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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 escalation of global urbanization and industrial expansion has resulted in an increase in the emission of harmful substances into the atmosphere. Evaluating the effectiveness of titanium dioxide (TiO2) in photocatalytic degradation through traditional methods is resource-intensive and complex due to the detailed photocatalyst structures and the wide range of contaminants. Therefore in this study, recent advancements in machine learning (ML) are used to offer data-driven approach using thirteen machine learning techniques namely XG Boost (XGB), decision tree (DT), lasso Regression (LR2), support vector regression (SVR), adaBoost (AB), voting Regressor (VR), CatBoost (CB), K-Nearest Neighbors (KNN), gradient boost (GB), random Forest (RF), artificial neural network (ANN), ridge regression (RR), linear regression (LR1) to address the problem of estimation of TiO2 photocatalytic degradation rate of air contaminants. The models are developed using literature data and different methodical tools are used to evaluate the developed ML models. XGB, DT and LR2 models have high R2 values of 0.93, 0.926 and 0.926 in training and 0.936, 0.924 and 0.924 in test phase. While ANN, RR and LR models have lowest R2 values of 0.70, 0.56 and 0.40 in training and 0.62, 0.63 and 0.31 in test phase respectively. XGB, DT and LR2 have low MAE and RMSE values of 0.450 min-1/cm2, 0.494 min-1/cm2 and 0.49 min-1/cm2 for RMSE and 0.263 min-1/cm2, 0.285 min-1/cm2 and 0.29 min-1/cm2 for MAE in test stage. XGB, DT, and LR2 have 93% percent errors within 20% error range in training phase. XGB has 92% and DT, and LR2 have 94% errors with 20% range in test phase. XGB, DT, LR2 models remained the highest performing models and XGB is the most robust and effective in predictions. Feature importances reveal the role of input parameters in prediction made by developed ML models. Dosage, humidity, UV light intensity remain important experimental factors. This study will impact positively in providing efficient models to estimate photocatalytic degradation rate of air contaminants using TiO2.

Plastic waste (PW) has emerged as a global environmental concern due to its detrimental impact on ecosystems and human health. Traditional concrete heavily relies on natural aggregates like sand, gravel, and crushed stone, whose extraction leads to environmental degradation, including habitat destruction and resource depletion. Recently, the use of PW in concrete has gained attention as a sustainable alternative to these conventional aggregates. By incorporating PW as a partial replacement for natural aggregates, the construction industry can reduce its reliance on finite resources while also addressing the issue of PW. However, despite its potential environmental benefits, the incorporation of PW into concrete has primarily been explored through experimental studies, which are often time-consuming and resource-intensive. Therefore, this study aims to optimize the utilization of waste plastic in concrete through machine learning (ML) techniques, specifically Multi-Expression Programming (MEP) and Gene Expression Programming (GEP). A comprehensive literature review was conducted to compile a database for evaluating the compressive strength (CS) and tensile strength (TS) of PW concrete. The most influential parameters, such as plastic (P), gravel (G), water (W), cement (C), sand (S), and age (A), were considered as inputs in the models' development. The models developed were thoroughly evaluated using multiple statistical measures. Additionally, sensitivity analysis was conducted to discern and highlight influential factors that have a significant impact on the predicted outcomes. The findings indicate that both MEP (CS_R2 = 0.88, and TS_R2 = 0.89) and GEP (CS_R2 = 0.87, and TS_R2 = 0.88) models performed well, with MEP demonstrating slightly superior performance. Sensitivity analysis highlights the significant influence of cement (25.63 % and 24.53 %) and plastic (22.4 % and 23.44 %) on concrete strength properties. Furthermore, the equations provided by GEP and MEP models are simple to use from a practical perspective. Overall, this study contributes to sustainability efforts by promoting the incorporation of waste materials in concrete mixtures, thereby reducing reliance on cement.

Steel bars are part and parcel of reinforced concrete used in the construction projects, ranging from single family houses to super tall buildings and long span bridges. Maintaining the quality of the steel bars has always been challenging especially in developing countries. Low quality steel bars result in serviceability issues and reduce useful life of structures. This research work is focused on examining and investigating some basic facts about the quality of the steel bars produced in Pakistan. Pakistan being 5th most populous country in the world has huge consumption of steel. Laboratory testing data of steel bars are statistically analysed to have an insight in to the quality of bars. These steel bars were tested over a period of two years, which belong to several real-life projects. Properties like weight per unit length, yield strength, ultimate strength and percentage elongation were mainly analysed. Findings show that out of the stock of 4782 bars, 17.4 % failed to meet the minimum requirements provided by American Society of Testing and Materials (ASTM A615). Quality of grade 60 bars was found better than those of grade 40.

The environmental challenges and high cost of raw materials (i.e., bentonite clays) for the production of ceramic tiles raise concerns for the ceramic industry to look for alternative materials. Therefore, the current research aimed at exploring the possibility of using high plastic clays as a replacement for Bentonite clay in the manufacturing of ceramic tiles. Two different types of high plastic clay in different proportions (i.e. 0–5 %) were used in the preparation of ceramic tiles. A comprehensive material characterization, shrinkage, visual index, flexural strength, water absorption, and microstructural investigation was carried out. The results revealed that the incorporation of plastic clays RC1 from 1 % to 5 % showed a decrease in the flexural strength of ceramic tiles as compared to that of ceramic tiles with bentonite clay. The water absorption of ceramic tiles using RC1 increased with the incorporation of high plastic clays for high plastic clays. However, specimens with all replacements of bentonite clay with high plastic clays showed flexural strength greater than the minimum specified limit of ISO 10545-4. Moreover, it was observed that the replacement of bentonite clay with high plastic clays exhibited higher shrinkage. Further, the visual index of all the specimens did not show any sign of the presence of black core in the ceramic tiles incorporating high plastic clays. The life cycle assessment (LCA) was carried out for the possible utilization of high plastic clays in place of bentonite clay to produce ceramic tiles. The results showed that less than 5 % high plastic clay can effectively be used for manufacturing ceramic tiles leading to economical and sustainable construction products. Hence, it can be concluded that the high plastic clays can be utilized as an alternative to bentonite clay material for manufacturing sustainable ceramic tile leading to sustainable finishes in construction.

