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In civil engineering, ultra-high-strength concrete (UHSC) is a useful and efficient building material. To save money and time in the construction sector, soft computing approaches have been used to estimate concrete properties. As a result, the current work used sophisticated soft computing techniques to estimate the compressive strength of UHSC. In this study, XGBoost, AdaBoost, and Bagging were the employed soft computing techniques. The variables taken into account included cement content, fly ash, silica fume and silicate content, sand and water content, superplasticizer content, steel fiber, steel fiber aspect ratio, and curing time. The algorithm performance was evaluated using statistical metrics, such as the mean absolute error (MAE), root mean square error (RMSE), and coefficient of determination (R2). The model’s performance was then evaluated statistically. The XGBoost soft computing technique, with a higher R2 (0.90) and low errors, was more accurate than the other algorithms, which had a lower R2. The compressive strength of UHSC can be predicted using the XGBoost soft computing technique. The SHapley Additive exPlanations (SHAP) analysis showed that curing time had the highest positive influence on UHSC compressive strength. Thus, scholars will be able to quickly and effectively determine the compressive strength of UHSC using this study’s findings.

In the past few years, there has been a notable interest in the application of machine learning methods to enhance energy efficiency in the smart building industry. The paper discusses the use of machine learning in smart buildings to improve energy efficiency by analyzing data on energy usage, occupancy patterns, and environmental conditions. The study focuses on implementing and evaluating energy consumption prediction models using algorithms like long short-term memory (LSTM), random forest, and gradient boosting regressor. Real-life case studies on educational buildings are conducted to assess the practical applicability of these models. The data is rigorously analyzed and preprocessed, and performance metrics such as root mean square error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE) are used to compare the effectiveness of the algorithms. The results highlight the importance of tailoring predictive models to the specific characteristics of each building’s energy consumption.

Due to vincristine sulfate’s (VCR sulfate) toxicity and non-specific targeting, which might adversely damage healthy cells, its clinical application is restricted. In this study, we loaded VCR sulfate on exosomes generated from mesenchymal stem cells (MSCs) to enhance its targeted distribution. Exosomes are able to deliver molecules to specific cells and tissues and have therapeutic potential. In this study, we isolated exosomes from MSCs, and using probe-sonication approach loaded them with VCR sulfate. Using SRB assay, the cytotoxicity of VCR sulfate-Exo was assessed in T47D breast cancer cells, and the results were contrasted with those of free VCR sulfate. Then We labeled markers (CD44+/CD24−) in the cell line to assess the targeting effectiveness of VCR sulfate-Exo using flow cytometry. Our results showed that the cytotoxicity of VCR sulfate-Exo was nearly the same as that of VCR sulfate. Flow cytometry analysis revealed that VRC sulfate-Exo was more effectively targeted to MSCs than free VCR sulfate. Our study shows that loading VCR sulfate to MSCs-derived exosomes can improve their targeted delivery and lessen their side effects. Additional research is required to determine VCR sulfate-Exo’s in vivo effectiveness and safety and improve the loading and delivery strategies.

Plastic consumption increases with the growing population worldwide and results in increased quantities of plastic waste. There are various plastic waste management strategies; however, the present management progress is not sustainable, and plastic waste dumping in landfills is still the most commonly employed strategy. Being nonbiodegradable, plastic waste dumping in landfills creates several environmental and human health problems. Numerous research studies have been conducted recently to determine safe and ecologically beneficial methods of plastic waste handling. This article performed a bibliographic analysis of the available literature on plastic waste management using a computational approach. The highly used keywords, most frequently cited papers and authors, actively participating countries, and sources of publications were analyzed during the bibliographic analysis. In addition, the various plastic waste management strategies and their environmental benefits have been discussed. It has been concluded that among the six plastic waste management techniques (landfills, recycling, pyrolysis, liquefaction, road construction and tar, and concrete production), road construction and tar and concrete production are the two most effective strategies. This is due to significant benefits, such as ease of localization, decreased greenhouse gas emissions, and increased durability and sustainability of manufactured materials, structures, and roadways. Conversely, using landfills is the most undesirable strategy because of the associated environmental and human health concerns. Recycling has equal benefits and drawbacks. In comparison, pyrolysis and liquefaction are favorable due to the production of char and fuel, but high energy requirements limit their benefits. Hence, the use of plastic waste for construction applications is recommended.

Utilizing scrap tire rubber by incorporating it into concrete is a valuable option. Many researchers are interested in using rubber tire waste in concrete. The possible uses of rubber tires in concrete, however, are dispersed and unclear. Therefore, a compressive analysis is necessary to identify the benefits and drawbacks of rubber tires for concrete performance. For examination, the important areas of concrete freshness, durability, and strength properties were considered. Additionally, several treatments and a microstructure investigation were included. Although it has much promise, there are certain obstacles that prevent it from being used as an aggregate in large numbers, such as the rubber’s weak structural strength and poor binding performance with the cement matrix. Rubber, however, exhibits mechanical strength comparable to reference concrete up to 20%. The evaluation also emphasizes the need for new research to advance rubberized concrete for future generations.

