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Diverse strategies were designed and evaluated for enhancement of concrete resistance to microbial-induced corrosion through refinement of concrete chemistry, pore structure, transport attributes, and amenability to microbial growth. Laboratory and insitu field experiments were implemented in order to screen different strategies. The study considered strategies involving; the use of hydrophobic additives for rendering capillary pores water-repelling; blend of pozzolans to refine the chemistry and pore system characteristics of concrete; use of polymer emulsions for refinement of concrete porosity, affinity for moisture and amenability to microbial growth; use of anti-microbial constituent for reducing the amenability of concrete to microbial growth; and incorporation of polyvinyl alcohol fibers for enhancing mechanical performance of concrete mixtures, were investigated.Test results indicate that two admixture formulations comprising of hydrophobic additive, and blend of pozzolan offer the best promise to achieve substantially enhanced resistance to microbial-induced corrosion and acid resistance in the aggressive sewer environment.

This study aims to investigate the compressive strength (CS) of foamcrete (FC), with a specific emphasis on its 7-day and 28-day performance. The multi-layer perceptron (MLP) and random forest (RF) machine learning techniques have been selected for forecasting the required outputs. Eight input parameters: density, cement, sand, sand-to-cement ratio, water-to-cement ratio, sand size, foam, and agent, were used to predict the CS of FC. Further, SHapley Additive exPlanations (SHAP) analysis was performed for feature interaction. The results indicate that the RF model had higher accuracy in forecasting the CS of FC. The assessment metrics, including R2 values of 0.952 for 7 days and 0.958 for 28 days strength, and the mean absolute errors (MAE) of 1.47 MPa and 1.71 MPa, respectively, verified the results. A user-friendly graphical user interface (GUI) was developed to predict the CS of foam concrete using a machine learning model based on key input parameters. The impact of the study's findings is of great importance in reducing project costs by avoiding costly laboratory work and speeding up achieving desired outcomes.

AbstractThis paper investigates the effects of powder waste glass (PWG) at 10%, 15%, and 20 wt.% replacement of cement on the flow characteristic, setting time, compressive strength, water sorption, rate of the heat of hydration, cumulative heat of hydration; alkali-silica reaction (ASR), and microstructure characteristics of the resulting paste and mortar mixtures. A total of eight cementitious mixtures including two control mixtures have been investigated in this experimental program to evaluate the effect of PWG on the fresh and hardened state characteristics of paste and mortar mixtures. Test results show that the incorporation of PWG in the cementitious mixtures results in significant enhancement of their microstructure, increase in compressive strength, improvement in moisture barrier characteristics, and considerable reduction in the rate of the heat of hydration and cumulative heat of hydration. Up to 20 wt.% replacement of cement with fine PWG is seen to be innocuous from the standpoint of ASR. Furthermore, the use of PWG in paste results in delaying the initial and final setting times and increases the flow characteristic of the mortar mixture.

Modern infrastructure requirements necessitate structural components with improved durability and strength properties. The incorporation of nanomaterials (NMs) into concrete emerges as a viable strategy to enhance both the durability and strength of the concrete. Nevertheless, the complexities inherent in these nanoscale cementitious composites are notably intricate. Traditional regression models face constraints in comprehensively capturing these intricate compositions. Thus, posing challenges in delivering precise and dependable estimations. Therefore, the current study utilized three machine learning (ML) methods, including artificial neural network (ANN), gene expression programming (GEP), and adaptive neuro-fuzzy inference system (ANFIS), in conjunction with experimental investigation to study the effect of the integration of graphene nanoplatelets (GNPs) on the electrical resistivity (ER) and compressive strength (CS) of concrete containing GNPs. Concrete containing GNPs demonstrated an improved fractional change in resistivity (FCR) and strength. The experimental measures depict that strength enhancement was notable at GNP concentrations of 0.05% and 0.1%, showcasing increases of 13.23% and 16.58%, respectively. Simultaneously, the highest observed FCR change reached −12.19% and −13%, respectively. The prediction efficacy of the three models proved to be outstanding in forecasting the characteristics of concrete containing GNPs. For CS, the GEP, ANN, and ANFIS models demonstrated impressive correlation coefficient (R) values of 0.974, 0.963, and 0.954, respectively. For electrical resistivity, the GEP, ANN, and ANFIS models exhibited high R-values of 0.999, 0.995, and 0.987, respectively. The comparative analysis of the models revealed that the GEP model delivered precise predictions for both ER and CS. The mean absolute error (MAE) of the GEP-CS model demonstrated a 14.51% reduction compared to the ANN-CS model and a substantial 48.15% improvement over the ANFIS-CS model. Similarly, the ANN-CS model displayed an MAE that was 38.14% lower compared to the ANFIS-CS model. Moreover, the MAE of the GEP-ER model demonstrated a 56.80% reduction compared to the ANN-CS model and a substantial 82.47% improvement over the ANFIS-CS model. The Shapley Additive explanation (SHAP) analysis provided that curing age exhibited the highest SHAP score. Thus, indicating its predominant contribution to CS prediction. In predicting ER, the graphene content exhibited the highest SHAP score, signifying its predominant contribution to ER estimation. This study highlights ML's accuracy in predicting the properties of concrete with graphene nanoplatelets, offering a fast and cost-effective alternative to time-consuming experiments.