Cementitious Tile Adhesives (CTAs) play a key role in ensuring the elegant outlook of buildings by improving the serviceability of various tile applications, hence making them an integral part of the dry-mix mortar industry with a significant market share globally. However, CTAs require special raw material for their production, making them a less economical option for sustainable construction applications. Hence, the current research work undertook an effort to explore the potential of different type of fine materials to produce CTAs to obtain a cheap value-added product. A total of 36 mixture proportions were prepared with six different sources of fine aggregates (i.e., silica sand, quartz sand, dune sand, river sand 1, river sand 2, and river sand 3), with six varying binder-to-fine aggregates ratios, and their mechanical strength parameters were evaluated. Further microstructure analysis was performed for fine aggregates, and best and worst performing mixture proportions to gain a deep understanding about their mechanical performance. The results revealed that the mixture proportions utilizing silica sand outperformed all other formulations in terms of compressive strength, and tensile strength with a value of 25.28 MPa, and 1.35 MPa, respectively, while dune sand performed the best in terms of shear strength 1.92 MPa with cement content of 40 % at 28 days. Moreover, microstructural investigation also showed the better microstructural performance of silica sand as compared to that of all other sources of sand. To increase the applicability of the undertaken research project, an Artificial Intelligence (AI) based neural network model, along with predictive equations, was developed for the prediction of compressive strength (R2 = 0.9831), shear strength (R2 = 0.9808), and tensile strength (R2 = 0.9862). Hence, it can be concluded that the current research work provides a foundation for an effective product development process to produce tile bond adhesives using different type of raw materials available leading to economical and sustainable manufacturing of cementitious adhesives.

This study investigates the development of a cost-effective and sustainable dry-shake surface hardener for enhancing the durability of industrial concrete floors. Utilizing locally sourced materials, the research aimed at not only ensuring the hardener's strength and finish but also its economic viability and environmental friendliness. Fourteen unique mixtures were formulated by altering the sand ratios and incorporating superplasticizers to optimize the composition. These mixtures underwent rigorous testing over 7, 14, and 28 days, evaluating their compressive and flexural strengths, flowability, water absorption, and impact resistance. The findings revealed that the modified floor hardener, specifically the FH-12 mixture, exhibited superior performance across all tested parameters. It showed higher compressive and flexural strengths, enhanced impact resistance, and reduced water absorption compared to other variants and commercially available hardeners. Notably, the use of finer coarse sand and the adjustment of superplasticizer quantities significantly contributed to these outcomes. This breakthrough demonstrates the potential of employing locally available materials to create a durable, cost-effective, and environmentally friendly solution for industrial flooring. The study underscores the importance of material characterization and methodical formulation in developing construction materials that meet the dual criteria of performance and sustainability. This option is preferred for its lower environmental impact and compatibility with sustainable practices, contributing to Sustainable Development Goal 9 on industry, innovation, and infrastructure. It highlights the role of floor hardeners in global sustainability efforts.

This study investigates the substitution of traditional burnt clay bricks (BCB), used since 7000 BCE, with environmentally friendly Fly Ash-Cement and Sand Composite Bricks (FCBs), utilizing industrial waste like Coal Fly Ash (CFA) from thermal power plants. The research encompasses two phases: the first involves experimental production of FCBs, while the second focuses on optimizing FCBs by varying CFA (50%, 60%, 70%), Ordinary Portland Cement (OPC) content (9%–21%), and incorporating stone dust (SD) and fine sand. Comprehensive tests under normal and steam curing conditions, adhering to ASTM C 67-05 standards, include X-Ray Diffraction (XRD), Energy Dispersive X-Ray (EDX), and Scanning Electron Microscopy (SEM) analyses. Results indicate that steam curing enhances early strength, with an optimized mix (MD: 5S) achieving a compressive strength of 15.57 MPa, flexural strength of 0.67 MPa, water absorption rate of 20.08%, and initial rate of water absorption of 4.64 g/min per 30 in2, devoid of efflorescence. Notably, a 9% OPC and 50% CFA mix (MD: 1S) shows improved early strength of 4.95 MPa at 28 days. However, excessive CFA replacement (70%) with lesser cement content negatively impacts physio-mechanical properties. This research underscores the potential of FCBs as a sustainable and economically viable alternative to BCBs in the construction industry.

The construction industry is adopting a ground-breaking invention called Building Information Modeling (BIM) to virtually manage and plan projects throughout the building's lifespan. In the architectural, engineering, and construction (AEC) sector, the adoption of BIM is growing throughout the government sector worldwide, including governmental entities and non-profit organizations. This article covers how building information modelling was used to produce suggestions to help customers and operational teams appropriately specify information needs for projects. The ISO 19650 standards underline this as the first and most critical stage in ensuring that there is adequate information available to optimize constructed assets over the course of their whole life cycle. A study gap that was discovered was the absence of explicit recommendations aimed at helping customers determine their information needs as the appointing party. This research gives recommendations for a plan of action to identify needs and promote effective BIM project outcomes. Since Pakistan does not have a BIM standard, the authors recommended using ISO 19650 with minimum modification as Pakistan's BIM standard, as it offers a systemic framework in this regard.

This paper presents the developmental process of ultra-high performance concrete (UHPC), the most advanced form of concrete. The entire process exclusively utilized locally available materials. The mixes were prepared without using any specialized mixer or treatments, such as elevated pressure, etc. The primary objective of the research was to develop low-cost non-proprietary version of UHPC by optimizing both cementitious and non-cementitious materials to attain the highest levels of workability, compressive strength, flexural strength and durability. The research utilizes a trial-and-error approach, subjecting specimens to curing in both regular and heated water. The findings validate the viability of producing self-compacting UHPC with compressive strength ranging from 120 to 160 MPa, employing local materials and manufacturing methods. Raw materials and mixing sequence had a significant influence on the fresh and hardened properties of UHPC. The inclusion of steel fibers and the application of heat treatment remarkably enhanced the compressive strength. Furthermore, cost analysis revealed that this particular UHPC is only slightly over four times more expensive than conventional concrete, in contrast to commercially available UHPC, which is approximately 10 times expensive than traditional concrete.

The aim of this research is to predict preplaced-aggregate concrete (PAC) compressive strength (CS) by using machine learning approaches such as gene expression programming (GEP) and random forest (RF). PAC requires injecting a portland cement-sand grout with admixtures into a mold after coarse aggregate has been deposited, making CS prediction complicated and requiring substantial study. Machine learning methods were used to cut down on the time and money needed for extensive experimental testing. The database includes 135 values for CS with eleven input variables. There is an acceptable degree of agreement between predicted and experimental values, as shown by the CS R2 values of 0.94 for GEP and 0.96 for RF. When comparing RF with GEP, RF performed better as measured by R2. The lower values displayed by the statistical error also showed that RF performed better than GEP. To compare, the GEP model's COV, MAE, RSME, and RMSLE were 0.527, 1.569, 2.706, and 0.133, whereas those for RF were 0.450, 1.648, 2.17, and 0.092. The SHAP analysis showed the effects of each input parameter, illuminating the positive effect of increasing the superplasticizer content on strength and the negative effect of raising the water-to-binder ratio. Using machine learning approaches to forecast the CS of PAC, this study has the potential to boost environmental protection and economic advantage.

