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The estimation of concrete characteristics through artificial intelligence techniques is come out to be an effective way in the construction sector in terms of time and cost conservation. The manufacturing of Ultra-High-Performance Concrete (UHPC) is based on combining numerous ingredients, resulting in a very complex composite in fresh and hardened form. The more ingredients, along with more possible combinations, properties and relative mix proportioning, results in difficult prediction of UHPC behavior. The main aim of this research is the development of Machine Learning (ML) models to predict UHPC flowability and compressive strength. Accordingly, sophisticated and effective artificial intelligence approaches are employed in the current study. For this purpose, an individual ML model named Decision Tree (DT) and ensembled ML algorithms called Bootstrap Aggregating (BA) and Gradient Boosting (GB) are applied. Statistical analyses like; Determination Coefficient (R 2 ), Root Mean Square Error (RMSE), and Mean Absolute Error (MAE) are also employed to evaluate algorithms’ performance. It is concluded that the GB approach appropriately forecasts the UHPC flowability and compressive strength. The higher R 2 value, i.e., 0.94 and 0.95 for compressive and flowability, respectively, of the DT technique and lesser error values, have higher precision than other considered algorithms with lower R 2 values. SHAP analysis reveals that limestone powder content and curing time have the highest SHAP values for UHPC flowability and compressive strength, respectively. The outcomes of this research study would benefit the scholars of the construction industry to quickly and effectively determine the flowability and compressive strength of UHPC.

Accumulating vast amounts of pollutants drives modern civilization toward sustainable development. Construction waste is one of the prominent issues impeding progress toward net-zero. Pollutants must be utilized in constructing civil engineering structures for a green ecosystem. On the other hand, large-scale production of industrial steel fibers (ISFs) causes significant damage to the goal of a sustainable environment. Recycled steel fibers (RSFs) from waste tires have been suggested to replace ISFs. This research critically examines RSF’s application in the mechanical properties’ improvement of concrete and mortar. A statistical analysis of dimensional parameters of RSFs, their properties, and their use in manufacturing various cement-based composites are given. Furthermore, comparative assessments are carried out among the improvements in compressive, split tensile, and flexural strengths of plain and RSF-incorporated concrete and mortar. In addition, the optimum contents of RSF for each strength property are also discussed. The influence of RSFs parameters on various strength properties of concrete and mortars is discussed. The possible applications of RSF for various civil engineering structures are reviewed. The limitations and errors noticed in previous review papers are also outlined. It is found that the maximum enhancement in compressive strength (CS), split tensile strength (STS), and flexure strength (FS) are 78%, 149%, and 157%, respectively, with the addition of RSF into concrete. RSF increased cement mortars’ CS, STS, and FS by 46%, 50.6%, and 69%, respectively. The current study encourages the building sector to use RSFs for sustainable concrete.

This study thoroughly reviews the recent design methods for ultra-high-performance concrete (UHPC) with agricultural waste. The goal is to identify UHPC composites that meets environmental sustainability requirements while fulfilling workability, durability, and mechanical properties. The capacity of typical review studies is limited in bridging the various literature aspects systematically. The article includes comparative analyses identifying these methods’ intrinsic connections and current trends. The analysis indicates that 71% of documents on incorporating agricultural waste into UHPC are in the “Engineering” and “Materials Science” disciplines, with 69% being journal articles, and 27% conference documents. Significant research keywords involve “Ultra-High-Performance Concrete,” “Cements,” “Sustainable Development,” and “Agricultural Wastes,” highlighting the extensive exploration of agricultural waste in UHPC. It has been discovered that agricultural waste can replace silica fume in UHPC, improving strength and durability by reducing pore volume and enhancing microstructure. Substituting 5–30% of cement with rice husk ash significantly boosts compressive strength, enhancing cement hydration, pore structure, and pozzolanic reaction, offering substantial environmental benefits and supporting the construction industry’s contribution to low-carbon sustainable development. This article provides guidance and recommendations for developing sustainable UHPC to meet diverse design specifications, promoting environmentally friendly construction practices.

