Editorial for Special Issue “Applications of Artificial Intelligence and Machine Learning in Geotechnical Engineering”

Jaksa, M.; Liu, Zhongqiang

doi:10.3390/geosciences11100399

Cited by 12 publications

(3 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Goh was one of the first, if not the first, proponents of ANNs in geotechnical engineering, and he released a study in 1994 that used ANNs to evaluate the possibility for seismic liquefaction (Jaksa and Liu, 2021).…”

Section: Machine Learning For Predicting Pile Capacitymentioning

confidence: 99%

Predicting Geotechnical Axial Capacity of Reinforced Concrete Driven Pile Using Machine Learning Technique

Zi Xun,

Abdullah

2023

MJCE

View full text Add to dashboard Cite

Modified Meyerhof method is a popular method to calculate pile geotechnical axial capacity in Malaysia currently. From past experience, pile design based on empirical and analytical method produce variability of predicted capacity, in which, there is a wide scatter of predicted capacities and tendency for the predictions to be conservative, i.e. to underestimate the load capacity. This study provides options of machine learning and statistical approach for prediction of pile capacity based on soil investigation and dynamic pile load test result. It serves as an additional checking for engineer during design of pile based on conventional empirical method. It also helps to provide deeper insights of non-linear variables related to pile capacity through machine learning and statistical approach. This study helps engineer to design pile foundation optimally, economically and safely. The prediction of pile geotechnical axial capacity with machine learning technique and statistical approach for local marine clay soil in Penang, Malaysia is proposed in this study. The information from soil investigation report and dynamic pile load test report are gathered from six projects at Batu Kawan and Nibong Tebal located in Penang state that contributed 439 numbers of data. The skin friction factor, end bearing factor and pile geotechnical axial capacity are computed and predicted using empirical method, machine learning model and statistical model. Support Vector Machine illustrate best fit model for predicting skin friction factor with R2 of 0.517 while Random Forest seems to be the best fit model for predicting end bearing factor with R2 of 0.264. Random Forest is found to be the best model in predicting the geotechnical pile axial capacity compare to other models as it explains 96.2% of the variability of pile capacity.

show abstract

Section: Machine Learning For Predicting Pile Capacitymentioning

confidence: 99%

Predicting Geotechnical Axial Capacity of Reinforced Concrete Driven Pile Using Machine Learning Technique

Zi Xun,

Abdullah

2023

MJCE

View full text Add to dashboard Cite

show abstract

“…As a result, this step is extremely crucial and might also be the most neglected when researching a particular subject. The first stage involved filtering titles and abstracts to weed out duplicate and irrelevant studies publications [54,56,80]. The second stage of the process involved reading the full texts of the chosen research articles [40,55].…”

Section: Study Selectionmentioning

confidence: 99%

A Systematic Review of Machine Learning Techniques and Applications in Soil Improvement Using Green Materials

et al. 2023

View full text Add to dashboard Cite

According to an extensive evaluation of published studies, there is a shortage of research on systematic literature reviews related to machine learning prediction techniques and methodologies in soil improvement using green materials. A literature review suggests that machine learning algorithms are effective at predicting various soil characteristics, including compressive strength, deformations, bearing capacity, California bearing ratio, compaction performance, stress–strain behavior, geotextile pullout strength behavior, and soil classification. The current study aims to comprehensively evaluate recent breakthroughs in machine learning algorithms for soil improvement using a systematic procedure known as PRISMA and meta-analysis. Relevant databases, including Web of Science, ScienceDirect, IEEE, and SCOPUS, were utilized, and the chosen papers were categorized based on: the approach and method employed, year of publication, authors, journals and conferences, research goals, findings and results, and solution and modeling. The review results will advance the understanding of civil and geotechnical designers and practitioners in integrating data for most geotechnical engineering problems. Additionally, the approaches covered in this research will assist geotechnical practitioners in understanding the strengths and weaknesses of artificial intelligence algorithms compared to other traditional mathematical modeling techniques.

show abstract

“…For this, a neural network (NN) tool will be used. Due to their superiority over the traditionally used statistical and experimental methods, NNs have been extensively used in field of geotechnical engineering [20][21][22][23][24][25][26]. Among their numerous advantages, a remarkable information processing capability pertinent to nonlinearity is of a highest benefit.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of Creep Behavior of Soft Soils by Utilizing Multisensor Data Combined with Machine Learning

Kovačević

Bačić

Librić

et al. 2022

Sensors

View full text Add to dashboard Cite

To identify the unknown values of the parameters of Burger’s constitutive law, commonly used for the evaluation of the creep behavior of the soft soils, this paper demonstrates a procedure relying on the data obtained from multiple sensors, where each sensor is used to its best advantage. The geophysical, geotechnical, and unmanned aerial vehicle data are used for the development of a numerical model whose results feed into the custom-architecture neural network, which then provides information about on the complex relationships between the creep characteristics and soil displacements. By utilizing InSAR and GPS monitoring data, particle swarm algorithm identifies the most probable set of Burger’s creep parameters, eventually providing a reliable estimation of the long-term behavior of soft soils. The validation of methodology is conducted for the Oostmolendijk embankment in the Netherlands, constructed on the soft clay and peat layers. The validation results show that the application of the proposed methodology, which relies on multisensor data, can overcome the high cost and long duration issues of laboratory tests for the determination of the creep parameters and can provide reliable estimates of the long-term behavior of geotechnical structures constructed on soft soils.

show abstract

Editorial for Special Issue “Applications of Artificial Intelligence and Machine Learning in Geotechnical Engineering”

Abstract: Since its inception in the mid-1950s [1], artificial intelligence (AI) has become a disruptive and pervasive technology. [...]

Cited by 12 publications

References 10 publications

Predicting Geotechnical Axial Capacity of Reinforced Concrete Driven Pile Using Machine Learning Technique

Predicting Geotechnical Axial Capacity of Reinforced Concrete Driven Pile Using Machine Learning Technique

A Systematic Review of Machine Learning Techniques and Applications in Soil Improvement Using Green Materials

Evaluation of Creep Behavior of Soft Soils by Utilizing Multisensor Data Combined with Machine Learning

Contact Info

Product

Resources

About