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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.

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.

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.

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.

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.

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.

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.

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.

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 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 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 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.

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.

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.

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.

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.

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.

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.

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.

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.

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 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.

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.

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.

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.

The utilization of waste material, such as fly ash, in the concrete industry will provide a valuable alternative solution for creating an eco-friendly environment. However, experimental work is time-consuming; employing soft machine learning techniques can accelerate the process of forecasting the strength properties of concrete. Ensemble machine learning modeling using Python Jupyter Notebook was employed in the forecasting of compressive strength (CS) of high-performance concrete. Multilayer perceptron neuron network (MLPNN) and decision tree (DT) were used as individual learning which then ensembled with bagging and boosting to provide strong correlations. Random forest (RF) and gradient boosting regression (GBR) were also used for prediction. A total of 471 data points with input parameters (e.g., cement, fine aggregate, coarse aggregate, superplasticizer, water, days, and fly ash), and an output parameter of compressive strength (CS), were retrieved to train and test the individual learners. Cross-validation with K-fold and statistical error (i.e., MAE, MSE, RMSE, and RMSLE) analysis was applied to check the accuracy of all models. All models showed the best correlation with an ensemble model rather than an individual one. DT with AdaBoost and random forest gave a strong correlation of R2 = 0.89 with fewer errors. Cross-validation results revealed a good response with an error of less than 10 MPa. Thus, ensemble modeling not only trains the data by employing several weak learners but also produces a robust correlation that can then be used to model and predict the mechanical performance of concrete.

Reactive powder concrete (RPC) is used in the beam-column joint region in two out of four frames. Finite element modeling of all specimens is developed by using ABAQUS software. Displacement controlled analysis is used rather than load control analysis to obtain the actual response of the structure. The prepared models were verified by using experimental results. The results showed that using RPC in the joint region increased the overall strength of the structure by more than 10%. Moreover, it also helped in controlling the crack width. Furthermore, using RPC in the joint region increased the ductility of the structures. Comparisons were made by varying the size of the mesh and viscosity parameter values. It was found that by increasing the mesh size and viscosity parameter value, analysis time and the number of steps during analysis were reduced. This study provides a new modeling approach using RPC beam-column joint to predict the behavior and response of structures and to improve the shear strength deformation against different structural loading.

This experimental study presents concrete-filled double-skin tubular columns and demonstrates their expected advantages. These columns consist of an outer steel tube, an inner steel tube, and concrete sandwiched between two tubes. The influence of the outer-to-inner tube dimension ratio, outer tube to thickness ratio, and type of inner tube material (steel, PVC pipe) on the ultimate axial capacity of concrete-filled double-skin tubular columns is studied. It is found that the yield strength of the inner tube does not significantly affect the ultimate axial capacity of concrete-filled double-skin tubular composites. With the replacement of the inner tube of steel with a PVC pipe, on average, less than 10% strength is reduced, irrespective of size and dimensions of the steel tube. Hence, the cost of a project can be reduced by replacing inner steel tubes with a PVC pipes. Finally, the experimental results are compared with the existing design methods presented in AISC 360-16 (2016), GB51367 (2019), and EC4 (2004). It is found from the comparison that GB51367 (2019) gives better results, followed by AISC (2016) and EC4 (2004).

This study presents the performance of quarry rock dust (QRD) incorporated fly ash (FA) and slag (SG) based geopolymer concretes (QFS-GPC) exposed to ambient and elevated temperatures. A total of five QFS-GPC mix types were prepared. The quantity of FA (50%) was kept constant in all the mixes, and SG was replaced by 5%, 10%, 15%, and 20% of QRD. The fresh, hardened properties of the QFS-GPC mixes, viz., workability, compressive strength, splitting tensile strength, and flexural strengths, and XRD for identification of reaction phases were evaluated. The prepared mixes were also heated up to 800 °C to evaluate the residual compressive strength and weight loss. The workability of the QFS-GPC mixes was observed to be reduced by increasing the dosage (0 to 20%) of QRD. Superplasticizer (SP) was used to maintain the medium standard of workability. The compressive, tensile, and flexural strengths were increased by replacing SG with QRD up to 15%, whereas a further higher dosage (20%) of QRD reduced the mechanical strengths of the QFS-GPC mixes. The strength of the QFS-GPC specimens, heated to elevated temperatures up to 800 °C, was reduced persistently with the increased contents of QRD from 0 to 20%. It was concluded from the study that QFS-GPC can be used to achieve 30 MPa strength of concrete.

