Axial load-carrying capacity of concrete-filled steel tube columns: a comparative analysis of various modeling techniques

被引:11
作者
Abdellatif, Selmi [1 ,2 ]
Raza, Ali [3 ]
Alsulamy, Saleh [4 ]
Khadimallah, Mohamed Amine [1 ,5 ]
机构
[1] Prince Sattam Bin Abdulaziz Univ, Coll Engn, Dept Civil Engn, Alkharj, Saudi Arabia
[2] Ecole Natl Ingn Tunis ENIT, Lab, Civil Engn, Tunis, Tunisia
[3] Univ Engn & Technol Taxila, Dept Civil Engn, Taxila, Pakistan
[4] King Khalid Univ, Coll Engn, Dept Architecture & Planning, Abha, Saudi Arabia
[5] Univ Carthage, Polytech Sch Tunisia, Lab Syst & Appl Mech, Tunis, Tunisia
关键词
Concrete-filled steel tube (CFST) circular compression elements; finite element numerical model; theoretical model; artificial neural network (ANN) model; axial load-carrying (LCC) capacity; FINITE-ELEMENT-ANALYSIS; ARTIFICIAL NEURAL-NETWORKS; SHEAR DESIGN PROCEDURE; STRESS-STRAIN MODEL; STUB COLUMNS; NONLINEAR-ANALYSIS; STRUCTURAL PERFORMANCE; RELIABILITY-ANALYSIS; NUMERICAL-ANALYSIS; PART II;
D O I
10.1080/15376494.2023.2188325
中图分类号
T [工业技术];
学科分类号
08 ;
摘要
Limited research is available in the literature to investigate the presentation of normal strength concrete-filled steel tube (CFST) circular compression elements under compression loading by considering various material and geometric coefficients. Thus, the present study investigates the mechanical behavior of CFST compression elements employing nonlinear finite element analysis (NLFEA), empirical/theoretical modeling, and a newly developed Artificial Neural Network (ANN) model, based on a large experimental database of 1223 samples. The NLFEA modeling is performed in ABAQUS (6.14) with an improved concrete damaged plasticity model for laterally restrained concrete. Various geometric and material properties are considered, for parametric NLFEA estimates preliminary validated toward the available experimental database. A new ANN model for the load-carrying capacity of CFST circular elements was also offered by employing the experimental database. The comparison between the calculations of the NLFEA model, empirical model, and ANN model displayed a close agreement with the database results.
引用
收藏
页码:3980 / 4002
页数:23
相关论文
共 97 条
[1]   Reliability analysis of strength models for short-concrete columns under concentric loading with FRP rebars through Artificial Neural Network [J].
Ahmad, Afaq ;
Elchalakani, Mohamed ;
Elmesalami, Nouran ;
El Refai, Ahmed ;
Abed, Farid .
JOURNAL OF BUILDING ENGINEERING, 2021, 42
[2]   Reliability analysis of proposed capacity equation for predicting the behavior of steel-tube concrete columns confined with CFRP sheets [J].
Ahmad, Afaq ;
Khan, Qaiser uz Zaman ;
Raza, Ali .
COMPUTERS AND CONCRETE, 2020, 25 (05) :383-400
[3]   Reliability analysis of strength models for CFRP-confined concrete cylinders [J].
Ahmad, Afaq ;
Khan, Qaiser uz Zaman ;
Raza, Ali .
COMPOSITE STRUCTURES, 2020, 244
[4]   Mapping and holistic design of natural hydraulic lime mortars [J].
Apostolopoulou, Maria ;
Asteris, Panagiotis G. ;
Armaghani, Danial J. ;
Douvika, Maria G. ;
Lourenco, Paulo B. ;
Cavaleri, Liborio ;
Bakolas, Asterios ;
Moropoulou, Antonia .
CEMENT AND CONCRETE RESEARCH, 2020, 136
[5]   Axial compressive behavior of damaged steel and GFRP bars reinforced concrete columns retrofitted with CFRP laminates [J].
Aslam, Hafiz Muhammad Usman ;
Khan, Qaiser Uz Zaman ;
Sami, Abdul ;
Raza, Ali .
COMPOSITE STRUCTURES, 2021, 258
[6]   Prediction of self-compacting concrete strength using artificial neural networks [J].
Asteris, P. G. ;
Kolovos, K. G. ;
Douvika, M. G. ;
Roinos, K. .
EUROPEAN JOURNAL OF ENVIRONMENTAL AND CIVIL ENGINEERING, 2016, 20 :s102-s122
[7]   Artificial bee colony-based neural network for the prediction of the fundamental period of infilled frame structures [J].
Asteris, Panagiotis G. ;
Nikoo, Mehdi .
NEURAL COMPUTING & APPLICATIONS, 2019, 31 (09) :4837-4847
[8]   Self-compacting concrete strength prediction using surrogate models [J].
Asteris, Panagiotis G. ;
Kolovos, Konstantinos G. .
NEURAL COMPUTING & APPLICATIONS, 2019, 31 (Suppl 1) :409-424
[9]   Feed-Forward Neural Network Prediction of the Mechanical Properties of Sandcrete Materials [J].
Asteris, Panagiotis G. ;
Roussis, Panayiotis C. ;
Douvika, Maria G. .
SENSORS, 2017, 17 (06)
[10]   Experiments and predictive modeling of optimized fiber-reinforced concrete columns having FRP rebars and hoops [J].
Baili, Jamel ;
Raza, Ali ;
Azab, Marc ;
Ali, Khawar ;
El Ouni, Mohamed Hechmi ;
Haider, Hammad ;
Farooq, Muhammad Ahmad .
MECHANICS OF ADVANCED MATERIALS AND STRUCTURES, 2023, 30 (23) :4913-4932