This study used three artificial intelligence-based algorithms – adaptive neuro-fuzzy inference system (ANFIS), artificial neural networks (ANNs), and gene expression programming (GEP) – to develop empirical models for predicting the compressive strength (CS) and slump values of fly ash-based geopolymer concrete. A database of 245 CS and 108 slump values were established from the published literature, where 17 significant parameters were chosen as input variables for the development of models. The algorithms were trained and tested using statistical measures including Nash-Sutcliffe efficiency, root-squared error, root-mean-square error, relative-root-mean-square error, mean absolute error, correlation coefficient, and regression coefficient. The comparison results showed that the GEP model was superior to the ANFIS and ANN models in terms of R-value, R2, and RMSE for both CS and slump prediction. The R-value for the CS models was 0.94 (GEP), 0.92 (ANFIS), and 0.91 (ANN), while for the slump it was 0.96 (GEP), 0.91 (ANFIS), and 0.90 (ANN). Moreover, the performance index factor values for slump and CS were found 0.03 and 0.029 for GEP-models and 0.036, 0.030 for ANFIS-models and 0.035 and 0.034 for ANN-models respectively. The sensitivity and parametric analysis were also performed for GEP-developed model. Results demonstrate that the GEP model generates more accurate prediction for the slump and CS of fly ash-based geopolymer after being rigorously trained and its hyperparameters optimized.

With the advanced development in sustainable cementitious materials, cleaner one-part alkali-activated materials (AAM) have shown a significant effect on increasing compressive strength and reduction in CO2 emissions. This study is aimed to prepare one-part AAM based on fly ash (FA) and hydrated lime (LM) as main precursors. The impact of LM content as replacement of FA and activator dosage was evaluated on fresh and hardened properties of one-part FA/LM-based alkali-activated mortar. Fresh characteristics of AAM were evaluated through workability (setting time, flow table) and rheology, while hardened properties were evaluated by compressive strength, flexural strength, water absorption, durability test, X-ray diffraction (XRD), Fourier transform infrared spectroscopy (FTIR) and scanning electron microscopy (SEM) tests. The test results revealed that the addition of FA/LM-based AAM increases the reactivity leading to earlier hardening of AAM. FA/LM-based AAM has shown higher compressive strength of 41.5 MPa. The rheology parameters show the increment in yield stress and plastic viscosity by increasing the lime content in the AAM. The activator dosage of sodium hydroxide increases the setting time and durability of AAM. The enhancement in strength and rheological parameters can be assigned to the enhanced geopolymerization reactions, leading to the formation of denser and compacted gels.

The experimental research object was low-density polyethene (LDPE) waste plastic as a binding material in the production of LDPE bonded sand paver blocks. The LDPE waste plastic was melted in the open air and mixed with sand to form paver blocks. LDPE to sand ratio, percentage of coconut fibers, and sand particle size was varied. The compressive strength, water absorption and density of paver blocks were measured. It was observed that the incorporation of coconut fiber proportion at 3% improved compressive strength by 18.4% and reduced water absorption by 54.1%. The best experimental results were observed at LDPE to sand ratio of 30:70, whereas the finest sand gives the highest compressive strength. The sustainable paver blocks developed in this research can be used in water-clogged areas.

The huge production of woven polypropylene bags has led to an increase generation of plastic waste. The recycling of waste woven polypropylene bags and textiles in construction applications can be a viable option. Therefore, an attempt has been made to utilize the woven polypropylene fibers and textiles in the production of gypsum-based composite false ceiling tile for an economical and sustainable construction product. In the current research work, different water-to-powder ratios (0.6 %, 0.7 %, 0.8 %, 0.9 %) and fiber contents (0.25 %, 0.35 %, 0.45 %, 0.55 %) in the gypsum-based composite were prepared. Flexural plates with different thicknesses (18, 15, 12, and 9 mm) incorporating different recycling fibers and reinforced with different types of meshes were manufactured to produce sustainable gypsum-based plates. The physical properties, compressive and flexural strength properties of the fiber-reinforced gypsum-based composite were also evaluated. The textile and woven polypropylene fiber mesh reinforced gypsum false ceiling plates were compared with the control specimen. The results revealed that the density of gypsum-based composite decreased up to 26 % with the increase in the proportion of waste fiber contents. The compressive and flexural strength of gypsum-based composites exhibited a decrease with an increase in fiber contents and water-to-powder ratio. However, all the specimens satisfied the acceptable limit of 1 MPa and 2 MPa, respectively, according to BS EN 14246. The bending strength of all the plates incorporating different fiber content and thicknesses satisfied the minimum specified flexural strength criteria according to BS EN 14246. Moreover, the plates with different thicknesses and fiber contents also satisfied the minimum specified flexural criteria except for the plate with a thickness of 9 mm. However, all the plates with varying thicknesses and reinforced mesh have also satisfied the minimum load criteria specified by BS EN 14246. Thus, it can be envisioned that the woven polypropylene fibers and textiles can be effectively utilized to produce sustainable gypsum-based composite leading to reduce waste generation.

Although the dumping of waste generated by industries in the environment poses a hazard, it is feasible to use waste and unused materials to manufacture high-performance concrete. By recycling these wastes in binary and ternary blends, sustainable and durable concrete mixtures can be created, resulting in the development of sustainable concrete structures. Therefore, a research project was undertaken to develop high-performance concrete (HPC) using available supplementary and waste materials, such as fly ash (FA), silica fume (SF), Quartz filler (QF), and limestone powder (LSP), as partial replacements for cement in binary and ternary blends under various curing conditions. Different percentages (5, 8, 10, 15, 20%) of the supplementary materials were mixed in binary and ternary combinations to determine the optimal dosage for high-performance concrete. The fresh and hardened properties of the concrete were evaluated using various tests, including slump, compressive strength, rapid chloride ion permeability, porosity, and drying shrinkage tests. The prepared concrete specimens were tested using various concentrations of binder replacement in binary, ternary, and quaternary combinations. The results showed that incorporating quartz filler as a partial replacement of cement up to 8% yielded positive results regarding mechanical properties. However, all mixtures containing different dosages of SF, QF, FA, and LSP demonstrated significant improvement in durability-related properties under normal and high-temperature curing conditions. Interestingly, the porosity of mixtures incorporating fly ash and LSP showed a slight increase compared to identical specimens under normal curing conditions. Moreover, the drying shrinkage behavior of ultrafine fillers (QF, SF, LSP, FA) indicated that their incorporation led to an increase in the shrinkage of high-performance mixtures under normal and high-temperature curing conditions. Binary and ternary blends incorporating QF, LSP, and SF showed an increase of around 5–8% in shrinkage under high-temperature curing compared to identical specimens under normal curing conditions. Therefore, it can be concluded that QF, LSP, SF, and FA can be efficiently used to produce high-performance cementitious composites, leading to sustainable concrete material.

