFIS and ANN model accuracies using the RMSE value to forecast the slope stability on soft ground, and it was found that the former model RMSE was higher than the latter RMSE. In that sense, a big data mining-based paddy leaves monitoring system model elaborated on five phases: image acquisition, segmentation, feature extraction, feature selection, and ANFIS classification, showed improved accuracy of 97.28 percent compared to other models accuracies (SVM classifier: 91.2%, KNN: 85.3% and ANN: 88.78%) (Suresh et al, 2020).…”

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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“…In some cases, the application of deep learning techniques in more traditional scientific disciplines can unlock unseen capabilities (e.g., control theory in geomechanics [ 13 ]). In other cases, deep learning has been limited to fitting problems, e.g., in the prediction of pile bearing capacity [ 14 ], slope staility [ 15 ], or rupture distances and capillary forces in wet granular media [ 16 ]. Fitting problems can be enriched with physics and thermodynamics supervision as well [ 17 ].…”

Section: Introductionmentioning

confidence: 99%

FEM-GAN: A Physics-Supervised Deep Learning Generative Model for Elastic Porous Materials

Argilaga

2023

Materials

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X-ray μCT imaging is a common technique that is used to gain access to the full-field characterization of materials. Nevertheless, the process can be expensive and time-consuming, thus limiting image availability. A number of existing generative models can assist in mitigating this limitation, but they often lack a sound physical basis. This work presents a physics-supervised generative adversarial networks (GANs) model and applies it to the generation of X-ray μCT images. FEM simulations provide physical information in the form of elastic coefficients. Negative X-ray μCT images of a Hostun sand were used as the target material. During training, image batches were evaluated with nonparametric statistics to provide posterior metrics. A variety of loss functions and FEM evaluation frequencies were tested in a parametric study. The results show, that in several test scenarios, FEM-GANs-generated images proved to be better than the reference images for most of the elasticity coefficients. Although the model failed at perfectly reproducing the three out-of-axis coefficients in most cases, the model showed a net improvement with respect to the GANs reference. The generated images can be used in data augmentation, the calibration of image analysis tools, filling incomplete X-ray μCT images, and generating microscale variability in multiscale applications.

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“…Using artificial intelligence techniques, it is possible to determine the relationship between different parameters with a high degree of accuracy, without prior knowledge. Various topics in geotechnical engineering, such as slope stability [25][26][27], tunneling [28][29][30], pavement and road construction [31,32], soil cracking [33][34][35], rock mechanics [36,37], soil dynamics [38][39][40][41], and soil stabilizers [42][43][44] have been addressed using artificial intelligence methods [45]. Nevertheless, only two studies have used artificial intelligence to predict the properties arising from mixing sludge with soil [46,47].…”

Section: Introductionmentioning

confidence: 99%

Improving Soil Stability with Alum Sludge: An AI-Enabled Approach for Accurate Prediction of California Bearing Ratio

et al. 2023

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Alum sludge is a byproduct of water treatment plants, and its use as a soil stabilizer has gained increasing attention due to its economic and environmental benefits. Its application has been shown to improve the strength and stability of soil, making it suitable for various engineering applications. However, to go beyond just measuring the effects of alum sludge as a soil stabilizer, this study investigates the potential of artificial intelligence (AI) methods for predicting the California bearing ratio (CBR) of soils stabilized with alum sludge. Three AI methods, including two black box methods (artificial neural network and support vector machines) and one grey box method (genetic programming), were used to predict CBR, based on a database with nine input parameters. The results demonstrate the effectiveness of AI methods in predicting CBR with good accuracy (R2 values ranging from 0.94 to 0.99 and MAE values ranging from 0.30 to 0.51). Moreover, a novel approach, using genetic programming, produced an equation that accurately estimated CBR, incorporating seven inputs. The analysis of parameter sensitivity and importance, revealed that the number of hammer blows for compaction was the most important parameter, while the parameters for maximum dry density of soil and mixture were the least important. This study highlights the potential of AI methods as a useful tool for predicting the performance of alum sludge as a soil stabilizer.

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Artificial intelligence application for predicting slope stability on soft ground: a comparative study

Cited by 11 publications

References 13 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

FEM-GAN: A Physics-Supervised Deep Learning Generative Model for Elastic Porous Materials

Improving Soil Stability with Alum Sludge: An AI-Enabled Approach for Accurate Prediction of California Bearing Ratio

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