Infections caused by Salmonella enterica, a Gram-negative facultative anaerobic bacteria belonging to the family of Enterobacteriaceae, are major threats to the health of humans and animals. The recent availability of complete genome data of pathogenic strains of the S. enterica gives new avenues for the identification of drug targets and drug candidates. We have used the genomic and metabolic pathway data to identify pathways and proteins essential to the pathogen and absent from the host. We took the whole proteome sequence data of 42 strains of S. enterica and Homo sapiens along with KEGG-annotated metabolic pathway data, clustered proteins sequences using CD-HIT, identified essential genes using DEG database and discarded S. enterica homologs of human proteins in unique metabolic pathways (UMPs) and characterized hypothetical proteins with SVM-prot and InterProScan. Through this core proteomic analysis we have identified enzymes essential to the pathogen. The identification of 73 enzymes common in 42 strains of S. enterica is the real strength of the current study. We proposed all 73 unexplored enzymes as potential drug targets against the infections caused by the S. enterica. The study is comprehensive around S. enterica and simultaneously considered every possible pathogenic strain of S. enterica. This comprehensiveness turned the current study significant since, to the best of our knowledge it is the first subtractive core proteomic analysis of the unique metabolic pathways applied to any pathogen for the identification of drug targets. We applied extensive computational methods to shortlist few potential drug targets considering the druggability criteria e.g. Non-homologous to the human host, essential to the pathogen and playing significant role in essential metabolic pathways of the pathogen (i.e. S. enterica). In the current study, the subtractive proteomics through a novel approach was applied i.e. by considering only proteins of the unique metabolic pathways of the pathogens and mining the proteomic data of all completely sequenced strains of the pathogen, thus improving the quality and application of the results. We believe that the sharing of the knowledge from this study would eventually lead to bring about novel and unique therapeutic regimens against the infections caused by the S. enterica.

This study investigates the compression behavior of circular concrete columns confined with basalt fiber-reinforced polymer (BFRP) stirrups, comparing their performance with traditional steel-reinforced columns. The objective of the study is to assess the impact of the BFRP transverse reinforcements and concrete mix design (CMD) codes on the compressive strength (CS), energy accumulation (G ), fracture energy (G ), and ductility of RC cylinders. A total of 81 reinforced concrete (RC) cylinders were prepared using three distinct CMD codes and tested under axial compression. The specimens were confined with 6 mm and 8 mm BFRP and steel spirals/ties at varying rib spacings (45 mm, 60 mm, and 90 mm). The experimental results revealed that closer BFRP spiral/tie spacings (45 mm) significantly enhanced the CS by 7–15 % and G by up to 59 % of the columns due to improved confinement effect, compared to those with larger spacings (60 mm and 90 mm). BFRP ties demonstrated superior performance in terms of CS, G , and G compared to BFRP spirals, particularly at moderate and larger spacings. Finite element model (FEM) simulations validated the experimental results with less than 8 % deviation and demonstrate a high degree of correlation between predicted and observed failure behaviors. The study suggests that BFRP spirals/ties reinforcements with optimal spacing can effectively replace steel in structural applications, offering comparable performance in strength, ductility, and energy absorption. These findings encourage the use of BFRP-based reinforcements in durable, lightweight, and eco-friendly concrete constructions.

This research investigates the impact of coarse aggregate (CA) type, shape, and specimen size on the compressive behavior of concrete, aiming to better understand how these factors affect its mechanical performance. Eight concrete mixtures were designed according to four different concrete mix design (CMD) codes using two types of coarse aggregates: crushed basalt and naturally rounded, both with a 15 mm size. A total of 96 concrete samples were tested to evaluate their failure mode, compressive strength (CS), energy accumulation (GA), and post-peak fracture energy (GF). The results show that concrete made with basalt CA offered significantly higher CS (by 7% to 40%), GA (by 34% to 57%), and GF (10% to 48%) compared to concrete made with natural CA across different CMD codes and specimen dimensions. Larger cylinders demonstrated higher CS than smaller cylinders, ranging from 7% to 19%. The incorporation of basalt CA enhanced the toughness and ductility of concrete, leading to superior post-peak behavior. In addition to the experimental program, four machine learning algorithms, i.e., Extreme Gradient Boosting (XGB), Gradient-Enhanced Regression Tree (GBR), Random Forest (RF), and Support Vector Regression (SVR), were employed to forecast the concrete’s CS. RF (R2 = 0.93) and gradient boosting models (R2 = 0.92) showed remarkable accuracy, whereas SVR underperformed. The feature importance and SHAP analysis identified cement content and CA type as the primary determinants of CS, while the water–cement ratio served as a crucial regulator. Moreover, a graphical user interface tool was developed to practically allow engineers to rapidly estimate concrete CS, bridging the gap between experimental validation and practical use.

