Khan Sangeen, Ali Khan Mohsin, Zafar Adeel, Javed Muhammad Faisal, Aslam Fahid, Musarat Muhammad Ali, Vatin Nikolai Ivanovich
Department of Structural Engineering, Military College of Engineering (MCE), National University of Science and Technology (NUST), Islamabad 44000, Pakistan.
Civil Engineering Department, CECOS University of IT and Emerging Science, Peshawar 25000, Pakistan.
Materials (Basel). 2021 Dec 22;15(1):39. doi: 10.3390/ma15010039.
The object of this research is concrete-filled steel tubes (CFST). The article aimed to develop a prediction Multiphysics model for the circular CFST column by using the Artificial Neural Network (ANN), the Adaptive Neuro-Fuzzy Inference System (ANFIS) and the Gene Expression Program (GEP). The database for this study contains 1667 datapoints in which 702 are short CFST columns and 965 are long CFST columns. The input parameters are the geometric dimensions of the structural elements of the column and the mechanical properties of materials. The target parameters are the bearing capacity of columns, which determines their life cycle. A Multiphysics model was developed, and various statistical checks were applied using the three artificial intelligence techniques mentioned above. Parametric and sensitivity analyses were also performed on both short and long GEP models. The overall performance of the GEP model was better than the ANN and ANFIS models, and the prediction values of the GEP model were near actual values. The of the predicted by GEP, ANN and ANFIS for training are 0.0416, 0.1423, and 0.1016, respectively, and for these values are 0.1169, 0.2990 and 0.1542, respectively. Corresponding values are 0.2300, 0.1200, and 0.090 for , and 0.1000, 0.2700, and 0.1500 for . The superiority of the GEP method to the other techniques can be seen from the fact that the GEP technique provides suitable connections based on practical experimental work and does not rely on prior solutions. It is concluded that the GEP model can be used to predict the bearing capacity of circular CFST columns to avoid any laborious and time-consuming experimental work. It is also recommended that further research should be performed on the data to develop a prediction equation using other techniques such as Random Forest Regression and Multi Expression Program.
本研究的对象是钢管混凝土（CFST）。本文旨在利用人工神经网络（ANN）、自适应神经模糊推理系统（ANFIS）和基因表达式编程（GEP）开发一种用于圆形钢管混凝土柱的多物理场预测模型。本研究的数据库包含1667个数据点，其中702个是短钢管混凝土柱，965个是长钢管混凝土柱。输入参数是柱结构元件的几何尺寸和材料的力学性能。目标参数是柱的承载能力，它决定了柱的生命周期。开发了一个多物理场模型，并使用上述三种人工智能技术进行了各种统计检验。还对短和长的GEP模型进行了参数分析和敏感性分析。GEP模型的整体性能优于ANN和ANFIS模型，且GEP模型的预测值接近实际值。GEP、ANN和ANFIS用于训练的预测值的均方根误差分别为0.0416、0.1423和0.1016，用于测试的这些值分别为0.1169、0.2990和0.1542。相应的决定系数值，训练时分别为0.2300、0.1200和0.090，测试时分别为0.1000、0.2700和0.1500。GEP方法相对于其他技术的优越性体现在，GEP技术基于实际实验工作提供了合适的联系，且不依赖于先前的解决方案。得出的结论是，GEP模型可用于预测圆形钢管混凝土柱的承载能力，以避免任何繁琐且耗时的实验工作。还建议应对数据进行进一步研究，以使用随机森林回归和多表达式编程等其他技术开发预测方程。