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.

In this paper, a seismic hazard assessment (SHA) of the Shigo Kas hydropower project has been performed by deterministic and probabilistic approaches. The previously developed MATLAB-based code has been used for deterministic SHA, incorporating local site effects through deep soil analysis. On the other hand, for probabilistic SHA, CRISIS 2007 has been used through diffuse areal source zones. The latest updated earthquake instrumental and historical catalogs have been developed. Based on the recommendations of the International Commission on Large Dams, peak ground acceleration (PGA) values for the maximum credible earthquake (MCE), safety evaluation earthquake (SEE), design basis earthquake (DBE) and operating basis earthquake (OBE) have been assessed, which are 0.50 g, 0.68 g, 0.35 g and 0.24 g, respectively, at the intake location, and 0.50 g, 0.61 g, 0.30 g and 0.22 g, respectively, at the powerhouse location. Hazard maps have been developed for scenario-based earthquakes (MCE) and for the peak ground acceleration of 145-, 475- and 2500-year return periods. The de-aggregation process has evaluated the combined effects of magnitude and distance. At a distance of 30 to 70 km from the earthquake source, earthquakes of magnitude 5 Mw to 5.6 Mw and 5.9 Mw to 6.5 Mw are more hazardous for the current project.

To minimize the environmental risks and for sustainable development, the utilization of recycled aggregate (RA) is gaining popularity all over the world. The use of recycled coarse aggregate (RCA) in concrete is an effective way to minimize environmental pollution. RCA does not gain more attraction because of the availability of adhered mortar on its surface, which poses a harmful effect on the properties of concrete. However, a suitable mix design for RCA enables it to reach the targeted strength and be applicable for a wide range of construction projects. The targeted strength achievement from the proposed mix design at a laboratory is also a time-consuming task, which may cause a delay in the construction work. To overcome this flaw, the application of supervised machine learning (ML) algorithms, gene expression programming (GEP), and artificial neural network (ANN) was employed in this study to predict the compressive strength of RCA-based concrete. The linear coefficient correlation (R2), mean absolute error (MAE), mean square error (MSE), and root mean square error (RMSE) were evaluated to investigate the performance of the models. The k-fold cross-validation method was also adopted for the confirmation of the model’s performance. In comparison, the GEP model was more effective in terms of prediction by giving a higher correlation (R2) value of 0.95 as compared to ANN, which gave a value of R2 equal to 0.92. In addition, a sensitivity analysis was conducted to know about the contribution level of each parameter used to run the models. Moreover, the increment in data points and the use of other supervised ML approaches like boosting, gradient boosting, and bagging to forecast the compressive strength, would give a better response.

Numerous research studies have been conducted to improve the weak properties of recycled aggregate as a construction material over the last few decades. In two-stage concrete (TSC), coarse aggregates are placed in formwork, and then grout is injected with high pressure to fill up the voids between the coarse aggregates. In this experimental research, TSC was made with 100% recycled coarse aggregate (RCA). Ten percent and twenty percent bagasse ash was used as a fractional substitution of cement along with the RCA. Conventional concrete with 100% natural coarse aggregate (NCA) and 100% RCA was made to determine compressive strength only. Compressive strength reduction in the TSC was 14.36% when 100% RCA was used. Tensile strength in the TSC decreased when 100% RCA was used. The increase in compressive strength was 8.47% when 20% bagasse ash was used compared to the TSC mix that had 100% RCA. The compressive strength of the TSC at 250 °C was also determined to find the reduction in strength at high temperature. Moreover, the compressive and tensile strength of the TSC that had RCA was improved by the addition of bagasse ash.

