Comparison of Prediction Models Based on Machine Learning for the Compressive Strength Estimation of Recycled Aggregate Concrete

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

A Systematic Review of the Research Development on the Application of Machine Learning for Concrete

Research on the applications of new techniques such as machine learning is advancing rapidly. Machine learning methods are being employed to predict the characteristics of various kinds of concrete such as conventional concrete, recycled aggregate concrete, geopolymer concrete, fiber-reinforced concrete, etc. In this study, a scientometric-based review on machine learning applications for concrete was performed in order to evaluate the crucial characteristics of the literature. Typical review studies are limited in their capacity to link divergent portions of the literature systematically and precisely. Knowledge mapping, co-citation, and co-occurrence are among the most challenging aspects of innovative studies. The Scopus database was chosen for searching for and retrieving the data required to achieve the study’s aims. During the data analysis, the relevant sources of publications, relevant keywords, productive writers based on publications and citations, top articles based on citations received, and regions actively engaged in research into machine learning applications for concrete were identified. The citation, bibliographic, abstract, keyword, funding, and other data from 1367 relevant documents were retrieved and analyzed using the VOSviewer software tool. The application of machine learning in the construction sector will be advantageous in terms of economy, time-saving, and reduced requirement for effort. This study can aid researchers in building joint endeavors and exchanging innovative ideas and methods, due to the statistical and graphical portrayal of participating authors and countries

Mechanical Performance of High-Strength Sustainable Concrete under Fire Incorporating Locally Available Volcanic Ash in Central Harrat Rahat, Saudi Arabia

This study investigated the effect of elevated temperatures on the mechanical properties of high-strength sustainable concrete incorporating volcanic ash (VA). For comparison, control and reference concrete specimens with fly ash (FA) were also cast along with additional specimens of VA and FA containing electric arc furnace slag (EAFS). Before thermal exposure, initial tests were performed to evaluate the mechanical properties (compressive strength, tensile strength, and elastic modulus) of cylindrical concrete specimens with aging. Additionally, 91 day moist-cured concrete specimens, after measuring their initial weight and ultrasonic pulse velocity (UPV), were exposed up to 800 °C and cooled to air temperature. Subsequently, the weight loss, residual UPV, and mechanical properties of concrete were measured with respect to exposure temperature. For all concrete specimens, test results demonstrated a higher loss of weight, UPV, and other mechanical properties under exposure to higher elevated temperature. Moreover, all the results of concrete specimens incorporating VA were observed before and after exposure to elevated temperature as either comparable to or slightly better than those of control and reference concrete with FA. According to the experimental results, a correlation was developed between residual UPV and residual compressive strength (RCS), which can be used to assess the RCS of fire-damaged concrete (up to 800 °C) incorporating VA and EAFS

Cyclic Behavior of Different Connections in Precast Concrete Shear Walls: Experimental and Analytical Investigations

This study investigated the grouted sleeve splices and corrugated duct splices between shear walls and footing. In this regard, three shear walls were experimentally tested. One wall was cast monolithically with the foundation (RCWS), whereas two walls were precast. One wall was connected to the foundation using splice sleeves (PGWS), and another with corrugated duct splices (PCWS). All the walls were tested under reverse cyclic loading and a constant axial load. It was observed that the performance of specimen PGWS was controlled by rocking, and a premature connection loss was observed at one of the grouted sleeve splices. The hysteretic performance of specimen PCWS was close to that of specimen RCWS, whereas extensive pinching was observed in the hysteretic response of specimen PGWS. The peak load, ductility, secant stiffness, and energy dissipation of specimens RCWS and PCWS were in good agreement, whereas the energy dissipated by specimen PGWS was considerably lower than the corresponding values of specimens RCWS and PCWS. Nonlinear fiber-based modeling in OpenSees was performed using SFI-MVLEM elements. The predicted hysteretic response of the OpenSees model was in close agreement with the experimental response

Influence of Fineness of Wheat Straw Ash on Autogenous Shrinkage and Mechanical Properties of Green Concrete

