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Abstract Bentonite plastic concrete (BPC) is extensively used in the construction of water-tight structures like cut-off walls in dams, etc., because it offers high plasticity, improved workability, and homogeneity. Also, bentonite is added to concrete mixes for the adsorption of toxic metals. The modified design of BPC, as compared to normal concrete, requires a reliable tool to predict its strength. Thus, this study presents a novel attempt at the application of two innovative evolutionary techniques known as multi-expression programming (MEP) and gene expression programming (GEP) and a boosting-based algorithm known as AdaBoost to predict the 28-day compressive strength ( ) of BPC based on its mixture composition. The MEP and GEP algorithms expressed their outputs in the form of an empirical equation, while AdaBoost failed to do so. The algorithms were trained using a dataset of 246 points gathered from published literature having six important input factors for predicting. The developed models were subject to error evaluation, and the results revealed that all algorithms satisfied the suggested criteria and had a correlation coefficient (R) greater than 0.9 for both the training and testing phases. However, AdaBoost surpassed both MEP and GEP in terms of accuracy and demonstrated a lower testing RMSE of 1.66 compared to 2.02 for MEP and 2.38 for GEP. Similarly, the objective function value for AdaBoost was 0.10 compared to 0.176 for GEP and 0.16 for MEP, which indicated the overall good performance of AdaBoost compared to the two evolutionary techniques. Also, Shapley additive analysis was done on the AdaBoost model to gain further insights into the prediction process, which revealed that cement, coarse aggregate, and fine aggregate are the most important factors in predicting the strength of BPC. Moreover, an interactive graphical user interface (GUI) has been developed to be practically utilized in the civil engineering industry for prediction of BPC strength.

Abstract The utilization of Self-compacting Concrete (SCC) has escalated worldwide due to its superior properties in comparison to normal concrete such as compaction without vibration, increased flowability and segregation resistance. Various other desirable properties like ductile behaviour, increased strain capacity and tensile strength etc. can be imparted to SCC by incorporation of fibres. Thus, this study presents a novel approach to predict 28-day compressive strength (C–S) of FR-SCC using Gene Expression Programming (GEP) and Multi Expression Programming (MEP) for fostering its widespread use in the industry. For this purpose, a dataset had been compiled from internationally published literature having six input parameters including water-to-cement ratio, silica fume, fine aggregate, coarse aggregate, fibre, and superplasticizer. The predictive abilities of developed algorithms were assessed using error metrices like mean absolute error (MAE), a20-index, and objective function (OF) etc. The comparison of MEP and GEP models indicated that GEP gave a simple equation having lesser errors than MEP. The OF value of GEP was 0.029 compared to 0.031 of MEP. Thus, sensitivity analysis was performed on GEP model. The models were also checked using some external validation checks which also verified that MEP and GEP equations can be used to forecast the strength of FR-SCC for practical uses.

Construction industry is indirectly the largest source of CO2 emissions in the atmosphere, due to the use of cement in concrete. These emissions can be reduced by using industrial waste materials in place of cement. Self-Compacting Concrete (SCC) is a promising material to enhance the use of industrial wastes in concrete. However, there are very few methods available for accurate prediction of its strength, therefore, reliable models for estimating 28-day Compressive Strength (C–S) of SCC are developed in current study by using three Machine Learning (ML) algorithms including Multi Expression Programming (MEP), Extreme Gradient Boosting (XGB), and Random Forest (RF). The ML models were meticulously developed using a dataset of 231 points collected from internationally published literature considering seven most influential parameters including cement content, quantities of fly ash and silica fume, water content, coarse aggregate, fine aggregate, and superplasticizer dosage to predict C–S. The developed models were evaluated using different statistical errors including Root Mean Square Error (RMSE), Mean Absolute Error (MAE), coefficient of determination (R2) etc. The results showed that the XGB model outperformed the MEP and RF model in terms of accuracy with a correlation R2 = 0.998 compared to 0.923 for MEP and 0.986 for RF. Similar trend was observed for other error metrices. Thus, XGB is the most accurate model for estimating C–S of SCC. However, it is pertinent to mention here that it does not give its output in the form of an empirical equation like MEP model. The construction of these empirical models will help to efficiently estimate C–S of SCC for practical purposes.

