Prediction of Uplift Capacity of Cylindrical Caissons in Anisotropic and Inhomogeneous Clays Using Multivariate Adaptive Regression Splines

Jearsiripongkul, Thira; Lai, Van Qui; Keawsawasvong, Suraparb; Nguyen, Thanh Son; Van, Chung Nguyen; Thongchom, Chanachai; Nuaklong, Peem

doi:10.3390/su14084456

Cited by 23 publications

(5 citation statements)

References 53 publications

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“…At present, suction caissons are widely used as the foundation structure of offshore wind turbines because of their technical feasibility, convenient transportation and installation, and cost-effectiveness [86]. Many scholars have conducted detailed research on offshore wind turbine foundations which can serve as a reference when designing underwater accumulators [87][88][89][90][91][92][93]. The loads on the foundation structure of underwater gas storage accumulators are similar to those of the offshore wind turbine foundations, but there are significant distinctions.…”

Section: Underwater Gas Storagementioning

confidence: 99%

Underwater Compressed Gas Energy Storage (UWCGES): Current Status, Challenges, and Future Perspectives

Wang

Liang

et al. 2022

Applied Sciences

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Underwater compressed air energy storage was developed from its terrestrial counterpart. It has also evolved to underwater compressed natural gas and hydrogen energy storage in recent years. UWCGES is a promising energy storage technology for the marine environment and subsequently of recent significant interest attention. However, it is still immature. In this study, the latest progress in both academic and industrial fields is summarized. Additionally, challenges facing this emerging technology are analyzed. The pros and cons of UWCGES are provided and are differentiated from the terrestrial variant. Technical, economic, environmental, and policy challenges are examined. In particular, the critical issues for developing artificial large and ultra-large underwater gas storage accumulators and effective underwater gas transportation are comprehensively analyzed. Finally, the demand for marine energy storage technology is briefly summarized, and the potential application scenarios and application modes of underwater compressed gas energy storage technology are prospected. This study aims to highlight the current state of the UWCGES sector and provide some guidance and reference for theoretical research and industrial development.

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Section: Underwater Gas Storagementioning

confidence: 99%

Underwater Compressed Gas Energy Storage (UWCGES): Current Status, Challenges, and Future Perspectives

Wang

Liang

et al. 2022

Applied Sciences

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“…Furthermore, MARS does not need any assumption between input and output variables. MARS is suitable for high-dimensional issues that contain many input and output variables (e.g., Zheng et al, 2019b;Zhang et al, 2019;Jearsiripongkul et al, 2022;Lai et al, 2022;Sirimontree et al, 2022;Yodsomjai et al, 2022).…”

Section: Figure 14mentioning

confidence: 99%

A machine learning regression approach for predicting the bearing capacity of a strip footing on rock mass under inclined and eccentric load

Lai

Sangjinda

Keawsawasvong

et al. 2022

Front. Built Environ.

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In this study, the Multivariate Adaptive Regression Splines (MARS) model is employed to create a data-driven prediction for the bearing capacity of a strip footing on rock mass subjected to an inclined and eccentric load. The strengths of rock masses are based on the Hoek-Brown failure criterion. To develop the set of training data in MARS, the lower and upper bound finite element limit analysis (FELA) is carried out to obtain the numerical results of the bearing capacity of a strip footing with the width of B. There are six considered dimensionless variables, including the geological strength index (GSI), the rock constant/yield parameter (mi), the dimensionless strength (γB/σci), the adhesion factor (α), load inclined angle from the vertical axis (β), and the eccentricity of load (e/B). A total of 5,120 FELA solutions of the bearing capacity factor (P/σciB) are obtained and used as a training data set. The influences of all dimensionless variables on the bearing capacity factors and the failure mechanisms are investigated and discussed in detail. The sensitivity analysis of these dimensionless variables is also examined.

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“…Compare to another machine learning approach (i.e., artificial neural networks (ANN), least-square support vector regression, extreme learning machine, Gaussian process regression [71][72][73][74][75][76][77]) MARS are considered as an effective approach [74,78]. Moreover, MARS model has been successfully applied to a number of geotechnical applications (see, e.g., [79][80][81][82][83][84][85][86][87][88][89][90][91][92][93]). Further details of MARS model can be found in Zhang [94].…”

Section: Sensitivity Study Using Mars Modelmentioning

confidence: 99%

Determining Seismic Bearing Capacity of Footings Embedded in Cohesive Soil Slopes Using Multivariate Adaptive Regression Splines

Lai

Yang

et al. 2022

Int. J. of Geosynth. and Ground Eng.

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Seismic bearing capacity of strip footings in cohesive soil slopes considering various embedded depths is investigated in this study. Novel solutions using pseudo-static method and finite element limit analysis (FELA) with upper bound (LB) and lower bound (LB) theorems are presented. The influences of footing depth, slope angle, slope height, undrained shear strength and pseudo-static acceleration on bearing capacity and failure mechanisms are examined using dimensionless parameters. With the comprehensive numerical results, the multivariate adaptive regression splines (MARS) model is then utilized to simulate the sensitivity of all dimensionless input parameters (i.e., the normalized depth of footing D/B, the normalized slope height H/B, the normalized distance from top slope to edge of the footing L/B, slope angle β, the strength ratio cu/γB, and the pseudo-static acceleration factor, kh). The degree of influence of each design parameter is produced, and an empirical equation for the dimensionless output parameter (i.e., bearing capacity factor Nc) is proposed. The study results are accessible in the design charts, tables, empirical equation for design practitioners.

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Prediction of Uplift Capacity of Cylindrical Caissons in Anisotropic and Inhomogeneous Clays Using Multivariate Adaptive Regression Splines

Cited by 23 publications

References 53 publications

Underwater Compressed Gas Energy Storage (UWCGES): Current Status, Challenges, and Future Perspectives

Underwater Compressed Gas Energy Storage (UWCGES): Current Status, Challenges, and Future Perspectives

A machine learning regression approach for predicting the bearing capacity of a strip footing on rock mass under inclined and eccentric load

Determining Seismic Bearing Capacity of Footings Embedded in Cohesive Soil Slopes Using Multivariate Adaptive Regression Splines

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