In the construction industry, the incorporation of steel fibers in ultra-high-performance concrete (UHPC) is vital for improving its mechanical characteristics. In order to identify the essential factors of UHPC, the literature on the effect of steel fibers on UHPC is reviewed using scientometric methods in this work. The review contains complex processes like knowledge mapping, co-occurrence, and co-citation. In order to analyze the bibliographic data on the impact of steel fibers on UHPC, this study makes use of contemporary methodologies for data processing, mining, analysis, presentation, and visualization. The aim is to provide direction for further research in this area by summarizing the literature. In order to achieve this goal, data from the Scopus database, including publication sources, top authors, keywords, significant publications, and nations contributing the most to the subject, are retrieved and examined. According to the scientometric analysis, the most frequently used keyword is “steel fibers,” “Construction and Building Materials” is the most popular publication source in terms of citations and articles, and China is the top-ranking nation in the industry. Academic scholars can gain from this study’s graphical and quantitative portrayal of the contributing researchers and nations by making it easier to share concepts and form collaborative initiatives. This study also shows that steel fibers can improve the mechanical properties of UHPC but their widespread manufacturing and use are dependent on factors including the fiber content and geometry.

In today’s digital age, innovative artificial intelligence (AI) methodologies, notably machine learning (ML) approaches, are increasingly favored for their superior accuracy in anticipating the characteristics of cementitious composites compared to typical regression models. The main focus of current research work is to improve knowledge regarding application of one of the new ML techniques, i.e., gene expression programming (GEP), to anticipate the ultra-high-performance concrete (UHPC) properties, such as flowability, flexural strength (FS), compressive strength (CS), and porosity. In addition, the process of training a model that predicts the intended outcome values when the associated inputs are provided generates the graphical user interface (GUI). Moreover, the reported ML models that have been created for the aforementioned UHPC characteristics are simple and have limited input parameters. Therefore, the purpose of this study is to predict the UHPC characteristics while taking into account a wide range of input factors (i.e., 21) and use a GUI to assess how these parameters affect the UHPC properties. This input parameters includes the diameter of steel and polystyrene fibers (µm and mm), the length of the fibers (mm), the maximum size of the aggregate particles (mm), the type of cement, its strength class, and its compressive strength (MPa) type, the contents of steel and polystyrene fibers (%), and the amount of water (kg/m3). In addition, it includes fly ash, silica fume, slag, nano-silica, quartz powder, limestone powder, sand, coarse aggregates, and super-plasticizers, with all measurements in kg/m3. The outcomes of the current research reveal that the GEP technique is successful in accurately predicting UHPC characteristics. The obtained R2, i.e., determination coefficients, from the GEP model are 0.94, 0.95, 0.93, and 0.94 for UHPC flowability, CS, FS, and porosity, respectively. Thus, this research utilizes GEP and GUI to accurately forecast the characteristics of UHPC and to comprehend the influence of its input factors, simplifying the procedure and offering valuable instruments for the practical application of the model’s capabilities within the domain of civil engineering.

Flood forecast models have become better through research as they led to a lower risk of flooding, policy ideas, less human death, and less destruction of property, so this study uses Scientometric analysis for floods. In this analysis, citation-based data are used to uncover major publishing areas, such as the most prominent keywords, top best commonly used publications, the most highly cited journal articles, countries, and authors that have achieved consequent distinction in flood analysis. Machine learning (ML) techniques have played a significant role in the development of prediction systems, which have improved results and more cost-effective strategies. This study intends to give a review of ML methods such as decision trees, artificial neural networks, and wavelet neural networks, as well as a comparison of their precision, speed, and effectiveness. Severe flooding has been recognized as a significant source of massive deaths and property destruction in several nations, including India, China, Nepal, Pakistan, Bangladesh, and Sri Lanka. This study presents far more effective flood forecast approaches. This analysis is being used as a guide for experts and climate researchers when deciding which ML algorithm to utilize for a particular forecasting assignment.

BackgroundAmong the resistant isolates of MTB, multidrug resistant tuberculosis (MDR-TB) and extensively drug resistant tuberculosis (XDR-TB) have been the areas of growing concern. The genomic analysis showed that the respective genomic pool of the XDR-MTB proteome contains more than 30% of the hypothetical proteins for which no functions have been annotated yet. This class of proteins presumably have their own importance to complete genome and proteome information. The bioinformatics advancements have helped to annotate those hypothetical proteins by using various computational tools and have potential to classify them functionally.ObjectiveThe objective of this study was to propose a new and unique drug target against the deadly Mycobacterium tuberculosis using Bioinformatics approaches to characterize the hypothetical proteins.ResultsWe stepwise reduced the hypothetical proteins (total number: 1256) out of the complete proteome to only 26 essential hypothetical proteins. Out of those 26 proteins, the protein WP_003401246.1 was computationally characterized as the druggable target.ConclusionThe study proposed a hypothetical protein from complete proteome of the XDR-MTB as a new drug target against which new drug candidates can be proposed. Hence, the study opens up the new avenues in the areas of drug discovery against deadly M. tuberculosis.