The increasing concern for the safety and sustainability of structures is calling for the development of smart self-healing materials and preventive repair methods. This research is carried out to investigate the extent of self-healing in normal-strength concrete by using Sporosarcina aquimarina – NCCP-2716 immobilized in expanded perlite (EP) as the carrier. The efficacy of crack-healing was also tested using two alternative self-healing techniques, i.e. expanded perlite (EP) concrete and direct introduction of bacteria in concrete. A bacterial solution was embedded in EP and calcium lactate pentahydrate was added as the nutrient. Experiments revealed that specimens containing EP-immobilized bacteria had the most effective crack-healing. After 28 days of healing, the values of completely healed crack widths were up to 0.78 mm, which is higher than the 0.5 mm value for specimens with the direct addition of bacteria. The specimen showed a significant self-healing phenomenon caused by substantial calcite precipitation by bacteria. The induced cracks were observed to be repaired autonomously by the calcite produced by the bacteria without any adverse effect on strength. The results of this research could provide a scientific foundation for the use of expanded perlite as a novel microbe carrier and Sporosarcina aquimarina as a potential microbe in bacteria-based self-healing concrete.

Abstract Today, it’s getting harder to find natural resources for concrete production. Utilization of the waste materials not just helps in getting them used in concrete, cement, and other construction materials, but also has various secondary advantages, for example, saving in energy, decrease in landfill cost, and protecting climate from pollution. Considering this in the development of modern structural design, utilizing waste materials instead of natural aggregate is a good option to make concrete that is sustainable and eco-friendly. The present research aims to find the impact of adding glass fiber into sustainable concrete made with silica fume, as a partial replacement of cement, and coconut shell added with different ratios as a replacement of coarse aggregate, on concrete mechanical and durability aspects. Various blends were made, with coconut shell as a substitution of coarse aggregates with different ratios. Portland cement was substituted with silica fume at 5%, 10%, 15%, and 20% by cement weight in all concrete blends. The volume ratios of glass fibers utilized in this study were 0.5%, 1.0%, 1.5% and 2.0%. Adding glass fibers increases concrete density to some extent and then marginally reduces the density of coconut shell concrete. When the percentage of glass fibers increases, the compressive, flexural and split tensile strength of coconut shell concrete also increases. From the lab results and SEM images of the present research display that glass fibers might be utilized in coconut shell concrete to enhance its mechanical and durability attributes, to accomplish sustainable concrete with acceptable strength with ease.

Carbon footprint reduction, recompense depletion of natural resources, as well as waste recycling are nowadays focused research directions to achieve sustainability without compromising the concrete strength parameters. Therefore, the purpose of the present study is to utilize different dosages of marble waste aggregates (MWA) and stone dust (SD) as a replacement for coarse and fine aggregate, respectively. The MWA with 10 to 30% coarse aggregate replacement and SD with 40 to 50% fine aggregate replacement were used to evaluate the physical properties (workability and absorption), durability (acid attack resistance), and strength properties (compressive, flexural, and tensile strength) of concrete. Moreover, statistical modeling was also performed using response surface methodology (RSM) to design the experiment, optimize the MWA and SD dosages, and finally validate the experimental results. Increasing MWA substitutions resulted in higher workability, lower absorption, and lower resistance to acid attack as compared with controlled concrete. However, reduced compressive strength, flexural strength, and tensile strength at 7-day and 28-day cured specimens were observed as compared to the controlled specimen. On the other hand, increasing SD content causes a reduction in workability, higher absorption, and lower resistance to acid attack compared with controlled concrete. Similarly, 7-day and 28-day compressive strength, flexural strength, and tensile strength of SD-substituted concrete showed improvement up to 50% replacement and a slight reduction at 60% replacement. However, the strength of SD substituted concrete is higher than controlled concrete. Quadratic models were suggested based on a higher coefficient of determination (R2) for all responses. Quadratic RSM models yielded R2 equaling 0.90 and 0.94 for compressive strength at 7 days and 28 days, respectively. Similarly, 0.94 and 0.96 for 7-day and 28-day flexural strength and 0.89 for tensile strength. The optimization performed through RSM indicates that 15% MWA and 50% SD yielded higher strength compared to all other mixtures. The predicted optimized data was validated experimentally with an error of less than 5%.