One of the most significant and critical urban assets for a sustainable community is the sewer pipeline network and water distribution system. Water sewer networks and distribution systems have a definite service life span to provide continuous facilities to end users. Therefore, it is pertinent to continuously evaluate the condition of water and sewer concrete pipelines to ensure the reliable, sustainable, and cost-efficient transport of water and sewerage for the safety of society. The condition assessment is commonly carried out by visual observations followed by some non-destructive testing methods. However, it is the need of the hour to shift assessment methods to advance assessment techniques to save time and money for our community. Currently, in this project, the condition assessment of pre-cast concrete pipes was carried out by destructive and non-destructive methods. Different test trials i.e., ultra-sonic pulse velocity, Schmidt hammer also known as rebound hammer test, visual inspection, three edge bearing test, and core cutting test on the old buried and new concrete pipes were performed. It was observed that concrete used for the construction of existing precast concrete pipes still has better quality indices after 20 years as compared to that of concrete of new pipes. However, steel has deteriorated with time and clear corrosion of steel was identified in existing pre-cast concrete pipes. At the same time, it was observed that there should be an automated mechanism to continuously asses the condition of pre-cast existing pipes which will address the sustainable development goals (SDG 6, 9, 11). Consequently, it can be said that condition assessment of pre-cast concrete pipes will lead to sustainable societies and infrastructure.

Abstract Industrial waste has been rapidly increased day by day because of the fast-growing population which results environmental pollutions. It has been recommended that the disposal of industrial waste would be greatly reduced if it could be incorporated in concrete production. In cement concrete technology, there are many possibilities to use waste materials either as cement replacement or aggregate in concrete production. Two major industrials waste are glass and marble waste. The basic objective of this investigation is to examine the characteristics of concrete waste glass (WG) as binding material in proportions 10%, 20% and 30% by weight of cement. Furthermore, to obtain high strength concrete, waste marble in proportion of 40%, 50% and 60% by weight cement as fine aggregate were used as a filler material to fill the voids between concrete ingredients. Fresh properties were evaluated through slump cone test while mechanical performance was evaluated through compressive strength and split tensile strength which were performed after 7 days, 28 days and 56 days curing. Results show that, workability of concrete decreased with incorporation of waste glass and marble waste. Furthermore, mechanical performance improved considerably up 20% and 50% substitution of waste glass and waste marble respectively. Statistical approach of Response Surface Methodology (RSM) was used optimize both waste materials in concrete. Results indicate better agreement between statistical and experimental results.

Abstract Self compacting concrete (SCC) is special type of concrete which is highly flowable and non-segregated and by its own mass, spreads into the formwork without any external vibrators, even in the presence of thick reinforcement. But SSC is also brittle nature like conventional concrete, which results in abrupt failure without giving any deformation (warning), which is undesirable for any structural member. Thus, self-compacting concrete (SCC) needs some of tensile reinforcement to enhance tensile strength and prevent the unsuitable abrupt failure. But fiber increased tensile strength of concrete more effectively than compressive strength. Hence, it is essential to add pozzolanic materials into fiber reinforced concrete to achieve high strength, durable and ductile concrete. This study is conducted to assess the performance of SCC with substitutions of marble waste (MW) and coconut fiber (CFs) into SCC. MW utilized as cementitious (pozzolanic) materials in percentage of 5.0 to 30% in increment of 5.0% by weight of binder and concrete is reinforced with CFs in proportion of 0.5 to 3.0% in increment of 0.5% by weight of binder. Rheological characteristics were measured through its filling and passing ability by using Slump flow, Slump T50, L-Box, and V-funnel tests while mechanical characteristics were measured through compressive strength, split tensile strength, flexure strength and bond strength (pull out) tests. Experimental investigation show that MW and CFs decrease the passing ability and filling ability of SCC. Additionally, Experimental investigation show that MW up to 20% and CFs addition 2.0% by weight of binder tend to increase the mechanical performance of SCC. Furthermore, statistical analysis (RSM) was used to optimize the combined dose of MW and CFs into SCC to obtain high strength self-compacting 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.

Cement is the main component of concrete, a widely used building material. Cement production requires substantial energy, exhausts natural resources, and causes CO2 emissions. Efforts are being undertaken to develop a concrete binder instead of cement. Geopolymer composite (GPC) is developed as a potential new building material, offering a clean alternative for construction sustainability. This study accumulated extensive bibliometric data on GPC from the Scopus database and conducted a scientometric analysis employing an appropriate software. The leading sources of publications, highly used keywords in the published articles, writers and papers with the highest citations, and active regions were all identified in the scientometric analysis. Manual reviews are unable to deal with the vast bibliometric data effectively. Additionally, this study covers the most crucial research concerns and shortcomings in existing research about GPC adoption and application. Lastly, further research guidelines are suggested. The scientometric review will assist academics from various states to exchange innovative ideas and expertize, encourage collaborative research, and develop joint ventures.

Concrete is a widely used construction material, and cement is its main constituent. Production and utilization of cement severely affect the environment due to the emission of various gases. The application of geopolymer concrete plays a vital role in reducing this flaw. This study used supervised machine learning algorithms, decision tree (DT), bagging regressor (BR), and AdaBoost regressor (AR) to estimate the compressive strength of fly ash-based geopolymer concrete. The coefficient of determination (R2), mean absolute error, mean square error, and root mean square error were used to evaluate the model's performance. The model's performance was further confirmed using the k-fold cross-validation technique. Compared to the DT and AR model, the bagging model was more effective in predicting results, with an R2 value of 0.97. The lesser values of the errors (MAE, MSE, RMSE) and higher values of the R2 were the clear indications of the better performance of the model. Additionally, a sensitivity analysis was conducted to ascertain the degree of contribution of each parameter towards the prediction of the results. The application of machine learning techniques to predict concrete's mechanical properties will benefit the area of civil engineering by saving time, effort, and resources.

The provision of appropriate fire-safety measures is a basic need in building design to guarantee the safety of its occupants. Damage due to fire is one of the most destructive aspects that cause deterioration of reinforced concrete structures. Although concrete is a noncombustible material, its physical, chemical, and mechanical characteristics degrade if exposed to a high temperature. The seriousness of a fire in concrete structures is mostly determined by the magnitude and duration of the fire. If the magnitude of fire is minor and for a limited time then the damage to the concrete members is likely to be minimal. Similarly, a higher magnitude or high temperature with a longer duration will cause maximum damage or may result in the collapse of concrete structures. The purpose of this review is to summarize the damage of reinforced concrete (RC) structures after a severe occurrence of fire. Additionally, to discuss the firefighting deficiencies, and remedial measures adopted for recovering the damage due to fire in high-rise buildings. The current study is a review of previous studies from the last decade on important fire damage assessment measures used for damage assessment of RC structures. Some case studies have also been reported in this work. The review results show the popularity of surveys and case studies on fire damage related to RC structures. The essential contributing factors are collected that correspond to the damage of RC structures due to fire. The electrical problems, faulty fire detection systems, and a lack of firefighting equipment, and obstacles in emergency exits were shown to be the most frequent causes of fire damage.