The construction sector is a major contributor to global greenhouse gas emissions. Using recycled and waste materials in concrete is a practical solution to address environmental challenges. Currently, agricultural waste is widely used as a substitute for cement in the production of eco-friendly concrete. However, traditional methods for assessing the strength of such materials are both expensive and time-consuming. Therefore, this study uses machine learning techniques to develop prediction models for the compressive strength (CS) of rice husk ash (RHA) concrete. The ML techniques used in the present study include random forest (RF), light gradient boosting machine (LightGBM), ridge regression, and extreme gradient boosting (XGBoost). A total of 348 values of CS were collected from the experimental studies, and five characteristics of RHA concrete were taken as input variables. For the performance assessment of the models, multiple statistical metrics were used. During the training phase, the correlation coefficients (R) obtained for ridge regression, RF, XGBoost, and LightGBM were 0.943, 0.981, 0.985, and 0.996, respectively. In the testing set, the developed models demonstrated even higher performance, with correlation coefficients of 0.971, 0.993, 0.992, and 0.998 for ridge regression, RF, XGBoost, and LightGBM, respectively. The statistical analysis revealed that the LightGBM model outperformed other models, whereas the ridge regression model exhibited comparatively lower accuracy. SHapley Additive exPlanation (SHAP) method was employed for the interpretability of the developed model. The SHAP analysis revealed that water-to-cement is a controlling parameter in estimating the CS of RHA concrete. In conclusion, this study provides valuable guidance for builders and researchers to estimate the CS of RHA concrete. However, it is suggested that more input variables be incorporated and hybrid models utilized to further enhance the reliability and precision of the models.

Contemporary infrastructure requires structural elements with enhanced mechanical strength and durability. Integrating nanomaterials into concrete is a promising solution to improve concrete strength and durability. However, the intricacies of such nanoscale cementitious composites are highly complex. Traditional regression models encounter limitations in capturing these intricate compositions to provide accurate and reliable estimations. This study focuses on developing robust prediction models for the compressive strength (CS) of graphene nanoparticle-reinforced cementitious composites (GrNCC) through machine learning (ML) algorithms. Three ML models, bagging regressor (BR), decision tree (DT), and AdaBoost regressor (AR), were employed to predict CS based on a comprehensive dataset of 172 experimental values. Seven input parameters, including graphite nanoparticle (GrN) diameter, water-to-cement ratio (wc), GrN content (GC), ultrasonication (US), sand content (SC), curing age (CA), and GrN thickness (GT), were considered. The models were trained with 70 % of the data, and the remaining 30 % of the data was used for testing the models. Statistical metrics such as mean absolute error (MAE), root mean square error (RMSE) and correlation coefficient (R) were employed to assess the predictive accuracy of the models. The DT and AR models demonstrated exceptional accuracy, yielding high correlation coefficients of 0.983 and 0.979 for training, and 0.873 and 0.822 for testing, respectively. Shapley Additive exPlanation (SHAP) analysis highlighted the influential role of curing age and GrN thickness (GT), positively impacting CS, while an increased water-to-cement ratio (w/c) negatively affected CS. This study showcases the efficacy of ML techniques in accurately predicting CS of graphene nanoparticle-modified concrete, offering a swift and cost-effective approach for assessing nanomaterial impact on concrete strength and reducing reliance on time-consuming and expensive experiments.

The use of fly ash in cementitious composites has gained popularity. However, assessing the depth of wear (DW) of concrete requires expensive and destructive laboratory tests utilizing specialized equipment like the rotating-cutter method. Therefore, there is a need for alternative methods to predict the depth of wear of such composites more efficiently and cost-effectively. Accordingly, the objective of this research is to utilize machine learning (ML) approaches, including one individual algorithm (Decision Tree) and two ensemble algorithms (AdaBoost Regressor and Bagging Regressor) to estimate the depth of wear of fly-ash-based concrete. A collection of 216 experimental records was obtained from the existing literature. The efficiency of the models was examined with multiple statistical indexes. The bagging regressor (BR) model provided superior estimation performance with a correlation coefficient (R) of 0.999 compared to AdaBoost regressor (R = 0.965) and decision tree (R = 0.962). The ensemble models, notably BR, provided more accurate predictions with an 87.8 % lower mean absolute error (MAE) and an 85 % lower root mean square error (RMSE) compared to the decision tree model. In addition, the BR model exhibited the lowest performance index (ρ) values of 0.016 for training and 0.012 for validation. The SHapley Additive exPlanation (SHAP) revealed that the time of testing and age are the most dominant controlling features that significantly contribute to the estimation of the depth of wear. In conclusion, utilizing ML techniques and SHAP interpretation to estimate the DW of fly ash concrete significantly reduces reliance on expensive lab tests, making durability assessment more practical and cost-effective.