Prior studies in the literature show promising results regarding the improvements in strength and durability of concrete upon incorporation of glass fibers into concrete formulations. However, the knowledge regarding glass fiber usage in concrete is scattered. Moreover, this makes it challenging to understand the behavior of glass fiber-reinforced concrete. Therefore, a detailed review is required on glass fiber-reinforced concrete. This paper provides a compressive analysis of glass fiber-reinforced composites. All-important properties of concrete such as flowability, compressive, flexural, tensile strength and modulus of elasticity were presented in this review article. Furthermore, durability aspects such as chloride ion penetration, water absorption, ultrasonic pulse velocity (UPV) and acid resistance were also considered. Finally, the bond strength of the fiber and cement paste was examined via scanning electron microscopy. Results indicate that glass fibers improved concrete’s strength and durability but decreased the concrete’s flowability. Higher glass fiber doses slightly decreased the mechanical performance of concrete due to lack of workability. The typical optimum dose is recommended at 2.0%. However, a higher dose of plasticizer was recommended for a higher dose of glass fiber (beyond 2.0%). The review also identifies research gaps that should be addressed in future studies.

The Architecture, Engineering, Construction and Operations (AECO) industry is generally slow in adopting emerging technologies, and such hesitance invariably restricts performance improvements. A plethora of studies have focused on the barriers, Critical Success Factors (CSFs), lifecycle and drivers independently, but none have explored the impact of BIM drivers and awareness on the project lifecycle. This study empirically explored the impact of BIM drivers and awareness on the project lifecycle using Structural Equation Modelling (SEM). Initially, a conceptual model was developed from an extensive literature review. Thereafter, the model was tested using primary questionnaire data obtained from 90 construction professionals in Lagos, Nigeria. Emergent findings indicate that Building Information Modelling (BIM) drivers have a high impact on BIM awareness at the operation stage of the project lifecycle. The SEM model has an average R2 value of 23% which is moderate. Consequently, this research contributes to the existing body of knowledge by providing invaluable insight into the impact of BIM drivers on BIM awareness in the project lifecycle. Knowledge acquired will help industry stakeholders and government to develop appropriate policies to increase BIM uptake within contemporary practice.

The push for sustainability in the construction sector has demanded the use of increasingly renewable resources. These natural fibers are biodegradable and non-toxic, and their mechanical capabilities are superior to those of synthetic fibers in terms of strength and durability. A lot of research recommends coconut fibers as an alternative to synthetic fibers. However, the knowledge is scattered, and no one can easily judge the suitability of coconut fibers in concrete. This paper presents a summary of research progress on coconut fiber (natural fibers) reinforced concrete. The effects of coconut fibers on the properties of concrete are reviewed. Factors affecting the fresh, hardened, and durability properties of concrete reinforced with coconut fiber are discussed. Results indicate that coconut fiber improved the mechanical performance of concrete due to crack prevention, similar to the synthetic fibers but decreased the flowability of concrete. However, coconut fibers improved flexure strength more effectively than compressive strength. Furthermore, improvement in some durability performance was also observed, but less information is available in this regard. Moreover, the optimum dose is an important parameter for high-strength concrete. The majority of researchers indicate that 3.0% coconut fiber is the optimum dose. The overall study demonstrates that coconut fibers have the creditability to be used in concrete instead of synthetic fibers.

The need for low-cost raw materials is driven by the fact that iron ore tailings, a prevalent kind of hazardous solid waste, have created major environmental issues. Although many studies have focused on using iron ore tailing (IOT) in concrete and have reported positive results, readers may find it difficult to accurately assess the behaviors of IOT in concrete due to the scattered nature of the information. Therefore, a comprehensive assessment of IOT in concrete is necessary. This paper thoroughly reviews the characteristics of concrete that contains IOT such as fresh properties, mechanical properties and durability at different age of curing. The outcome of this review indicates that by using IOT, concrete’s mechanical properties and durability improved, but its flowability decreased. Compressive strength of concrete with 20% substitution of IOT is 14% more than reference concrete. Furthermore, up to 40% substitution of IOT produces concrete that has sufficient flowability and compactability. Scan electronic microscopy results indicate a weak interfacial transition zone (ITZ). The optimum IOT dosage is important since a greater dose may decrease the strength properties and durability owing to a lack of fluidity. Depending on the physical and chemical composition of IOT, the average value of optimum percentages ranges from 30 to 40%. The assessment also recommends areas of unsolved research for future investigations.

Research has focused on creating new methodologies such as supervised machine learning algorithms that can easily calculate the mechanical properties of fiber-reinforced concrete. This research aims to forecast the flexural strength (FS) of steel fiber-reinforced concrete (SFRC) using computational approaches essential for quick and cost-effective analysis. For this purpose, the SFRC flexural data were collected from literature reviews to create a database. Three ensembled models, i.e., Gradient Boosting (GB), Random Forest (RF), and Extreme Gradient Boosting (XGB) of machine learning techniques, were considered to predict the 28-day flexural strength of steel fiber-reinforced concrete. The efficiency of each method was assessed using the coefficient of determination (R2), statistical evaluation, and k-fold cross-validation. A sensitivity approach was also used to analyze the impact of factors on predicting results. The analysis showed that the GB and RF models performed well, and the XGB approach was in the acceptable range. Gradient Boosting showed the highest precision with an R2 of 0.96, compared to Random Forest (RF) and Extreme Gradient Boosting (XGB), which had R2 values of 0.94 and 0.86, respectively. Moreover, statistical and k-fold cross-validation studies confirmed that Gradient Boosting was the best performer, followed by Random Forest (RF), based on reduced error levels. The Extreme Gradient Boosting model performance was satisfactory. These ensemble machine learning algorithms can benefit the construction sector by providing fast and better analysis of material properties, especially for fiber-reinforced concrete.