The production of rice is significant worldwide; the husk produced is generally used as a combustible material for the preparation of paddies, delivering energy through direct combustion as well as by gasifying. Annually, 7.4 million tons of Rice Husk Ash (RHA) is produced and poses an incredible danger to the environment, harming the land and the encompassing zone where it is unloaded. In the transformation of rice husk to ash, the ignition cycle eliminates the natural products, leaving silica-rich remains. These silica-rich remains have proven to have potential to be utilized in concrete as a limited substitution of cement to enhance the concrete compressive strength. Steel fibers’ incorporation increases the concrete tensile strength, balances out concrete samples, and changes their brittle behavior to a more ductile response. In the current study, the influence of various doses of Rice Husk Ash (RHA) used in concrete in the presence and absence of steel fibers and concrete performance has been examined. A total of nine mixes have been designed: one was a control, four were without steel fibers containing only RHA, and the last four mixed RHA with steel fibers from 0.5 to 2%. Tests with 5, 10, 15, and 20% percentages of RHA replacing the concrete have been targeted. Results have been compared with the reference samples and the reasonability of adding Rice Husk Ash to concrete has been studied. From the results, it was noted that about 10% of cement might be replaced with Rice Husk Ash mixed in with steel fibers with almost equal compressive strength. Replacing more than 15% of cement with RHA will produce concrete with a low performance in terms of strength and durability.

China–Pakistan Economic Corridor (CPEC), a multibillion-dollar partnered project is considered as a game-changer, as it combines the strategic interests of China and Pakistan that cannot be compromised. The mega-construction project has recently experienced xenophobic incidents involving Chinese and Pakistani nationals, creating significant harm towards the CPEC progress and success, especially under enormous geopolitical and economic threats. This sketches our motivation for conducting this research that focused on qualitative design based expert interviews of 8 individuals (i.e., both Chinese and Pakistani nationals) directly associated with CPEC. Drawing on a review of mainstream research, the article constructs a framework to explore the demotions of xenophobic behaviors at workplace and to explore the human resource diversity management (HRDM) practices (including intercultural training and development, flexible compensation, international staffing, work-design, and work–life balance) in coping with xenophobia and its sub-dimensions (including discrimination, prejudice, and stereotyping). Experts based interview findings confirm the presence of xenophobia in the CPEC. We propose a framework of various HRDM practices in managing the xenophobia at workplace, however, it is still in its infancy stage. Hence, the present study introduces a new operating model for future researchers, academicians, and management practitioners to cope with xenophobia based on the practices and insights that were explored during this research.

‘Concrete needs some tensile reinforcement to enhance tensile strength and avoid unacceptable brittle nature. This study is supported out to estimate the characteristics of coconut fibers reinforced concrete with the incorporation of marble slurry. Marble waste used as binding materials in percentage of 0 to 30% in 5.0% increment by weight of cement to enhance compressive capacity while coconut fiber was used in proportion of 0.5%, 1.0%, 1.5%, 2.0%, 2.5% and 3.0% by weight of cement to enhance tensile capacity of concrete. Mechanical performance was evaluated through compressive, flexure, and split tensile strength. To assess durability characteristics of all mix, different parameters such as acid attack resistance, carbonation resistance and water absorption are examined. Experiment findings indicate that marble slurry and coconut fiber decrease the workability of fresh concrete. Furthermore, Concrete specimens’ tests indicate that marble slurry up to 20% and coconut fiber addition 2.0% tend to improve the mechanical performance of hardened Concrete. It also indicates that durability aspects such as water absorption, carbonation resistance and acid attack resistance significantly improved with the substitution of marble waste and coconut fibers. Response surface methodology (statistically models) is used to optimize combine dosage of marble slurry and coconut fiber and verified through experimental tests’.