The incorporation of waste foundry sand (WFS) into concrete has been recognized as a sustainable approach to improve the strength properties of waste foundry sand concrete (WFSC). However, machine learning (ML) techniques are still necessary to forecast the characteristics of WFSC and evaluate the dominant input features for the suitable mix design. For this purpose, the present work selected five ML-based techniques based on gene expression programming (GEP), deep neural network (DNN), and optimizable Gaussian process regressor (OGPR) to predict the mechanical characteristics of WFSC. To build up the predictive models, a database containing 397 values of compressive strength (CS) and 169 values of flexural strength (FS) is collected from published literature. The models' performance was evaluated via various statistical metrics and additionally, external validation criteria were employed to validate the developed models. Furthermore, the Shapley additive explanation (SHAP) was carried out to interpret the model's prediction. The DNN2 model exhibited superior performance, with R-values of 0.996 (training), 0.999 (testing), and 0.997 (validation) for the compressive strength estimation. In contrast, the GEP2 model showed poor accuracy in estimating the CS compared to other developed models, with R-values of 0.851, 0.901, and 0.844 for the training, testing, and validation sets, respectively. Similarly, for the flexural strength estimation, the DNN2 model provided R-values of 0.999 for training, 0.996 for testing, and 0.999 for validation sets, indicating its robust performance. The SHAP analysis revealed that the age, water-cement ratio, and coarse aggregate-to-cement ratio have the prime influence in determining flexural and compressive strength, respectively. The comparison of the models provided that the DNN2 model accurately estimated the output with high accuracy and lower error values and might be utilized in practical fields to reduce labor and cost by optimizing the mix combinations. Finally, for future studies, it is recommended to utilize ensemble and hybrid algorithms, as well as post-hoc explanatory techniques, to forecast the characteristics of WFSC accurately.

The ceramic waste powder (CWP) is generated in the ceramic industry during the cutting and polishing stages. It is harmful to the environment and needs a massive area for disposal. Therefore, an alternative way is required to reduce the environmental pollution and landfill caused by CWP. The aim of the study is to establish an Artificial Intelligence (AI) model for CWP concrete from the experimental results to save time and cost. Advancements in AI have made the estimation of concrete mechanical characteristics possible by employing Machine Learning (ML) approaches. In the current study, 60 concrete mixes with waste CWP are made as a partial replacement of cement by 10% and 20%. The plain concrete's ultrasonic pulse velocity (UPV) is taken as a reference. Furthermore, supervised ML techniques (i.e., Bagging, XG Boost, AdaBoost) and standalone (Decision tree) are employed to foresee the UPV of CWP concrete (CWPC). The prediction model's performance is evaluated using R2, Root Mean Square Error (RMSE) values, and Mean Absolute Error (MAE). The k-fold cross-validation is used to validate the performance of the prediction model. The XG Boost model, with an R2 value of 0.95, performed better compared to Bagging, AdaBoost, and DT models. Among all ensemble and individual models, the XG Boost model performs better with higher R2 and lower RMSE (0.081 km/s) and MAE (0.063 km/s) values. Therefore, the CWPC, as a construction material, would reduce land degradation and water pollution. In addition, applying ML techniques for estimating concrete characteristics would have reduced the consumption of efforts, resources, and time of researchers in the construction sector.

The performance of blended mortar mixtures produced with partial replacement of cement with agriculture waste ashes of rice husk, sugarcane bagasse, corncob, and waste wood were evaluated in this investigation. Cement was replaced at 5, 10, and 15 wt% with each of agriculture waste ashes for manufacturing of blended mortars. Fresh mixture properties, compressive strength, electrical resistivity, and sulfate resistance of the mixtures were evaluated at 7, 28, 56, and 90 days of curing ages and compared with that of control mixture.Test results showed a significant improvement in the later age compressive strength, electrical resistivity and sulfate resistance of blended mortar mixtures. Considerable increase in the initial and final setting times of the blended mortar mixtures was noted. Increasing demand for the superplasticizer dosage to achieve the target flow level was recorded with increase in percent replacement of cement with the agriculture waste ashes. Blended mortar mixtures produced with rice husk ash as partial replacement of cement showed superior performance among the four types of mixtures. A replacement level of 10 wt% of cement with the agriculture waste ashes was observed to be the optimum to achieve enhanced strength and durability of the blended mortar mixtures.

