No abstract is provided for this article.
No abstract is provided for this article.
A new design equation is proposed for the prediction of shear strength of reinforced concrete (RC) beams without stirrups using an innovative linear genetic programming methodology. The shear strength was formulated in terms of several effective parameters such as shear span to depth ratio, concrete cylinder strength at date of testing, amount of longitudinal reinforcement, lever arm, and maximum specified size of coarse aggregate. A comprehensive database containing 1938 experimental test results for the RC beams was gathered from the literature to develop the model. The performance and validity of the model were further tested using several criteria. An efficient strategy was considered to guarantee the generalization of the proposed design equation. For more verification, sensitivity and parametric analysis were conducted. The results indicate that the derived model is an effective tool for the estimation of the shear capacity of members without stirrups (R =0.921). The prediction performance of the proposed model was found to be better than that of several existing buildings codes.
This study presents a robust evolutionary computational technique, called multi-expression programming (MEP), to derive a highly nonlinear model for the prediction of compression index of fine-grained soils. The proposed model relates the soil compression index to its liquid limit, plastic limit and void ratio. The experimental database used for developing the models was established upon 108 consolidation tests conducted on different soils sampled from different construction sites in Iran. The generalization capability of the model was verified via several statistical criteria. The parametric and sensitivity analyses were performed and discussed. The results indicate that the MEP approach accurately characterizes the soil compression index leading to a very good prediction performance. The correlation coefficients between the experimental and predicted soil compression index values are equal to 0.935 and 0.901 for the calibration and testing data sets, respectively. The developed model has a significantly better performance than the existing empirical equations for the soil compression index.