This experimental investigation reveals the projected benefits of concrete-filled double-skin tubular columns. These columns are made up of an outside steel tube, an interior UPVC pipe, and a sandwich of concrete among these tubes. The effect of the dimension ratio of the outer-to-inner tube, the thickness ratio of the outer tube, and the internal tube material (steel/UPVC) on the ultimate compressive strength of double-skin tubular columns samples filled with concrete are investigated. Finally, the experimental findings were compared to previously published design approaches in AISC (2010), GB50936 (2014), and EC4 (2004). The analysis revealed that GB50936 (2014) produces the best outcomes, followed by AISC (2010) and EC4 (2004). In addition, the samples were tested in elevated temperatures to study their post-fire axial capacity. It was discovered that the type of infilled concrete and interior tube material has no major impact on the performance of samples at elevated temperatures.

Rice husk ash (RHA) is obtained by burning rice husks. An advanced programming technique known as genetic expression programming (GEP) is used in this research for developing an empirical multiphysics model for predicting the compressive strength of RHA incorporated concrete. A vast database comprising of 250 data points is obtained from the extensive and consistent literature review. Different parameters such as age, RHA content, cement content, water content, amount of superplasticizer and aggregate content are used as inputs. A closed-form equation solution was obtained to predict the compressive strength of RHA based on input parameters. The performance of GEP is evaluated by comparing it with regression models. Statistical parameter R2 is used to assess the results predicted by GEP and regression models. Statistical and parametric analysis is also carried out to determine the influence of inputs on the outcome. The GEP model performed better in all terms as compared to other models.

This paper presents a study of the combined effect of PVA (powder form) and fly ash on the mechanical properties of concrete. Three different parameters (PVA, fly ash, and different patterns of curing) were used. The Taguchi statistical method has been used to decrease the number of concrete specimens from 160 to 54. Different percentages of PVA (1% and 2%) with fly ash (10% and 20%) were used in 36 concrete samples. Afterward, all samples were cured in three different ways namely: Open air-dry curing, normal water curing, and temperature bath curing. Samples with 1% PVA and 10% Fly ash shows the highest compressive strength, tensile strength, and modulus of elasticity. The improvement in strength is due to the interaction of PVA and FA with cement, filling pores, and strengthening the bond between the cement. Furthermore, the samples kept in water curing exhibited the greatest strength among all types of curing.

Concrete and plastic are a major source of environmental contamination and unsustainability. This study has been employed to come up with the eco-friendly concrete by using the plastic waste to promote the sustainability. Gamma radiations were used to enhance the micro-structure of plastic waste to increase the mechanical properties of resulting concrete. The crystallinity and chemical properties of regular and irradiated plastic were studied using x-ray diffraction (Xrd) and x-ray fluorescence (Xrf), respectively. Enhancement in the crystallinity and chemical properties were observed in the irradiated plastic. Different compositions by weight were opted for replacement of cement and fine aggregate for both regular and irradiated plastic waste with silica fume and superplasticizers. A set of experiments were performed on concrete and the effect of plastic waste on the compressive strength and split tensile strength of concrete was examined. Untreated replacement of plastic waste has shown to decrease the compressive strength and anomalous behavior was observed in case of split tensile strength. Both the mechanical properties were enhanced by replacing the cement and fine aggregates with irradiated plastic waste including silica fume and superplasticizers. This study contributes towards sustainable development of green concrete by lowering the carbon emissions and overcoming the fine aggregate scarcity problem. Moreover, as plastic is ductile and inclusion of plastic in concrete lowers the overall weight of the structure; impact of an earthquake is reduced.

In this paper performance of graphite nano platelets and carbon nanotubes was investigated in both conventional as well as self-compacting mortar. Workability, linear-shrinkage, rheological properties, flexural and compressive strength, air content and water absorption tests were conducted. Results reveal that by addition of nano media in cement matrix workability, shrinkage, air content, and water absorption adamantly reduces as compared to control mixtures. On the other hand yield stress, plastic viscosity, compressive and flexural strength enhances significantly. The shrinkage response of SCM-3 and CCM-3 depicts reduction of 66% and 61% respectively. Furthermore, yield stress and plastic viscosity increase 53% and 12% respectively. By addition of nano media deviation in crack propagation, more nucleation sites, capturing the micro voids, filling the pores and dense packing of the hydrated products were found. Increase in compressive strength and flexural strength was observed. Hence hybrid nano media can be intruded as a potential candidate in cement matrix for enhancing the structural properties. Keywords: Linear shrinkage, Rheological properties, Stress strain response, Air content, Water absorption and microstructure

The construction sector has a significant impact on the environment, contributing significantly to energy consumption, resource depletion, and CO2 emissions. This sector is currently shifting away from natural materials and cement in favor of alternative materials, thereby reducing environmental impact and promoting sustainability. Worldwide, enormous quantities of waste materials are generated. Most of these waste materials are hazardous, corrosive, flammable, chemically reactive, incendiary, and infectious and are typically disposed of in landfills, causing environmental pollution and posing health risks. As a result, incorporating waste materials into concrete would be a more prudent course of action. This study collected vast bibliometric data comprising journal articles and review articles from the Scopus database over the last two decades and conducted a scientometric review on waste material utilization in concrete. Scientometric analysis is used to determine the current state of research by analyzing available bibliometric data and identifying related publication areas, sources with the most publications, the most frequently occurring keywords, authors, and papers with the most citations, and states that have made the greatest contribution to the field of waste materials utilization in concrete. Moreover, the most active research areas are identified and discussed. Also, the sustainability aspects of recycling waste materials in concrete are discussed, and finally, future research is proposed. The scientometric review will enable scholars from diverse countries to exchange novel ideas and knowledge, foster research collaboration, and establish joint ventures.

Cement-based composites (CBCs) are the widely used construction materials, and cement is their main ingredient. The production of cement consumes a considerable amount of energy, releases a substantial quantity of CO2 to the environment, and causes depletion of natural resources. Thus, utilizing sugarcane bagasse ash (SBA) in CBCs in place of cement might be a sustainable approach. In this review, two approaches have been adopted, namely, scientometric analysis and a thorough manual review of the use of SBA in CBCs. A scientometric analysis can deal with huge bibliometric data without complications. This study retrieved journal articles and review articles on SBA utilization in CBCs available at the Scopus database from 2007 to 2021 and performed a scientometric analysis using a suitable software tool. The aim of the scientometric analysis was to ascertain the current state of research and to identify relevant publication fields, sources with the most publications, the most frequently used keywords, the most cited articles and authors, and the countries that have made the greatest contribution to the field of SBA utilization in CBCs. Additionally, the influence of SBA on the fresh and hardened properties as well as durability properties of CBCs are comprehensively discussed. It was found that the top publication source is construction and building materials with 36 publications, the author with most publications is Bahurudeen A with 14 articles, and the country having the highest contribution in the relevant field is India with 110 publications. Furthermore, the addition of SBA in CBCs as cement replacement was found to be beneficial in terms of sustainability aspects and performance of composites. This study also highlighted the limitations associated with SBA utilization in CBCs and reported recommendations for future studies.

