Estimation of ultimate bearing capacity of driven piles in c-φ soil using MLP-GWO and ANFIS-GWO models: a comparative study
Sign Up to like & getrecommendations! Published in 2021 at "Soft Computing"
DOI: 10.1007/s00500-020-05435-0
Abstract: A new forecast method is proposed in order to improve the estimation accuracy of the ultimate bearing capacity (UBC) of single driven piles. The performance of the adaptive neuro-fuzzy inference system (ANFIS) and multilayer perceptron… read more here.
Keywords: ultimate bearing; bearing capacity; gwo; driven piles ... See more keywords
Load–Displacement Behavior of Driven Piles in Sand Using CPT-Based Stress and Strain Fields
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DOI: 10.1007/s40999-018-0388-7
Abstract: The bearing capacity of piles is often estimated by a variety of methods such as the limit equilibrium or the limit analysis. In contrast, the load–displacement behavior, which should not be disregarded in common practices,… read more here.
Keywords: load displacement; stress strain; displacement behavior; driven piles ... See more keywords
Reclamation pathway of waste piles: A case study in Shanghai
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DOI: 10.1016/j.jclepro.2019.119919
Abstract: Abstract Reclaiming waste driven piles to use as the foundations of new buildings is an innovative and effective approach to manage waste driven piles for cleaner construction. A reclamation pathway for waste driven piles is… read more here.
Keywords: reclamation pathway; waste driven; waste; driven piles ... See more keywords
Evolutionary computing to determine the skin friction capacity of piles embedded in clay and evaluation of the available analytical methods
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DOI: 10.1016/j.trgeo.2020.100372
Abstract: Abstract Deep foundations are very important elements in the routine design of railways and bridges when the loads applied due to these important structures are higher than the bearing capacity of the soil. However, the… read more here.
Keywords: capacity; analytical methods; friction capacity; skin friction ... See more keywords
Optimizing the Prediction Accuracy of Friction Capacity of Driven Piles in Cohesive Soil Using a Novel Self-Tuning Least Squares Support Vector Machine
Sign Up to like & getrecommendations! Published in 2018 at "Advances in Civil Engineering"
DOI: 10.1155/2018/6490169
Abstract: This research presents a novel hybrid prediction technique, namely, self-tuning least squares support vector machine (ST-LSSVM), to accurately model the friction capacity of driven piles in cohesive soil. The hybrid approach uses LS-SVM as a… read more here.
Keywords: machine; capacity; friction; friction capacity ... See more keywords