The present research aims at evaluating the mechanical performance of untreated and treated crumb rubber concrete (CRC). The study was also conducted to reduce the loss in mechanical properties of CRC. In this study, sand was replaced with crumb rubber (CR) with 0%, 5%, 10%, 15%, and 20% by volume. CR was treated with NaOH, lime, and common detergent for 24 h. Furthermore, water treatment was also carried out. All these treatments were done to enhance the mechanical properties of concrete that are affected by adding CR. The properties that were evaluated are compressive strength, indirect tensile strength, unit weight, ultrasonic pulse velocity, and water absorption. Compressive strength was assessed after 7 and 28 days of curing. The mechanical properties were decreased by increasing the percentage of the CR. The properties were improved after the treatment of CR. Lime treatment was found to be the best treatment of all four treatments followed by NaOH treatment and water treatment. Detergent treatment was found to be the worse treatment of all four methods of treatment. Despite increasing the strength it contributed to strength loss.

Cement is among the major contributors to the global carbon dioxide emissions. Thus, sustainable alternatives to the conventional cement are essential for producing greener concrete structures. Rice husk ash has shown promising characteristics to be a sustainable option for further research and investigation. Since the experimental work required for assessing its properties is both time consuming and complex, machine learning can be used to successfully predict the properties of concrete containing rice husk ash. A total of 192 data points are used in this study to assess the compressive strength of rice husk ash blended concrete. Input parameters include age, amount of cement, rice husk ash, super plasticizer, water, and aggregates. Four soft computing and machine learning methods, i.e., artificial neural networks (ANN), adaptive neuro-fuzzy inference system (ANFIS), multiple nonlinear regression (NLR), and linear regression are employed in this research. Sensitivity analysis, parametric analysis, and correlation factor (R2) are used to evaluate the obtained results. The ANN and ANFIS outperformed other methods.

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

The Pakistan construction industry recorded a high growth rate of 9.05% in 2016–2017. However, it fails to complete the projects within the specified cost, time and quality due to the use of traditional management techniques. This can be easily overcome by adopting and implementing new technologies like Building Information Modeling (BIM). In this study, a questionnaire survey was performed to find the present state of BIM in Pakistan and highlight the barriers in its successful adoption. In addition, Interpretive Structure Modeling (ISM) and Cross-Impact Matrix Multiplication Applied to Classification (MICMAC) analysis were used to identify interrelationships among these barriers. Out of 104 responses received, 63% of Architecture, Engineering and Construction (AEC) professionals were aware of BIM and among them, only 17% had utilized BIM in their projects, which is a very low percentage. The adoption of BIM in Pakistan is only at 11%. The respondents acknowledged that BIM helps in minimizing the cost and time of a project up to 57%. This study concluded that BIM is more economical and efficient than other management techniques. Moreover, this research also identifies the ISM and MICMAC analysis-based hierarchical model and three level strategy model that can facilitate the possible implementation. Solutions to common barriers around the globe are also proposed. Thus, by utilizing BIM, an economical, timely, properly managed project can be obtained in Pakistan and other developing construction markets all over the globe.

Supervised machine learning and its algorithm is an emerging trend for the prediction of mechanical properties of concrete. This study uses an ensemble random forest (RF) and gene expression programming (GEP) algorithm for the compressive strength prediction of high strength concrete. The parameters include cement content, coarse aggregate to fine aggregate ratio, water, and superplasticizer. Moreover, statistical analyses like MAE, RSE, and RRMSE are used to evaluate the performance of models. The RF ensemble model outbursts in performance as it uses a weak base learner decision tree and gives an adamant determination of coefficient R2 = 0.96 with fewer errors. The GEP algorithm depicts a good response in between actual values and prediction values with an empirical relation. An external statistical check is also applied on RF and GEP models to validate the variables with data points. Artificial neural networks (ANNs) and decision tree (DT) are also used on a given data sample and comparison is made with the aforementioned models. Permutation features using python are done on the variables to give an influential parameter. The machine learning algorithm reveals a strong correlation between targets and predicts with less statistical measures showing the accuracy of the entire model.