The use of superabsorbent polymers, sometimes known as SAP, is a tremendously efficacious method for reducing the amount of autogenous shrinkage (AS) that occurs in high-performance concrete. This study utilizes support vector regression (SVR) as a standalone machine-learning algorithm (MLA) which is then ensemble with boosting and bagging approaches to reduce the bias and overfitting issues. In addition, these ensemble methods are optimized with twenty sub-models with varying the nth estimators to achieve a robust R2. Moreover, modified bagging as random forest regression (RFR) is also employed to predict the AS of concrete containing supplementary cementitious materials (SCMs) and SAP. The data for modeling of AS includes water to cement ratio (W/C), water to binder ratio (W/B), cement, silica fume, fly ash, slag, the filer, metakaolin, super absorbent polymer, superplasticizer, super absorbent polymer size, curing time, and super absorbent polymer water intake. Statistical and k-fold validation is used to verify the validation of the data using MAE and RMSE. Furthermore, SHAPLEY analysis is performed on the variables to show the influential parameters. The SVM with AdaBoost and modified bagging (RF) illustrates strong models by delivering R2 of approximately 0.95 and 0.98, respectively, as compared to individual SVR models. An enhancement of 67% and 63% in the RF model, while in the case of SVR with AdaBoost, it was 47% and 36%, in RMSE and MAE of both models, respectively, when compared with the standalone SVR model. Thus, the impact of a strong learner can upsurge the efficiency of the model.

The utilization of carbon-fiber-reinforced polymer (CFRP) composites as strengthening materials for structural components has become quite famous over the last couple of decades. The present experimental study was carried out to examine the effect of varied widths of externally bonded CFRP on the debonding strain of CFRP and the failure mode of plain concrete beams. Twelve plain concrete prims measuring 100 mm × 100 mm × 500 mm were cast and tested under identical loading conditions. The twelve specimens include two control prisms, i.e., without CFRP strips, and the remaining ten prisms were reinforced with CFRP strips with widths of 10 mm, 20 mm, 30 mm, 40 mm, and 50 mm, respectively, i.e., two prisms in each group. Four-point loading flexural testing was carried out, and the resulting data are presented in the form of peak load vs. midpoint displacement, load vs. concrete strain, and load vs. CFRP strain. The peak load was directly recorded from the testing machine, while the midpoint deflection was recorded through the linear variable differential transducer (LVDT) installed at the midpoint. To measure the strain, two separate strain gauges were installed at the bottom of each concrete prism, i.e., one on the concrete surface and the other on the surface of the CFRP strip. The results of this study indicate that the debonding strain is a function of CFRP strip width and that the failure patterns of beams are significantly affected by the CFRP reinforcement ratio.

This study aims to investigate the two-way shear strength of concrete slabs with FRP reinforcements. Twenty-one strength models were briefly outlined and compared. In addition, information on a total of 248 concrete slabs with FRP reinforcements were collected from 50 different research studies. Moreover, behavior trends and correlations between their strength and various parameters were identified and discussed. Strength models were compared to each other with respect to the experimentally measured strength, which were conducted by comparing overall performance versus selected basic variables. Areas of future research were identified. Concluding remarks were outlined and discussed, which could help further the development of future design codes. The ACI is the least consistent model because it does not include the effects of size, dowel action, and depth-to-control perimeter ratio. While the EE-b is the most consistent model with respect to the size effect, concrete compressive strength, depth to control perimeter ratio, and the shear span-to-depth ratio. This is because of it using experimentally observed behavior as well as being based on mechanical bases.

Alkali–silica reaction (ASR) is one of the major durability issues that affect the material degradation and structural performance, compromising the service life of concrete structures. Therefore, this study was planned to investigate the potential of ASR for locally available unexplored and vastly used aggregates, as per ASTM C1260. Aggregates from five different sources (Shalozan, Abbotabad, Orakzai, Swabi and Sada) were procured from their respective crusher sites. Mineralogical components of these aggregates were studied using the petrographic analysis. Cube, prism and mortar bar specimens were cast using mixture design in accordance with ASTM C1260 and placed in sodium hydroxide solution at 80 °C for 90 days. Identical specimens were also cured in water for the purpose of comparison. It was observed that mortar bar expansion of Orakzai aggregate was higher among the other tested aggregates and greater than 0.20% at 28 days, indicating the reactive nature according to ASTM C1260. Petrographic analysis also revealed the presence of reactive silica (quartzite) in the tested Orakzai source. It was observed that the compressive and flexural strengths of specimens exposed to ASR conducive environment was lower than the identical specimens placed in water. For instance, an approximately 9% decrease in compressive strength was observed for Orakzai aggregates exposed to ASR environment at 90 days compared to similar specimens placed in water curing. Moreover, microstructural analysis showed the development of micro-cracks for specimens incorporating Orakzai source aggregates. This study assists the construction stakeholders for the potential of unexplored local aggregates with regard to ASR before its utilization in mega construction projects.

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.

Burnt clay bricks are one of the most important building units worldwide, are easy and cheap to make, and are readily available. However, the utilization of fertile clay in the production of burnt clay bricks is also one of the causes of environmental pollution because of the emission of greenhouse gases from industrial kilns during the large-scale burning process. Therefore, there is a need to develop a new class of building units (bricks) incorporating recycled industrial waste, leading toward sustainable construction by a reduction in the environmental overburden. This research aimed to explore the potential of untreated coal ash for the manufacturing of building units (coal ash unburnt bricks). Coal ash unburnt bricks were manufactured at an industrial brick plant by applying a pre-form pressure of 3 MPa and later curing them via water sprinkling in a control shed. Various proportions of coal ash (i.e., 30, 35, 40, 45, 50, and 55%) were employed to investigate the mechanical and durability-related properties of the resulting bricks, then they were compared with conventional burnt clay bricks. Compressive strength, flexural strength, an initial rate of water absorption, efflorescence, microstructural analysis via scanning electron microscopy, and cost analysis were conducted. The results of the compressive strength tests revealed that the compressive strength of coal ash unburnt brick decreased with an increase in the content of coal ash; however, up to a 45% proportion of coal ash, the minimum required compressive strength specified by ASTM C62 and local building codes was satisfied. Furthermore, bricks incorporating up to 45% of coal ash also satisfied the ASTM C62 requirements for water absorption. Coal ash unburnt bricks are lighter in weight owing to their porous developed microstructure. The cost analysis showed that the utilization of untreated, locally available coal ash in brick production leads us on the path toward more economical and sustainable building units.