This study presents the physical, mechanical, and durability characteristics of the extruded cement fiber products manufactured with various types of wood fibers. Four types of wood fiber cement products, one each produced with softwood, hardwood, recycled wood, and a blend of hard/softwood fibers were investigated in the experimental program. For each type of fiber mixtures, the effect of fiber content (fiber to cement ratio) was investigated at 5%, 10%, and 15% levels. These products were tested in unaged and aged conditions after exposure to accelerated aging effects. Test results showed decrease in bulk specific gravity and increase in water absorption capacity of the wood fiber cement composites with increase in fiber content. Considerable increase in flexural toughness was recorded with increase in fiber content for the softwood fiber products. After exposure to accelerated aging conditions comprising of freeze-thaw cycles, wet-dry cycles, saturation, and warm water exposure, the softwood fibers-based cement composite provided the highest level of flexural strength and stiffness. Furthermore, fiber cement composites having 10% fiber content retained a target flexural strength of 8–10 MPa besides showing good flexural stiffness after aging. Test results of this study help in identifying wood fiber reinforcement conditions which yield extruded fiber cement products with the balance of physical, mechanical, and durability characteristics suiting different building applications.

Different cementitious matrix formulations incorporating various chemical, polymeric and mineral additives were evaluated for use in extruded fiber cement products. These alternative formulations were designed to enhance the end product performance characteristics under aging conditions and to facilitate the extrusion process. The impact of different matrix formulations on mechanical, durability and physical characteristics of extruded cellulose fiber cement products were compared. Test results show that most of the additives lowered the bulk specific gravity but increased the moisture absorption capacity of extruded fiber cement products when compared with the basic mix formulation. Generally, the additives caused drop in flexural strength but did not significantly affect the flexural toughness of the matrix formulations. Upon saturation the frictional pull-out of swollen fibers increased the ductility (deformation) capacity of extruded cellulose fiber cement products. These results provide further insight into performance characteristics of extruded cellulose fiber cement products, and help with selection of matrix mix formulations to meet performance requirements of a particular application.

This paper reports the long-term field and laboratory-tested performance of concrete produced with 20 wt% replacement of cement with powder waste glass (WG). Field investigations of WG concrete were carried out in a large-scale field project comprising of various segments subjected to harsh weathering conditions and service load over a period of about three years. While laboratory cured WG concrete and normal concrete specimens were tested in compression and flexure at various concrete ages ranging from 3 days to 300 days. After about three years of field exposure, concrete cores were extracted from various segments of the project and tested for compressive strength, moisture sorption, and abrasion resistance. A detailed survey of the various segments of the project was carried out to physically examine the state of WG concrete after three years of service. Test results of the field WG concrete showed enhanced compressive strength, up to 57% reduction in moisture sorption and up to 61% reduction in abrasion weight loss in comparison to normal concrete at 300 days of concrete age. Similarly, the laboratory tests showed 43% gain in compressive strength and 28% gain in flexural strength of WG concrete in comparison to that of normal concrete at 90 days of concrete age. Detailed physical examination of various project segments showed no signs of deterioration or material failure after three years of service. The field project also demonstrated the constructability of WG concrete similar to that of normal concrete.

Lightweight aggregate concrete mixtures incorporating 0, 10, 20, and 30 weight percent replacement of normal-weight aggregate with lightweight aggregate and crushed stone as fine aggregate were tested at 7, 28, 56, and 90 days for various strength and durability characteristics of hardened concrete. Test results indicated that compressive, flexural, and split tensile strengths and static modulus of elasticity of concrete mixtures reduced with an increase in the percent replacement of normal-weight aggregate with lightweight aggregate. Furthermore, the volume of permeable voids and hence moisture sorption in lightweight aggregate concrete reduced as the lightweight aggregate content was increased pointing at the enhanced durability of the lightweight aggregate concrete mixtures. A significant reduction in dry density of hardened concrete was recorded due to the inclusion of lightweight aggregate in concrete mixtures. Considering the strength and reduction in dry mass, 20 wt.% replacement of normal-weight aggregate with lightweight aggregate is considered to be an optimum level. Reduced density of lightweight aggregate concrete mixtures is viewed as a major source of economical design of structural members as the lower concrete density will result into a reduction of self-weight of the structural members, which will allow the economical structural design of such members with smaller cross-sections. Test results of this work also showed the viability of 100% replacement of desert sand with crushed stone sand as fine aggregate towards the production of lightweight aggregate concrete.