Search

Current filters:


Current filters:


Search Results

Results 1-2 of 2 (Search time: 0.0 seconds).
  • <<
  • 1
  • >>
    • Soft computing based closed form equations correlating L and N-type Schmidt hammer rebound numbers of rocks
    Authors: Panagiotis G.Asteris; Anna Mamoua; Mohse Hajihassani; Mahdi Hasanipanah; Mohammadreza Koopialipoor; Tien-Thinh Le; Navid Kardanig; Danial J.Armaghanih;  Advisor: -;  Co-Author: - (2021)

    This paper reports the results of soft computing-based models correlating L and N-type Schmidt hammer rebound numbers of rock. A data-independent database was compiled from available measurements reported in the literature, which was used to train and develop back propagating neural networks, genetic programming and least square method models for the prediction of L-type Schmidt hammer rebound numbers. The results show that the highest predictive accuracy was obtained for the neural network model, which predicts the L type Schmidt hammer rebound number, with less than ±20% deviation from the experimental data for 97.27% of the samples. The optimum neural network is presented as a closed form equation and is also incorporated into an Excel-based graphical user interface, which direct...
    • Prediction of axial load capacity of rectangular concrete-filled steel tube columns using machine learning techniques
    Authors: Tien-Thinh Le; Panagiotis G. Asteris; Minas E. Lemonis;  Advisor: -;  Co-Author: - (2021)

    This work aims to develop a novel and practical equation for predicting the axial load of rectangular concrete-filled steel tubular (CFST) columns based on soft computing techniques. More precisely, a dataset containing 880 experimental tests was first collected from the available literature for the development of an artificial neural network (ANN) model. An optimization strategy was conducted to obtain a final set of ANN’s architecture as well as its weight and bias parameters. The performance of the developed ANN was then compared to current codes (AS, EN, AIJ, ACI, AISC, LRFD, and DBJ) and existing empirical equations. The accuracy of the present model was found superior to the results obtained by others when predicting the axial load of rectangular CFST columns. For practical ap...