Fly ash (FA) is a residual from thermal industries that has been effectively utilized in the production of FA-based geopolymer concrete (FGPC). To avoid time-consuming and costly experimental procedures, soft computing techniques, namely, random forest regression (RFR) and gene expression programming (GEP), are used in this study to develop an empirical model for the prediction of compressive strength of FGPC. A widespread, reliable, and consistent database of compressive strength of FGPC is set up via a comprehensive literature review. The database consists of 298 compressive strength data points. The influential parameters that are considered as input variables for modelling are curing temperature T, curing time t, age of the specimen A, the molarity of NaOH solution M, percent SiO2 solids to water ratio % S/W in sodium silicate (Na2SiO3) solution, percent volume of total aggregate ( % AG), fine aggregate to the total aggregate ratio F/AG, sodium oxide (Na2O) to water ratio N/W in Na2SiO3 solution, alkali or activator to the FA ratio AL/FA, Na2SiO3 to NaOH ratio Ns/No, percent plasticizer (% P), and extra water added as percent FA EW%. RFR is an ensemble algorithm and gives outburst performance as compared to GEP. However, GEP proposed an empirical expression that can be used to estimate the compressive strength of FGPC. The accuracy and performance of both models are evaluated via statistical error checks, and external validation is considered. The proposed GEP equation is used for sensitivity analysis and parametric study and then compared with nonlinear and linear regression expressions.

In this research, multiexpression programming (MEP) has been employed to model the compressive strength, splitting tensile strength, and flexural strength of waste sugarcane bagasse ash (SCBA) concrete. Particle swarm optimization (PSO) algorithm was used to fine-tune the hyperparameter of the proposed MEP. The formulation of SCBA concrete was correlated with five input parameters. To train and test the proposed model, a large number of data were collected from the published literature. Afterward, waste SCBA was collected, processed, and characterized for partial replacement of cement in concrete. Concrete specimens with varying proportion of SCBA were prepared in the laboratory, and results were used for model validation. The performance of the developed models was then evaluated by statistical criteria and error assessment tests. The result shows that the performance of MEP with PSO algorithm significantly enhanced its accuracy. The essential input variables affecting the output were revealed, and the parametric analysis confirms that the models are accurate and have captured the essential properties of SCBA. Finally, the cross validation ensured the generalized capacity and robustness of the models. Hence, the adopted approach, i.e., MEP-based modeling with PSO, could be an effective tool for accurate modeling of the concrete properties, thus directly contributing to the construction sector by consuming waste and protecting the environment.

Numerous tests are used to determine the performance of concrete, but compressive strength (CS) is usually regarded as the most important. The recycled aggregate concrete (RAC) exhibits lower CS compared to natural aggregate concrete. Several variables, such as the water-cement ratio, the strength of the parent concrete, recycled aggregate replacement ratio, density, and water absorption of recycled aggregate, all impact the RAC’s CS. Many studies have been carried out to ascertain the influence of each of these elements separately. However, it is difficult to investigate their combined effect on the CS of RAC experimentally. Experimental investigations entail casting, curing, and testing samples, which require considerable work, expense, and time. It is vital to adopt novel methods to the stated aim in order to conduct research quickly and efficiently. The CS of RAC was predicted in this research utilizing machine learning techniques like decision tree, gradient boosting, and bagging regressor. The data set included eight input variables, and their effect on the CS of RAC was evaluated. Coefficient correlation (R2), the variance between predicted and experimental outcomes, statistical checks, and k-fold evaluations, were carried out to validate and compare the models. With an R2 of 0.92, the bagging regressor technique surpassed the decision tree and gradient boosting in predicting the strength of RAC. The statistical assessments also validated the superior accuracy of the bagging regressor model, yielding lower error values like mean absolute error (MAE) and root mean square error (RMSE). MAE and RMSE values for the bagging model were 4.258 and 5.693, respectively, which were lower than the other techniques employed, i.e., gradient boosting (MAE = 4.956 and RMSE = 7.046) and decision tree (MAE = 6.389 and RMSE = 8.952). Hence, the bagging regressor is the best suitable technique to predict the CS of RAC.

The emission of greenhouse gases and natural-resource depletion caused by the production of ordinary Portland cement (OPC) have a detrimental effect on the environment. Thus, an alternative means is required to produce eco-friendly concrete such as geopolymer concrete (GPC). However, GPC has a complex cementitious matrix and an ambiguous mix design. Aside from that, the composition and proportions of materials utilized may have an impact on the compressive strength. Similarly, the use of robust and efficient machine-learning (ML) approaches is now required to forecast the strength of such a composite cementitious matrix. As a result, this study anticipated the compressive strength of GPC with waste resources using ensemble and non-ensemble ML algorithms. This was accomplished through the use of Anaconda (Python). To build a strong ensemble learner by integrating weak learners, adaptive boosting, random forest (RF), and ensemble learner bagging were employed. Furthermore, ensemble learners were utilized on non-ensemble or weak learners, such as decision trees (DT) and support vector machines (SVM) via regression. The data encompassed 156 statistical samples in which nine variables, namely superplasticizer (kg/m3), fly ash (kg/m3), ground granulated blast-furnace slag (GGBS), temperature (°C), coarse and fine aggregate (kg/m3), sodium silicate (Na2SiO3), and sodium hydroxide (NaOH), were chosen to anticipate the results. Exploring it in depth, twenty sub-models with ensemble boosting and bagging approaches were trained, and tuning was performed to achieve the highest possible coefficient of determination (R2). Moreover, cross K-Fold validation analysis and statistical checks were performed via indicators for the evaluation of the models. The result revealed that ensemble approaches yielded robust performance compared to non-ensemble algorithms. Generally, an ensemble learner with the RF and bagging approach on a DT yielded robust performance by achieving a better R2 as 0.93, and with the lowest statistical errors. The communal model in artificial-intelligence analysis, on average, improved the accuracy of the model.

Numerical modelling can simulate the interaction between structural elements and the soil continuum in a piled-raft foundation. The present work utilized a two-dimensional finite element Plaxis 2D software to investigate the settlement, swelling, and structural behavior of foundations during the settlement and swelling of soil on various soil profiles under various load combinations and geometry conditions. The field and laboratory testing have been performed to determine the behavior soil parameters necessary for numerical modelling. The Mohr–Coulomb model is utilized to simulate the behavior of soil, as this model requires very few input parameters, which is important for the practical geotechnical behavior of soil. From this study, it was observed that, as soil is soft and has less stiffness, the un-piled raft was not sufficient to resists and higher loads and exceeds the limits of settlement. Piled raft increases the load carrying capacity of soil, and the lower soil layer has a higher stiffness where the pile rests, decreasing the significant settlement. Further, the effects of (L/d) and (s/d) of the pile and Krs on the settlement are also discussed, detailed numerically under different scenarios. The swelling of expansive soil was also simulated in Plaxis 2D with an application of positive volumetric strain. The above-mentioned parametric study was similarly implemented for the heaving of foundation on expansive soil.

