Journal ArticleDOI
Efficient computational techniques for predicting the California bearing ratio of soil in soaked conditions
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TLDR
The proposed MARS-L model is very potential to be an alternate solution to estimate the CBR value in different phases of civil engineering projects, and has the most accurate prediction in predicting the soaked CBR at all stages.About:
This article is published in Engineering Geology.The article was published on 2021-09-20. It has received 45 citations till now. The article focuses on the topics: Multivariate adaptive regression splines.read more
Citations
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Predicting Resilient Modulus of Flexible Pavement Foundation Using Extreme Gradient Boosting Based Optimized Models
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Predicting resilient modulus of flexible pavement foundation using extreme gradient boosting based optimised models
Journal ArticleDOI
Predicting the thermal conductivity of soils using integrated approach of ANN and PSO with adaptive and time-varying acceleration coefficients
TL;DR: In this article , a hybrid adaptive neuro swarm intelligence (HANSI) technique was proposed for predicting the thermal conductivity of unsaturated soils, which integrated artificial neural networks (ANNs) and particle swarm optimisation (PSO) with adaptive and time-varying acceleration coefficients.
Journal ArticleDOI
Soft computing techniques for the prediction of concrete compressive strength using Non-Destructive tests
TL;DR: Experimental results show that the BPNN attained the most accurate prediction of concrete CS based on both ultrasonic pulse velocity and rebound number values, and these two models are very potential to assist engineers in the design phase of civil engineering projects to estimate the concrete CS with a greater accuracy level.
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Prediction of rapid chloride penetration resistance of metakaolin based high strength concrete using light GBM and XGBoost models by incorporating SHAP analysis
Anas Abdulalim Alabdullah,Mudassir Iqbal,Muhammad Zahid,Kaffayatullah Khan,Muhammad Nasir Amin,Fazal E. Jalal +5 more
TL;DR: In this article , the authors investigated the non-linear capabilities of two machine learning prediction models, namely Light GBM and XGBoost, for predicting the values of Rapid Chloride Penetration Test (RCPT).
References
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Journal ArticleDOI
An improvement to MLR model for predicting liquefaction-induced lateral spread using multivariate adaptive regression splines
Anthony T. C. Goh,Wengang Zhang +1 more
TL;DR: In this paper, a nonparametric regression procedure known as multivariate adaptive regression splines (MARS) was proposed to predict the liquefaction induced lateral displacement, which automatically models nonlinearities and interactions between variables without making any specific assumptions.
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Estimation of California bearing ratio by using soft computing systems
B. Yildirim,O. Gunaydin +1 more
TL;DR: It has been shown that the correlation equations obtained as a result of regression analyses are in satisfactory agreement with the test results and it is recommended that the proposed correlations will be useful for a preliminary design of a project where there is a financial limitation and limited time.
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A novel technique based on the improved firefly algorithm coupled with extreme learning machine (ELM-IFF) for predicting the thermal conductivity of soil
Navid Kardani,Abidhan Bardhan,Pijush Samui,Majidreza Nazem,Annan Zhou,Danial Jahed Armaghani +5 more
TL;DR: All the proposed hybrid models have a great ability to be considered as alternatives for empirical relevant models and can be employed in the initial stages of any engineering projects for fast determination of thermal conductivity.
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Effects of microstructure on desiccation cracking of a compacted soil
TL;DR: In this article, the effects of microstructure on the desiccation cracking of a compacted lean clay were investigated and the change in water content and the evolution of surface crack pattern during the drying process were continuously monitored.
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Modelling the energy performance of residential buildings using advanced computational frameworks based on RVM, GMDH, ANFIS-BBO and ANFIS-IPSO
TL;DR: Four advanced computational frameworks including relevance vector machine (RVM), group method of data handling (GMDH), hybridization of adaptive neuro-fuzzy interface system (ANFIS) and biogeography-based optimisation (BBO) are proposed as novel approaches to predict the heating load (HL) and cooling load (CL) of residential buildings.