A Discussion on “Genetic programming for retrieving missing information in wave records along the west coast of India” [Applied Ocean Research 2007; 29 (3): 99–111]

Evapotranspiration was determined to be hydrological effect which induces suction. Evapotranspiration induced suction governs the slope stability, ground water recharge and runoff. This study focuses on suction induced in root zone of mix grass used in green infrastructure. Most of the previous studies focused on evapotranspiration induced suction less than 100 kPa measured using tensiometers. However, for investigating the drought condition, it is essential to obtain suction data greater than 100 kPa. It was noted that the response of plant parameters such as stomatal conductance and surface area were not considered holistically evapotranspiration induced suction. The main objective of this study is to investigate relationship among evapotranspiration induced suction, stomatal conductance and surface area for a mix grass species used in green infrastructure subjected to drought stress (corresponds to suction > 100 kPa). Stomatal conductance was observed to increase up to suction (6 ± 1 kPa) corresponding to volumetric field capacity. Thereafter, large decrease (more than 90%) in stomatal conductance was found with an increase in suction. Stomatal conductance was found to be 0 at 2431 kPa (i.e., wilting point). Change in normalized surface areas of mix grass was found to be relatively low (within ±15%) with an increase in suction up to 138 kPa. However, beyond 138 kPa, it varied drastically (by 31–68%) with an increase in suction from 138 kPa to 3642 kPa. Two new relationships i.e., Stomatal conductance characteristic curve (SCCC) and surface area characteristic curve (SACC) were proposed. Therefore, the relationships developed in current study help to analyze the performance of green infrastructure accurately.

In this study, a new design equation is derived to predict the shear strength of slender reinforced concrete (RC) beams without stirrups using gene expression programming (GEP). The predictor variables included in the analysis are web width, effective depth, concrete compressive strength, amount of longitudinal reinforcement, and shear span to depth ratio. A set of published database containing 1942 experimental test results is used to develop the model. An extra set of test results which is not involved in the modeling process is employed to verify the applicability of the proposed model. Sensitivity and parametric analyses are carried out to determine the contributions of the affecting parameters. The proposed model is effectively capable of estimating the ultimate shear capacity of members without shear steel. The results obtained by GEP are found to be more accurate than those obtained using several building codes. The GEP-based formula is fairly simple and useful for pre-design applications.

Flow discharge determination in rivers is one of the key elements in mathematical modelling in the design of river engineering projects. Because of the inundation of floodplains and sudden changes in river geometry, flow resistance equations are not applicable for compound channels. Therefore, many approaches have been developed for modification of flow discharge computations. Most of these methods have satisfactory results only in laboratory flumes. Due to the ability to model complex phenomena, the artificial intelligence methods have recently been employed for wide applications in various fields of water engineering. Linear genetic programming (LGP), a branch of artificial intelligence methods, is able to optimise the model structure and its components and to derive an explicit equation based on the variables of the phenomena. In this paper, a precise dimensionless equation has been derived for prediction of flood discharge using LGP. The proposed model was developed using published data compiled for stage–discharge data sets for 394 laboratories, and field of 30 compound channels. The results indicate that the LGP model has a better performance than the existing models.

In this study, gene expression programming (GEP) is utilized to derive a new model for the prediction of compressive strength of high performance concrete (HPC) mixes. The model is developed using a comprehensive database obtained from the literature. The validity of the proposed model is verified by applying it to estimate the compressive strength of a portion of test results that are not included in the analysis. Linear and nonlinear least squares regression analyses are performed to benchmark the GEP model. Contributions of the parameters affecting the compressive strength are evaluated through a sensitivity analysis. GEP is found to be an effective method for evaluating the compressive strength of HPC mixes. The prediction performance of the optimal GEP model is better than the regression models.

This study presents two branches of soft computing techniques, namely multi expression programming (MEP) and multilayer perceptron (MLP) of artificial neural networks for the evaluation of rutting potential of dense asphalt-aggregate mixtures. Constitutive MEP and MLP-based relationships were obtained correlating the flow number of Marshall specimens to the coarse and fine aggregate contents, percentage of bitumen, percentage of voids in mineral aggregate, Marshall stability, and Marshall flow. Different correlations were developed using different combinations of the influencing parameters. The comprehensive experimental database used for the development of the correlations was established upon a series of uniaxial dynamic creep tests conducted in this study. Relative importance values of various predictor variables of the models were calculated to determine the significance of each of the variables to the flow number. A multiple least squares regression (MLSR) analysis was performed to benchmark the MEP and MLP models. For more verification, a subsequent parametric study was also carried out and the trends of the results were confirmed with the experimental study results and those of previous studies. The observed agreement between the predicted and measured flow number values validates the efficiency of the proposed correlations for the assessment of the rutting potential of asphalt mixtures. The MEP-based straightforward formulas are much more practical for the engineering applications compared with the complicated equations provided by MLP.