Preplaced aggregate concrete (PAC) is prepared in two steps, with the coarse aggregate being initially laid down in the formwork, after which a specialised grout is injected into it. To enhance the properties of concrete and to reduce the emission of CO2 produced during the production of cement, supplementary cementitious materials (SCMs) are used to partially substitute ordinary Portland cement (OPC). In this study, 100 mm × 200 mm (diameter x height) PAC cylinders were cast with 10 per cent of cement being substituted with silica fume; along with that, 1.5% dosage of Macro polypropylene fibres were also introduced into the coarse aggregate matrix. Compressive strength test, splitting tensile strength test, mass loss at 250 °C, and compressive strength at 250 °C were performed on the samples. PAC samples with 10% of cement replaced with Silica Fume (SPAC) were used as control samples. The primary objective of this study was to observe the effect of the addition of Polypropylene fibres to PAC having Silica fume as SCM (FRPAC). The aforementioned tests showed that FRPAC had a lower compressive strength than that of the control mix (SPAC). FRPAC had greater tensile strength than that of NPAC and SPAC. Mass loss at 250 °C was greater in SPAC compared to FRPAC. The compressive strength loss at 250 °C was significantly greater in FRPAC compared to SPAC. FRPAC exhibited a greater strain for the applied stress, and their stress-strain curve showed that FRPAC was more ductile than SPAC.

Geopolymers may be the best alternative to ordinary Portland cement because they are manufactured using waste materials enriched in aluminosilicate. Research on geopolymer composites is accelerating. However, considerable work, expense, and time are needed to cast, cure, and test specimens. The application of computational methods to the stated objective is critical for speedy and cost-effective research. In this study, supervised machine learning approaches were employed to predict the compressive strength of geopolymer composites. One individual machine learning approach, decision tree, and two ensembled machine learning approaches, AdaBoost and random forest, were used. The coefficient correlation (R2), statistical tests, and k-fold analysis were used to determine the validity and comparison of all models. It was discovered that ensembled machine learning techniques outperformed individual machine learning techniques in forecasting the compressive strength of geopolymer composites. However, the outcomes of the individual machine learning model were also within the acceptable limit. R2 values of 0.90, 0.90, and 0.83 were obtained for AdaBoost, random forest, and decision models, respectively. The models’ decreased error values, such as mean absolute error, mean absolute percentage error, and root-mean-square errors, further confirmed the ensembled machine learning techniques’ increased precision. Machine learning approaches will aid the building industry by providing quick and cost-effective methods for evaluating material properties.

The consumption of waste materials in the construction sector is a sustainable approach that helps in reducing the environmental pollution and decreases the construction cost. The present research work emphasizes the mechanical properties of bituminous concrete mix prepared with crumb rubber (CR) and waste sugarcane bagasse ash (SCBA). For the preparation of bituminous concrete mix specimens with CR and SCBA, the effective bitumen content was determined using the Marshall Mix design method. A total of 15 bituminous concrete mix specimens with 4%, 4.5%, 5%, 5.5% and 6% of bitumen content were prepared, and the effective bitumen content turned out to be 4.7%. The effect of five different CR samples of 2%, 4%, 6%, 8% and 10% by weight of total mix and SCBA samples of 25%, 50%, 75% and 100% by weight of filler were investigated on the performance of bituminous concrete. A total of 180 samples with different percentages of CR and SCBA were tested for indirect tensile strength (ITS) and Marshall Stability, and the results were compared with conventional bituminous concrete mix. It was observed that the stability values rose with an increase in CR percentage up to 6%, while the flow values rose as the percentage of SCBA increased in the mix. Maximum ITS results were observed at 4% CR and 25% SCBA replacement levels. However, a decrease in stability and ITS result was observed as the percentages of CR and SCBA increased beyond 4% and 25%, respectively. We concluded that the optimum CR and SCBA content of 4% and 25%, respectively, can be effectively used as a sustainable alternative in bituminous concrete mix.

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.

Improvements in tensile strength and impact resistance of concrete are among the most researched issues in the construction industry. The present study aims to improve the properties of concrete against impact loadings. For this purpose, energy-absorbing materials are used along with fibers that help in controlling the crack opening. A polymer-based energy-absorbing admixture, SBR latex, along with polypropylene fibers are used in this study to improve the impact resistance. Along with fibers and polymers, the effect of the size of aggregates was also investigated. In total, 12 mixes were prepared and tested against the drop weight test and the Charpy impact test. Other than this, mechanical characterization was also carried out for all the 12 concrete mixes. Three dosages of SBR latex, i.e., 0%, 4%, and 8% by weight of cement, were used along with three aggregates sizes, 19 mm down, 10 mm down, and 4.75 mm down. The quantity of polypropylene fibers was kept equal to 0.5% in all mixes. In addition to these, three control samples were also prepared for comparison. The mix design was performed to achieve a normal-strength concrete. For this purpose, a concrete mix of 1:1.5:3 was used with a water to a cement ratio of 0.4 to achieve a normal-strength concrete. The experimental study concluded that the addition of SBR latex improves the impact resistance of concrete. Furthermore, an increase in impact resistance was also observed for a larger aggregate size. The use of fibers and SBR latex is encouraged due to their positive results and the fact that they provide an economical solution for catering to impact strains. The study concludes that 4% SBR latex and 0.5% fibers with a larger aggregate size improve the resistance against impact loads.

Environment-friendly concrete is gaining popularity these days because it consumes less energy and causes less damage to the environment. Rapid increases in the population and demand for construction throughout the world lead to a significant deterioration or reduction in natural resources. Meanwhile, construction waste continues to grow at a high rate as older buildings are destroyed and demolished. As a result, the use of recycled materials may contribute to improving the quality of life and preventing environmental damage. Additionally, the application of recycled coarse aggregate (RCA) in concrete is essential for minimizing environmental issues. The compressive strength (CS) and splitting tensile strength (STS) of concrete containing RCA are predicted in this article using decision tree (DT) and AdaBoost machine learning (ML) techniques. A total of 344 data points with nine input variables (water, cement, fine aggregate, natural coarse aggregate, RCA, superplasticizers, water absorption of RCA and maximum size of RCA, density of RCA) were used to run the models. The data was validated using k-fold cross-validation and the coefficient correlation coefficient (R2), mean square error (MSE), mean absolute error (MAE), and root mean square error values (RMSE). However, the model’s performance was assessed using statistical checks. Additionally, sensitivity analysis was used to determine the impact of each variable on the forecasting of mechanical properties.