Infrastructure design, construction and development experts are making frantic efforts to overcome the overbearing effects of greenhouse gas emissions resulting from the continued dependence on the utilization of conventional cement as a construction material on our planet. The amount of CO₂ emitted during cement production, transportation to construction sites, and handling during construction activities to produce concrete is alarming. The present research work is focused on proposing intelligent models for fly ash (FA)-based concrete comprising cement, fine and coarse aggregates (FAg and CAg), FA, and water as mix constituents based on environmental impact (P) considerations in an attempt to foster healthier and greener concrete production and aid the environment. FA as a construction material is discharged as a waste material from power plants in large amounts across the world. Its utilization as a supplementary cement ensures a sustainable waste management mechanism and is beneficial for the environment too; hence, this research work is a multi-objective exercise. Intelligent models are proposed for multiple concrete mixes utilizing FA as a replacement for cement to predict 28-day concrete compressive strength and life cycle assessment (LCA) for cement with FA. The data collected show that the concrete mixes with a higher amount of FA had a lesser impact on the environment, while the environmental impact was higher for those mixes with a higher amount of cement. The models which utilized the learning abilities of ANN (-BP, -GRG, and -GA), GP and EPR showed great speed and robustness with R² performance indices (SSE) of 0.986 (5.1), 0.983 (5.8), 0.974 (7.0), 0.78 (19.1), and 0.957 (10.1) for Fc, respectively, and 0.994 (2.2), 0.999 (0.8), 0.999 (1.0), 0.999 (0.8), and 1.00 (0.4) for P, respectively. Overall, this shows that ANN-BP outclassed the rest in performance in predicting Fc, while EPR outclassed the others in predicting P. Relative importance analyses conducted on the constituent materials showed that FA had relatively good importance in the concrete mixes. However, closed-form model equations are proposed to optimize the amount of FA and cement that will provide the needed strength levels without jeopardizing the health of the environment.

As the most commonly used construction material, concrete produces extreme amounts of carbon dioxide (CO₂) yearly. For this resulting environmental impact on our planet, supplementary materials are being studied daily for their potentials to replace concrete constituents responsible for the environmental damage caused by the use of concrete. Therefore, the production of bio-concrete has been studied by utilizing the environmental and structural benefit of the bacteria, Bacillus subtilis, in concrete. This bio-concrete is known as self-healing concrete (SHC) due to its potential to trigger biochemical processes which heal cracks, reduce porosity, and improve strength of concrete throughout its life span. In this research paper, the life cycle assessment (LCA) based on the environmental impact indices of global warming potential, terrestrial acidification, terrestrial eco-toxicity, freshwater eco-toxicity, marine eco-toxicity, human carcinogenic toxicity, and human non-carcinogenic toxicity of SHC produced with Bacillus subtilis has been evaluated. Secondly, predictive models for the mechanical properties of the concrete, which included compressive (Fc), splitting tensile (Ft), and flexural (Ff) strengths and slump (S), have been studied by using artificial intelligence techniques. The results of the LCA conducted on the multiple data of Bacillus subtilis-based SHC mixes show that the global warming potential of SHC-350 mix (350 kg cement mix) is 18% less pollutant than self-healing geopolymer concrete referred to in the literature study. The more impactful mix in the present study has about 6% more CO₂ emissions. In the terrestrial acidification index, the present study shows a 69–75% reduction compared to the literature. The results of the predictive models show that ANN outclassed GEP and EPR in the prediction of Fc, Ft, Ff, and S with minimal error and overall performance.

Steel plates are used in the construction of various structures in civil engineering, aerospace, and shipbuilding. One of the main failure modes of plate members is buckling. Openings are provided in plates to accommodate various additional facilities and make the structure more serviceable. The present study examined the critical buckling load of rectangular steel plates with centrally placed circular openings and different support conditions. Various datasets were compiled from the literature and integrated into artificial intelligence techniques like Gene Expression Programming (GEP), Artificial Neural Network (ANN) and Evolutionary Polynomial Regression (EPR) to predict the critical buckling loads of the steel plates. The comparison of the developed models was conducted by determining various statistical parameters. The assessment revealed that the ANN model, with an R² of 98.6% with an average error of 10.4%, outperformed the other two models showing its superiority in terms of better precision and less error. Thus, artificial intelligence techniques can be adopted as a successful technique for the prediction of the buckling load, and it is a sustainable method that can be used to solve practical problems encountered in the field of civil engineering, especially in steel structures.

In this study, the employment of the gene expression programming (GEP) technique in forecasting models on sustainable construction materials including mineral admixtures and civil engineering quantities (e.g., compressive strength), was investigated. Compared to the artificial neural networks (ANN) based formulations, which are often too complicated to be used, GEP-based derived models provide estimation equations that are reasonably simple and may be used for practical design purposes and even for hand calculations. Many popular models, such as best-fitted curves based on regression analyses, multi-linear regression (MLR), multinomial logistic regression (MNLR), and multinomial variate regression (MNVR), can also be used for construction materials properties modeling. However, due to the nonlinearity and complexity of the target properties, the models established using linear regression analyses may not reveal the precise behavior. Additionally, regression models lack generality, and this comes from the fact that some functions are defined for regression in classical regression techniques; while in the GEP approach, there is no predefined function to be considered, and it reproduces or omits various combinations of parameters to provide the formulation that fits the experimental outcomes. If the input parameters can be evaluated through simple laboratory or rapid measurements, and also a comprehensive experimental database is made available, the models can be constructed with optimal flexibility. Flexibility in choosing the complexity and fitness functions, such as RMSE, MAE, and MSE, might lead to better performance of the approach and well-capturing the governing pattern behind the material’s characteristics. There may be minor inaccuracies with this technique; however, the explicit mathematical expressions, which can be easily implemented in the design and analysis process, may cover the minor inaccuracies compared to ANN, support vector machine (SVM), and other intelligent approaches. Based on the presented study, sometimes it would be better to provide more than one GEP model and consider different combinations of input contributing variables to afford the possible initial feed for a more settled and comprehensive model. Mostly, GEP’s strengths as a superior machine learning technique in modeling the behavior of construction materials including mineral admixtures, leading to innovative solutions in civil engineering, have been presented.

This study focuses on identifying damage in reinforced concrete (RC) beams using time-domain modal testing and wavelet analysis. A numerical model of an RC beam was used to generate various damage scenarios with different severities and locations. Acceleration time histories were recorded for both damaged and undamaged structures. Two damage indices, DI_MW and DI_SW, derived from the wavelet analysis, were employed to determine the location and severity of the damage. The results showed that different wavelet families and specific mother wavelets had varying effectiveness in detecting damage. The Daubechies wavelet family (db2, db6, and db9) detected damage at the center and sides of the RC beams due to good time and frequency localization. The Biorthogonal wavelet family (bior2.8 and bior3.1) provided improved time–frequency resolution. The Symlets wavelet family (sym2 and sym7) offered a balanced trade-off between time and frequency localization. The Shannon wavelet family (shan1-0.5 and shan1-0.1) exhibited good time localization, while the Frequency B-Spline wavelet family (fbsp2-1-0.1) excelled in frequency localization. Certain combinations of mother wavelets, such as shan1-0.5 with the DI_SW index, were highly effective in detecting damage. The DI_SW index outperformed DI_MW across different numerical models. Selecting appropriate wavelet analysis techniques, particularly utilizing shan1-0.5 in the DI_SW, proved effective for detecting damage in RC beams.