This study investigates the effectiveness of an agricultural by-product wheat straw ash (WSA) as an internal curing agent in reducing the autogenous shrinkage of high-performance concrete (HPC). After incineration under different controlled time–temperature conditions, grinding and sieving were performed to obtain two different grades of fine WSA (F-WSA) and superfine WSA (SF-WSA). Subsequently, material characterization tests were carried out, followed by tests for mechanical properties and autogenous shrinkage potential of concrete incorporating 10% and 20% F-WSA and SF-WSA as a partial replacement of cement. The results demonstrated slightly higher compressive and tensile strength of concrete containing SF-WSA compared to control, whereas concrete with F-WSA demonstrated comparable strength results to that of the control concrete. Moreover, a significant reduction in 7 days’ autogenous shrinkage was observed in concrete containing 10% and 20% F-WSA by 42% and 25% compared to that of control concrete, respectively. This reduction in autogenous shrinkage increased further to 57% and 40% for concrete with 10% and 20% SF-WSA, respectively. The results of microstructural investigations on paste samples such as FTIR, TGA, and N2 adsorption analyses revealed a more refined and compact microstructure of paste samples with increasing fineness of WSA due to the formation of a more densified C-S-H phase. The improvement of the microstructure is attributable to the improved pozzolanic properties of SF-WSA compared with F-WSA

Pozzolanic Potential and Mechanical Performance of Wheat Straw Ash Incorporated Sustainable Concrete

The pozzolanic potential, mechanical strength, and stress-strain behavior of a locally available wheat straw ash (WSA) as a partial substitute of cement was evaluated in this study. Various samples of a locally available wheat straw were burnt to ashes at three distinct temperatures and characterized through X-ray powder diffraction and energy dispersive X-ray spectroscopy. The WSA obtained from burning at 550 °C was found highly amorphous and possessed suitable chemical composition to be used as pozzolanic material. The burned WSA was grounded to achieve the desired fineness and mortar cubes and concrete cylinders were cast by substituting 15%, 20%, 25%, and 30% cement with it. The strength of mortar and concrete decreased with increasing amounts of WSA except for those containing 15% WSA, where it slightly increased than the respective control samples at later ages, i.e., 28 and 91 days. Despite reduced strength at high replacements (20%, 25%, and 30%), the strength activity index values met ASTM C618 requirements for pozzolanic materials. Moreover, the compressive strength of concrete containing 20% WSA exceeded to that of control concrete at 91 days. The stress-strain relation of concrete containing 15% to 20% WSA also showed comparable stiffness and toughness to those of control samples at all ages. Particularly, the concrete containing 15% WSA showed significant improvement of strength, stiffness, toughness, and ductility at 91 days. Lastly, the results of mechanical strengths and pozzolanic reactivity were successfully validated indirectly by measuring the porosity of mortars and thermo-gravimetric analysis of cement pastes, respectively. Based on current findings and their validation, WSA can be used as a substitute of cement up to 20% in the production of sustainable normal strength concrete for their application in common domestic building projects

Nano-Silica-Modified Concrete: A Bibliographic Analysis and Comprehensive Review of Material Properties

Several review studies have been performed on nano-silica-modified concrete, but this study adopted a new method based on scientometric analysis for the keywords’ assessment in the current research area. A scientometric analysis can deal with vast bibliometric data using a software tool to evaluate the diverse features of the literature. Typical review studies are limited in their ability to comprehensively and accurately link divergent areas of the literature. Based on the analysis of keywords, this study highlighted and described the most significant segments in the research of nano-silica-modified concrete. The challenges associated with using nano-silica were identified, and future research is directed. Moreover, prediction models were developed using data from the literature for the strength estimation of nano-silica-modified concrete. It was noted that the application of nano-silica in cement-based composites is beneficial when used up to an optimal dosage of 2–3% due to high pozzolanic reactivity and a filler effect, whereas a higher dosage of nano-silica has a detrimental influence due to the increased porosity and microcracking caused by the agglomeration of nano-silica particles. The mechanical strength might enhance by 20–25% when NS is incorporated in the optimal amount. The prediction models developed for predicting the strength of nano-silica-modified concrete exhibited good agreement with experimental data due to lower error values. This type of analysis may be used to estimate the essential properties of a material, therefore saving time and money on experimental tests. It is recommended to investigate cost-effective methods for the dispersion of nano-silica in higher concentrations in cement mixes; further in-depth studies are required to develop more accurate prediction models to predict nano-silica-modified concrete properties

A Comprehensive Review of Types, Properties, Treatment Methods and Application of Plant Fibers in Construction and Building Materials

