35 Years of (AI) in Geotechnical Engineering: State of the Art

Ebid, Ahmed M.

doi:10.1007/s10706-020-01536-7

Cited by 95 publications

(59 citation statements)

References 476 publications

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“…Various methods explored in this study belong to a subsets of SC techniques, namely Machine Learning (ML). According to two state-of-the-art reviews of SC applications [17,64], the adoption of SC techniques in geotechnical engineering is growing exponentially. ANN is the leading technique and represents about half of the studies in this field.…”

Section: Soft Computing Techniquesmentioning

confidence: 99%

A soft computing approach to tunnel face stability in a probabilistic framework

Soranzo

Guardiani

2021

Acta Geotech.

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Tunnel face is important for shallow tunnels to avoid collapses. In this study, tunnel face stability is studied with soft computing techniques. A database is created based on the literature which is used to train some broadly adopted soft computing techniques, ranging from linear regression to the artificial neural network. The soil dry density, cohesion, friction angle, cover depth and the tunnel diameter are used as the input parameters. The soft computing techniques state whether the face support is stable and predict the face support pressure. It is found that the artificial neural network outperforms the other techniques. The face support pressure is predicted with the artificial neural network for statistically distributed samples, and the failure probability is obtained with Monte Carlo simulations. In this way, the stability of the tunnel face can be reliably assessed and the support pressure can be estimated fairly accurately.

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Section: Soft Computing Techniquesmentioning

confidence: 99%

A soft computing approach to tunnel face stability in a probabilistic framework

Soranzo

Guardiani

2021

Acta Geotech.

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“…Hence, these are becoming integral parts of mineral processing. The readers can refer to reviews, like [85,86], for a review of the application of ML and AI algorithms in the holistic mining domain with a focus on ethical mineral processing.…”

Section: And Ai In Ethical and Green Mineral Processingmentioning

confidence: 99%

AI4R2R (AI for Rock to Revenue): A Review of the Applications of AI in Mineral Processing

Mishra

2021

Minerals

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In the last few years, jargon, such as machine learning (ML) and artificial intelligence (AI), have been ubiquitous in both popular science media as well as the academic literature. Many industries have tried the current suite of ML and AI algorithms with various degrees of success. Mineral processing, as an industry, is looking at AI for two reasons. First of all, as with other industries, it is pertinent to know if AI algorithms can be used to enhance productivity. The second reason is specific to the mining industry. Of late, the grade of ores is reducing, and the demand for ethical mining (with as little effect on ecology as possible) is increasing. Thus, mineral processing industries also want to explore the possible use of AI in solving these challenges. In this review paper, first, the challenges in mineral processing that can potentially be solved by AI are presented. Then, some of the most pertinent developments in the domain of ML and AI (applied in the domain of mineral processing) are discussed. Lastly, a top-level modus operandi is presented for a mineral processing industry that might want to explore the possibilities of using AI in its processes. Following are some of the new paradigms added by this review. This review presents a holistic view of the domain of mineral processing with an AI lens. It is also one of the first reviews in this domain to thoroughly discuss the use of AI in ethical, green, and sustainable mineral processing. The AI process proposed in this paper is a comprehensive one. To ensure the relevance to industry, the flow was made agile with the spiral system engineering flow. This is expected to drive rapid and agile investigation of the potential of applying ML and AI in different mineral processing industries.

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“…Prediction. Genetic programming (GP) is one of the datadriven evolutionary computation methods that explores a program space rather than just searching it in a process setting as shown in Figure 1, which is a unique advantage it claims over the genetic algorithm [24,25]. GP performs this exploration effectively by selecting datasets accordingly, fitting, and introducing genetic variation via some genetic operators [26,27].…”

Section: Genetic Programming (Gp) and Potential For Soil Lossmentioning

confidence: 99%

Evolutionary Prediction of Soil Loss from Observed Rainstorm Parameters in an Erosion Watershed Using Genetic Programming

Onyelowe

Ebid

Nwobia

2021

Applied and Environmental Soil Science

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Various environmental problems such as soil degradation and landform evolutions are initiated by a natural process known as soil erosion. Aggregated soil surfaces are dispersed through the impact of raindrop and its associated parameters, which were considered in this present work as function of soil loss. In an attempt to monitor environmental degradation due to the impact of raindrop and its associated factors, this work has employed the learning abilities of genetic programming (GP) to predict soil loss deploying rainfall amount, kinetic energy, rainfall intensity, gully head advance, soil detachment, factored soil detachment, runoff, and runoff rate database collected over a three-year period as predictors. Three evolutionary trials were executed, and three models were presented considering different permutations of the predictors. The performance evaluation of the three models showed that trial 3 with the highest parametric permutation, i.e., that included the influence of all the studied parameters showed the least error of 0.1 and the maximum coefficient of determination (R2) of 0.97 and as such is the most efficient, robust, and applicable GP model to predict the soil loss value.

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35 Years of (AI) in Geotechnical Engineering: State of the Art

Cited by 95 publications

References 476 publications

A soft computing approach to tunnel face stability in a probabilistic framework

A soft computing approach to tunnel face stability in a probabilistic framework

AI4R2R (AI for Rock to Revenue): A Review of the Applications of AI in Mineral Processing

Evolutionary Prediction of Soil Loss from Observed Rainstorm Parameters in an Erosion Watershed Using Genetic Programming

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