封面图片

Prediction of bearing capacity of thin-walled foundation: a simulation approach

In the recent past years, utilization of intelligent models for solving geotechnical problems has received considerable attention. This paper highlights the feasibility of adaptive neuro-fuzzy inference system (ANFIS) for predicting the bearing capacity of thin-walled foundations. For this reason, a...

Saved in:

书目详细资料
Main Authors:	Momeni, Ehsan, Armaghani, Danial Jahed, Fatemi, Seyed Alireza, Nazir, Ramli
格式:	Article
出版:	Springer London 2018
主题:	TA Engineering (General). Civil engineering (General)
在线阅读:	http://eprints.utm.my/id/eprint/85655/ http://dx.doi.org/10.1007/s00366-017-0542-x
标签:	添加标签没有标签, 成为第一个标记此记录!

相似书籍

Bearing capacity of precast thin-walled foundation in sand
由: Momeni, Ehsan, et al.
出版: (2015)

Prediction of bearing capacity for thin-wall spread foundations using ICA-ANN predictive model
由: Nazir, Ramli, et al.
出版: (2015)

Prediction of pile bearing capacity using a hybrid genetic algorithm-based ANN
由: Momeni, Ehsan, et al.
出版: (2014)

Bearing capacity of shallow foundation's prediction through hybrid artificial neural networks
由: Marto, Aminaton, et al.
出版: (2014)

Comparative study on prediction of axial bearing capacity of driven piles in granular materials
由: Momeni, Ehsan, et al.
出版: (2013)

Application of artificial neural network for predicting shaft and tip resistances of concrete piles
由: Momeni, Ehsan, et al.
出版: (2015)

Experimental and intelligent techniques to estimate bearing capacity of cohesive soft soils reinforced with soil-cement columns
由: Bunawan, Aliff Ridzuan, et al.
出版: (2018)

A new hybrid simulated annealing-based genetic programming technique to predict the ultimate bearing capacity of piles
由: Yong, Weixun, et al.
出版: (2021)

A new hybrid simulated annealing-based genetic programming technique to predict the ultimate bearing capacity of piles
由: Yong, Weixun, et al.
出版: (2021)

Neuro-genetic, neuro-imperialism and genetic programing models in predicting ultimate bearing capacity of pile
由: Chen, Wusi, et al.
出版: (2020)

Axial bearing capacity of piles and modelling of distribution of skin resistance with depth
由: Momeni, Ehsan
出版: (2012)

Prediction of bearing capacity of the square concrete-filled steel tube columns: An application of metaheuristic-based neural network models
由: Sarir, Payam, et al.
出版: (2022)

An efficient optimal neural network based on gravitational search algorithm in predicting the deformation of geogrid-reinforced soil structures
由: Momeni, Ehsan, et al.
出版: (2021)

Prediction of unconfined compressive strength of rocks: a review paper
由: Momeni, Ehsan, et al.
出版: (2015)

Prediction of ultimate bearing capacity of shallow foundations on cohesionless soils: a gaussian process regression approach
由: Ahmad, Mahmood, et al.
出版: (2021)

Correlation between cone penetration test and bearing capacity for shallow foundation
由: Ahmad, Mazlan
出版: (2007)

Uplift resistance of buried pipelines enhanced by geogrid
由: Faizi, Koohyar, et al.
出版: (2014)

Factors influencing pile friction bearing capacity: Proposing a novel procedure based on gradient boosted tree technique
由: Huat, Chia Yu, et al.
出版: (2021)

Developing GEP tree-based, neuro-swarm, and whale optimization models for evaluation of bearing capacity of concrete-filled steel tube columns
由: Sarir, Payam, et al.
出版: (2021)

Novel hybrid XGBoost model to forecast soil shear strength based on some soil index tests
由: Momeni, Ehsan, et al.
出版: (2023)

The Fire Resistance And Load Bearing Capacity Of Non-Load Bearing Lightweight Sandwiched Rubberized Concrete Wall Panel
由: Yu, Tien Wan
出版: (2021)

Prediction of bearing capacity of shallow sites by seismic method
由: Ramli, Ahmad Bukhari, et al.
出版: (2020)

Tunnel boring machine performance prediction in tropically weathered granite through empirical and computational methods
由: Armaghani, Danial Jahed
出版: (2015)

Developing a hybrid PSO–ANN model for estimating the ultimate bearing capacity of rock-socketed piles
由: Jahed Armaghani, D., et al.
出版: (2017)

Prediction of ultimate load bearing capacity of driven piles
由: Wong, Charng Chen
出版: (2006)

Numerical simulation of bearing capacity for rigid footing on cohesive soil
由: Yusof, Zarina
出版: (2013)

Application of artificial intelligence techniques for the verification of pile capacity at construction site: A review
由: Sai Hin, Lai, et al.
出版: (2023)

Prediction of peak particle velocity caused by blasting through the combinations of boosted-chaid and svm models with various kernels
由: Zeng, Jie, et al.
出版: (2021)

Performance of bearing capacity for eco-friendly raft pile foundation system (ERP System) in soft soil
由: Abdul Rahman, Siti Saimah
出版: (2015)

Prediction of uniaxial compressive strength of rock samples using hybrid particle swarm optimization-based artificial neural networks
由: Momeni, Ehsan, et al.
出版: (2015)

Prediction of the unconfined compressive strength of soft rocks: a PSO-based ANN approach
由: Mohamad, Edy Tonnizam, et al.
出版: (2015)

Bearing capacity of fiber reinforced soil
由: Wan Nuratiqah Ilyani, Wan Yusoff
出版: (2012)

Excellent energy absorption capacity of nanostructured Cu–Zn thin-walled tube
由: Afrasiab, M., et al.
出版: (2014)

Modelling of stability, sliding, overturning and bearing capacity failure of retaining wall using geostructure analysis software
由: Salman Khaled Salman Ba Karman
出版: (2024)

An approach to predict ultimate bearing capacity of surface footings using artificial neural network
由: Noorzaei, Jamaloddin, et al.
出版: (2008)

Simulation of blasting induced ground vibration by using artificial neural network
由: Mohamad, Edy Tonnizam, et al.
出版: (2012)

Bearing capacity performance of soft cohesive soil treated by kenaf limited life geotextile
由: A Rashid, Ahmad Safuan, et al.
出版: (2020)

Enhancing the bearing capacity of rigid footing using limited life kenaf geotextile reinforcement
由: Shirazi, Mohammad Gharehzadeh, et al.
出版: (2020)

Enhancing the bearing capacity of rigid footing using limited life kenaf geotextile reinforcement
由: Shirazi, Mohammad Gharehzadeh, et al.
出版: (2022)

The Deep Mixing Method: Bearing Capacity Studies
由: A. Rashid, A. S., et al.
出版: (2017)