Sustainable development involves the usage of alternative sustainable materials in order to sustain the excessive depletion of natural resources. Plant fibers, as a “green” material, are progressively gaining the attention of various researchers in the field of construction for their potential use in composites for stepping towards sustainable development. This study aims to provide a scientometric review of the summarized background of plant fibers and their applications as construction and building materials. Studies from the past two decades are summarized. Quantitative assessment of research progress is made by using connections and maps between bibliometric data that are compiled for the analysis of plant fibers using Scopus. Data refinement techniques are also used. Plant fibers are potentially used to enhance the mechanical properties of a composite. It is revealed from the literature that plant-fiber-reinforced composites have comparable properties in comparison to composites reinforced with artificial/steel fibers for civil engineering applications, such as construction materials, bridge piers, canal linings, soil reinforcement, pavements, acoustic treatment, insulation materials, etc. However, the biodegradable nature of plant fibers is still a hindrance to their application as a structural material. For this purpose, different surface and chemical treatment methods have been proposed in past studies to improve their durability. It can be surmised from the gathered data that the compressive and flexural strengths of plant-fiber-reinforced cementitious composites are increased by up to 43% and 67%, respectively, with respect to a reference composite. In the literature, alkaline treatment has been reported as an effective and economical method for treating plant fibers. Environmental degradation due to excessive consumption of natural resources and fossil fuels for the construction industry, along with the burning of waste plant fibers, can be reduced by incorporating said fibers in cementitious composites to reduce landfill pollution and, ultimately, achieve sustainable development

Biochar Produced from Saudi Agriculture Waste as a Cement Additive for Improved Mechanical and Durability Properties—SWOT Analysis and Techno-Economic Assessment

The Kingdom of Saudi Arabia generates an enormous amount of date palm waste, causing severe environmental concerns. Green and strong concrete is increasingly demanded due to low carbon footprints and better performance. In this research work, biochar derived from locally available agriculture waste (date palm fronds) was used as an additive to produce high-strength and durable concrete. Mechanical properties such as compressive and flexural strength were evaluated at 7, 14, and 28 days for control and all other mixes containing biochar. In addition, the durability properties of the concrete samples for the mixes were investigated by performing electric resistivity and ultra-sonic pulse velocity testing. Finally, a SWOT (strengths, weaknesses, opportunities, and threats) analysis was carried out to make strategic decisions about biochar’s use in concrete. The results demonstrated that the compressive strength of concrete increased to 28–29% with the addition of 0.75–1.5 wt% of biochar. Biochar-concrete containing 0.75 wt% of biochar showed 16% higher flexural strength than the control specimen. The high ultrasonic pulse velocity (UPV) values (>7.79 km/s) and low electrical resistivity (<22.4 kΩ-cm) of biochar-based concrete confirm that the addition of biochar resulted in high-quality concrete free from internal flaws, cracks, and better structural integrity. SWOT analysis indicated that biochar-based concrete possessed improved performance than ordinary concrete, is suitable for extreme environments, and has opportunities for circular economy and applications in various construction designs. However, cost and technical shortcomings in biochar production and biochar-concrete mix design are still challenging

Optimizing compressive strength prediction models for rice husk ash concrete with evolutionary machine intelligence techniques

This research intended to increase the understanding of using modern machine intelligence techniques, including multi-expression programming (MEP) and gene expression programming (GEP), for the compressive strength (CS) prediction of rice husk ash (RHA) concrete. In addition, SHapley Additive exExplanations (SHAP) analysis was made to study the impact and interaction of raw materials on the CS of RHA concrete. A comprehensive database of 192 points with six inputs (cement, specimen age, RHA, superplasticizer, water, and fine aggregate) was used for developing prediction models. This research determined that both GEP and MEP models for the CS prediction of RHA concrete yielded reliable results, which were in close agreement with the real CS. Comparing the performance of both GEP and MEP models, it was noted that MEP, with an R2 of 0.89, outperformed the GEP model having an R2 of 0.83. Additionally, SHAP analysis indicated that specimen age was the most vital measure, followed by cement, which positively correlated with CS of RHA. The overall effect of RHA was found to be more positive, suggesting RHA utilization in the optimal range of 75–100 kg/m3 in the RHA concrete mix. The use of prediction models and SHAP analysis will help the building industry assess material properties and raw material effects faster and more economical