A comparative study of regression, neural network and neuro-fuzzy inference system for determining the compressive strength of brick–mortar masonry by fusing nondestructive testing data
出版年份 2019 全文链接
标题
A comparative study of regression, neural network and neuro-fuzzy inference system for determining the compressive strength of brick–mortar masonry by fusing nondestructive testing data
作者
关键词
Neuro-fuzzy inference system, Brick masonry, Ultrasonic testing, Compressive strength, Non-destructive methods
出版物
ENGINEERING WITH COMPUTERS
Volume -, Issue -, Pages -
出版商
Springer Science and Business Media LLC
发表日期
2019-06-29
DOI
10.1007/s00366-019-00810-4
参考文献
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注意:仅列出部分参考文献,下载原文获取全部文献信息。- Modelling and optimization of ultimate bearing capacity of strip footing near a slope by soft computing methods
- (2018) Hossein Moayedi et al. APPLIED SOFT COMPUTING
- Schmidt hammer rebound hardness tests for the characterization of ancient fired clay bricks
- (2018) Laurent Debailleux International Journal of Architectural Heritage
- Data fusion approaches for structural health monitoring and system identification: Past, present, and future
- (2018) Rih-Teng Wu et al. STRUCTURAL HEALTH MONITORING-AN INTERNATIONAL JOURNAL
- Simulating the peak particle velocity in rock blasting projects using a neuro-fuzzy inference system
- (2018) Wenchao Jiang et al. ENGINEERING WITH COMPUTERS
- Prediction of reinforced concrete strength by ultrasonic velocities
- (2017) Nevbahar Sabbağ et al. JOURNAL OF APPLIED GEOPHYSICS
- Estimation of compressive strength of hollow concrete masonry prisms using artificial neural networks and adaptive neuro-fuzzy inference systems
- (2016) Qiang Zhou et al. CONSTRUCTION AND BUILDING MATERIALS
- A new hybrid ANFIS–PSO model for prediction of peak particle velocity due to bench blasting
- (2016) Ebrahim Ghasemi et al. ENGINEERING WITH COMPUTERS
- Adaptive neuro fuzzy prediction of deflection and cracking behavior of NSM strengthened RC beams
- (2015) Kh Mahfuz ud Darain et al. CONSTRUCTION AND BUILDING MATERIALS
- A Bayesian approach for NDT data fusion: The Saint Torcato church case study
- (2015) Luís F. Ramos et al. ENGINEERING STRUCTURES
- A rapid structural damage detection method using integrated ANFIS and interval modeling technique
- (2014) Futao Zhu et al. APPLIED SOFT COMPUTING
- Prediction of concrete compressive strength by combined non-destructive methods
- (2014) Lucio Nobile MECCANICA
- A practical neuro-fuzzy model for estimating modulus of elasticity of concrete
- (2014) Idris Bedirhanoglu STRUCTURAL ENGINEERING AND MECHANICS
- Determine the Compressive Strength of Calcium Silicate Bricks by Combined Nondestructive Method
- (2014) Jiri Brozovsky TheScientificWorldJOURNAL
- Modeling for pavement roughness using the ANFIS approach
- (2013) Serdal Terzi ADVANCES IN ENGINEERING SOFTWARE
- Prediction of concrete compressive strength: Research on hybrid models genetic based algorithms and ANFIS
- (2013) Zhe Yuan et al. ADVANCES IN ENGINEERING SOFTWARE
- Using artificial neural networks for predicting the elastic modulus of recycled aggregate concrete
- (2013) Z.H. Duan et al. CONSTRUCTION AND BUILDING MATERIALS
- Modeling compressive strength of EPS lightweight concrete using regression, neural network and ANFIS
- (2013) A. Sadrmomtazi et al. CONSTRUCTION AND BUILDING MATERIALS
- Assessing the spatial variability of concrete structures using NDT techniques – Laboratory tests and case study
- (2013) Ngoc Tan Nguyen et al. CONSTRUCTION AND BUILDING MATERIALS
- An experimental study on the within-member variability of in situ concrete strength in RC building structures
- (2013) A. Masi et al. CONSTRUCTION AND BUILDING MATERIALS
- Compressive strength evaluation of structural lightweight concrete by non-destructive ultrasonic pulse velocity method
- (2013) J. Alexandre Bogas et al. ULTRASONICS
- Concrete properties evaluation by statistical fusion of NDT techniques
- (2012) Zoubir Mehdi Sbartaï et al. CONSTRUCTION AND BUILDING MATERIALS
- Prediction of elastic modulus of normal and high strength concrete using ANFIS and optimal nonlinear regression models
- (2012) Behrouz Ahmadi-Nedushan CONSTRUCTION AND BUILDING MATERIALS
- Nondestructive evaluation of concrete strength: An historical review and a new perspective by combining NDT methods
- (2012) D. Breysse CONSTRUCTION AND BUILDING MATERIALS
- Compressive strength of masonry made of clay bricks and cement mortar: Estimation based on Neural Networks and Fuzzy Logic
- (2012) Julio Garzón-Roca et al. ENGINEERING STRUCTURES
- Multiple regression, ANN and ANFIS models for prediction of backbreak in the open pit blasting
- (2012) Mohammad Esmaeili et al. ENGINEERING WITH COMPUTERS
- Comparison of ANFIS and NN models—With a study in critical buckling load estimation
- (2011) Mahmut Bilgehan APPLIED SOFT COMPUTING
- Estimation of elastic constant of rocks using an ANFIS approach
- (2011) Rajesh Singh et al. APPLIED SOFT COMPUTING
- Prediction of the concrete compressive strength by means of core testing using GMDH-type neural network and ANFIS models
- (2011) Rahmat Madandoust et al. COMPUTATIONAL MATERIALS SCIENCE
- Neural networks model and adaptive neuro-fuzzy inference system for predicting the moment capacity of ferrocement members
- (2010) Mohammed A. Mashrei et al. ENGINEERING STRUCTURES
- Predicting the compressive strength of mortars containing metakaolin by artificial neural networks and fuzzy logic
- (2009) Mustafa Sarıdemir ADVANCES IN ENGINEERING SOFTWARE
- Prediction of the compressive strength of no-slump concrete: A comparative study of regression, neural network and ANFIS models
- (2009) Jafar Sobhani et al. CONSTRUCTION AND BUILDING MATERIALS
- Assessment of masonry arch railway bridges using non-destructive in-situ testing methods
- (2009) Zoltán Orbán et al. ENGINEERING STRUCTURES
- Prediction of concrete strength using ultrasonic pulse velocity and artificial neural networks
- (2008) Gregor Trtnik et al. ULTRASONICS
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