Future of machine learning in geotechnics

Phoon, Kok‐Kwang; Zhang, Wengang

doi:10.1080/17499518.2022.2087884

Cited by 100 publications

(29 citation statements)

References 58 publications

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“…This perhaps suggests that the performance capability is not necessarily associated to the sample of data, but to the quality of data, and the inclusion of specific parameters in the predictive models. This links to what Phoon and Zhang [126] suggest that a deep appreciation of the geotechnical context is critical to the development of novel ML methods that can lead to 'data-centric geotechnics' as a distinctive field that can transform practice.…”

Section: Discussionmentioning

confidence: 67%

“…During the last two decades ML have been successfully applied to solve various problems in geotechnical engineering applications. In a survey of 444 papers by ISSMGE TC304/309 in 2021, both supervised and unsupervised algorithms are used with ANN, Support vector machines, nearest neighbour classifiers and Bayesian networks, being the most popular amongst them employed to solve problems such as site characterization, geomaterial behaviour modelling, foundations, retaining structures, slope stability, landslides, tunnels and underground openings, liquefaction assessment, etc, [126].…”

Section: Machine Learning Methodsmentioning

confidence: 99%

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Generic compressive strength prediction model applicable to multiple lithologies based on a broad global database

Muzamhindo

Ferentinou

2023

Probabilistic Engineering Mechanics

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Section: Discussionmentioning

confidence: 67%

Section: Machine Learning Methodsmentioning

confidence: 99%

Generic compressive strength prediction model applicable to multiple lithologies based on a broad global database

Muzamhindo

Ferentinou

2023

Probabilistic Engineering Mechanics

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“…Other examples of the use of ABAs in the railway maintenance purpose are In 2021, the use of ABAs to detect and classify the severity of wheelflats 23 , to detect and classify the railway combined defects 24 , different railway defect detection [25][26][27][28] , defect severity classification [29][30][31][32][33] or railway operation 34,35 . Moreover, machine learning also has the potential in different areas such as geoengineering and geoscience [36][37][38][39] .…”

Section: Literature Reviewmentioning

confidence: 99%

Automated machine learning recognition to diagnose flood resilience of railway switches and crossings

Sresakoolchai

Hamarat

Kaewunruen

2023

Sci Rep

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The increase in demand for railway transportation results in a significant need for higher train axle load and faster speed. Weak and sensitive trackforms such as railway switches and crossings (or called ‘turnout’) can suffer from such an increase in either axle loads or speeds. Moreover, railway turnout supports can deteriorate from other incidences due to extreme weather such as floods which undermine cohesion between ballast leading to ballast washaway or loss of support under turnout structures. In this study, new intelligent automation based on machine learning pattern recognition has been built to detect and predict the deterioration of railway turnouts exposed to flooding conditions which is the scope of this study. Since the turnout system is very complex by nature, different features and smart filtering are explored to find the potential features for deep learning. Nonlinear finite element models validated by actual field measurements are used to mimic the dynamic behaviors of turnout supports under flooding conditions. The study exhibits that the novel recognition model can achieve more than 98% accuracy, yielding the potential capability to recognize and classify turnout support deteriorations facing extreme weather conditions which will be beneficial for responsible parties to schedule and plan maintenance activities.

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“…Geotechnical researchers have demonstrated that machine learning (ML)-based methods can effectively solve complex geotechnical problems (Karthikeyan and Samui,2014;Fahim et al, 2022;Ghani and Kumari, 2022;Phoon and Zhang, 2022;Tehrani et al, 2022;Dehghanbanadaki, 2021). Arti cial intelligence (AI)-based solutions have also gained popularity recently for solving geotechnical problems (Uncuoglu et al, 2022;Baghbani et al, 2022).…”

Section: Introductionmentioning

confidence: 99%

Enhanced Soil Liquefaction Potential Estimation using Machine Learning and Web-Based Platform

Vasegh

Dehghanbanadaki

Motamedi

2023

Preprint

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In this study, a new web-based platform was developed for fast estimation of soil liquefaction potential (SLP). The geotechnical results from 47 boreholes in the north of Iran were collected over three years to create an estimator model. The dataset included information on SPT, soil type, strength parameters, and water content. Python libraries Pycaret and Gradio were used to develop the model for SLP. A set of pipeline codes were applied to base classifiers, including 13 different machine learning models such as the Ada boost classifier (ad), decision tree classifier (dt), gradient boosting classifier (gb), the k-neighbors classifier (knn), light gradient boosting machine (lightgbm) and random forest classifier (rf). The results show that the lightgbm model outperformed the other applied machine learning classifiers with accuracy = 0.946, AUC = 0.982, and F1-score = 0.9. The proposed model was then used as the primary element of the web-based application, providing a helpful tool for geotechnical engineers to determine SLP.

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Future of machine learning in geotechnics

Cited by 100 publications

References 58 publications

Generic compressive strength prediction model applicable to multiple lithologies based on a broad global database

Generic compressive strength prediction model applicable to multiple lithologies based on a broad global database

Automated machine learning recognition to diagnose flood resilience of railway switches and crossings

Enhanced Soil Liquefaction Potential Estimation using Machine Learning and Web-Based Platform

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