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The experimental design of high-strength concrete (HSC) requires deep analysis to get the target strength. In this study, machine learning approaches and artificial intelligence python-based approaches have been utilized to predict the mechanical behaviour of HSC. The data to be used in the modelling consist of several input parameters such as cement, water, fine aggregate, and coarse aggregate in combination with a superplasticizer. Empirical relation with mathematical expression has been proposed using engineering programming. The efficiency of the models is assessed by statistical analysis with the error by using MAE, RRMSE, RSE, and comparisons were made between regression models. Moreover, variable intensity and correlation have shown that deep learning can be used to know the exact amount of materials in civil engineering rather than doing experimental work. The expression tree, as well as normalization of the graph, depicts significant accuracy between target and output values. The results reveal that machine learning proposed adamant accuracy and has elucidated performance in the prediction aspect.

The complication linked with the prediction of the ultimate capacity of concrete-filled steel tubes (CFST) short circular columns reveals a need for conducting an in-depth structural behavioral analyses of this member subjected to axial-load only. The distinguishing feature of gene expression programming (GEP) has been utilized for establishing a prediction model for the axial behavior of long CFST. The proposed equation correlates the ultimate axial capacity of long circular CFST with depth, thickness, yield strength of steel, the compressive strength of concrete and the length of the CFST, without need for conducting any expensive and laborious experiments. A comprehensive CFST short circular column under an axial load was obtained from extensive literature to build the proposed models, and subsequently implemented for verification purposes. This model consists of extensive database literature and is comprised of 227 data samples. External validations were carried out using several statistical criteria recommended by researchers. The developed GEP model demonstrated superior performance to the available design methods for AS5100.6, EC4, AISC, BS, DBJ and AIJ design codes. The proposed design equations can be reliably used for pre-design purposes—or may be used as a fast check for deterministic solutions.

Carbon nanotubes (CNTs) and graphite nanoplatelets (GNPs) belong to the family of graphite nanomaterials (GNMs) and are promising candidates for enhancing properties of cementitious matrix. However, the problem lies with their improper dispersion. In this paper graphite nanoplatelets are used with carbon nanotubes for dispersion facilitation of CNTs in cement mortar. The intended role is to use the GNPs particles for dispersion of CNTs and to investigate the synergistic effect of resulting nano-intruded mortar. Mechanical properties such as flexure and compressive strength have been studied along with volumetric stability, rheology, and workability. Varying dosages of CNTs to GNPs have been formulated and were analyzed. The hybrid use of CNTs-GNPs shows promise. Scanning electron microscopy reveals that hybrid CNTs/GNPs are well-suited for use in cement mortar composite performing a dual function.