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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.

The utilization of waste industrial materials such as Blast Furnace Slag (BFS) and Fly Ash (F. Ash) will provide an effective alternative strategy for producing eco-friendly and sustainable concrete production. However, testing is a time-consuming process, and the use of soft machine learning (ML) techniques to predict concrete strength can help speed up the procedure. In this study, artificial neural networks (ANNs) and decision trees (DTs) were used for predicting the compressive strength of the concrete. A total of 1030 datasets with eight factors (OPC, F. Ash, BFS, water, days, SP, FA, and CA) were used as input variables for the prediction of concrete compressive strength (response) with the help of training and testing individual models. The reliability and accuracy of the developed models are evaluated in terms of statistical analysis such as R2, RMSE, MAD and SSE. Both models showed a strong correlation and high accuracy between predicted and actual Compressive Strength (CS) along with the eight factors. The DT model gave a significant relation to the CS with R2 values of 0.943 and 0.836, respectively. Hence, the ANNs and DT models can be utilized to predict and train the compressive strength of high-performance concrete and to achieve long-term sustainability. This study will help in the development of prediction models for composite materials for buildings.

The strengthening of masonry infill wall panels (MIWPs) in reinforced concrete (RC) frame structures is of prime importance due to their failure in the in-plane and out-of-plane direction, as evidenced by past earthquakes. This research study applies an innovative approach of dual-strengthening materials to individual MIWPs in the RC frame structures to improve their seismic performance. Ferrocement overlay (FCO) and engineered cementitious composites (ECC) materials are applied to different MIWPs in a half-scaled two-story, two-by-two-bay reinforced concrete frame structure with different arrangements of infill walls. The strengthened masonry infill wall panels (SMIWPs) in the RC frame structure were tested using the quasi-static testing protocol. The testing results are compared with conventional masonry infill wall panels (CMIWPs) walls in RC frame structure in terms of damage pattern and backbone curve to evaluate the effect of the strengthening strategies applied to the MIWPs. The lateral Strength, stiffness, and ultimate ductility were enhanced by 166%, 40%, and 60%, respectively. The strengthening strategy effectively improved the damage propagations, lateral Strength, stiffness, and ductility.

Perovskite solar cells (PSCs) are a promising area of research among different new generations of photovoltaic technologies. Their manufacturing costs make them appealing in the PV industry compared to their alternatives. Although PSCs offer high efficiency in thin layers, they are still in the development phase. Hence, optimizing the thickness of each of their layers is a challenging research area. In this paper, we investigate the effect of the thickness of each layer on the photoelectric parameters of n-ZnO/p-CH3NH3PbI3/p-NiOx solar cell through various simulations. Using the Sol–Gel method, PSC structure can be formed in different thicknesses. Our aim is to identify a functional connection between those thicknesses and the optimum open-circuit voltage and short-circuit current. Simulation results show that the maximum efficiency is obtained using a perovskite layer thickness of 200 nm, an electronic transport layer (ETL) thickness of 60 nm, and a hole transport layer (HTL) thickness of 20 nm. Furthermore, the output power, fill factor, open-circuit voltage, and short-circuit current of this structure are 18.9 mW/cm2, 76.94%, 1.188 V, and 20.677 mA/cm2, respectively. The maximum open-circuit voltage achieved by a solar cell with perovskite, ETL and HTL layer thicknesses of (200 nm, 60 nm, and 60 nm) is 1.2 V. On the other hand, solar cells with the following thicknesses, 800 nm, 80 nm, and 40 nm, and 600 nm, 80 nm, and 80 nm, achieved a maximum short-circuit current density of 21.46 mA/cm2 and a fill factor of 83.35%. As a result, the maximum value of each of the photoelectric parameters is found in structures of different thicknesses. These encouraging results are another step further in the design and manufacturing journey of PSCs as a promising alternative to silicon PV.

This paper reviews the application of artificial intelligence (AI)-based models in modeling vehicular road traffic noise. A computerized search method was used to conduct the literature search. Fifty published articles from 2007 to 2023 were reviewed regarding observation time, input data, countries where studies were performed, and modeling techniques. Sixty-three percent of the studies used an observation period of 60 min, and 29% used 15 min. All the reviewed papers considered traffic flow as the major input parameter, followed by average speed, with 95% of the researchers using it as an input parameter. It was found that using AI-based models for traffic noise prediction was popular in countries with no established empirical models. The primary input parameters for the AI-based models are traffic volume and speed. Traffic volume is used either as total traffic volume or classified into sub-categories, and each category is used as an independent input parameter. Although AI-based models have demonstrated reliable performance regarding prediction error and goodness of fit, the accuracy of the AI-based models' performance should be compared with the results of the empirical models in countries with established models, such as the UK (CoRTN) and the USA (FHWA).

This research work is devoted to the experimental investigation of both rheological and mechanical properties of self-compacting concrete (SCC) produced with waste galvanized copper wire fiber and rice husk ash (RHA). In the study, three different volume fractions of 0.5 p to 0.75 percent, 1 percent of scrap copper wire fiber as reinforcing material, and 2 percent RHA as cement replacement were used. To evaluate the fresh characteristics of SCC, the slump flow, J-ring, and V-funnel experiments were conducted for this investigation. Compressive strength, splitting tensile strength, and flexural strength of the concrete were conducted to assess the hardened properties. The test was carried out to compare each characteristic of plain SCC with this modified SCC mixture, containing RHA as pozzolanic materials and copper fiber as reinforcing material. Incorporating copper fiber in the SCC leads to a drop in fresh properties compared to plain SCC but remains within an acceptable range. On the other hand, the inclusion of 2% RHA makes the SCC more viscous. Although adding 2% RHA and 1% copper wire in SCC provide the highest strength, this mix has an unacceptable passing ability. The SCC mix prepared with 2% RHA and 0.75% copper fiber is suggested to be optimum in terms of the overall performance. According to this study, adding metallic fiber reinforcement like copper wire and mineral admixture like RHA can improve the mechanical properties of SCC up to a certain level.

The continuous increase in population, advancement in technology, and affluence have influenced the amount of biodegradable and nonbiodegradable waste generated. Studies have shown that the utilization of different wastes in concrete is imperative to reduce the long-term environmental problems associated with their handling and management. This study evaluates the performance of concrete incorporating polyethene terephthalate powder (PETp) and rice husk ash (RHA) as supplementary cementitious materials varied at 0%, 7.5%, and 15%. Results indicated that the presence of PETp reduces workability while increasing the content of both PETp and RHA decreased the compressive and flexural strengths. A few studies have demonstrated the prediction and optimization of PETp as a fine aggregate. This study explores the central composite design of response surface methodology in optimizing the fresh and hardened properties of concrete incorporating PETp and RHA. The results indicate that workable concrete can be achieved with an RHA content higher than the PETp content. The analysis of variance provided effective models with good prediction capabilities. The simulated values from the models were close to those obtained experimentally. An optimal percentage of 5.76% PETp and 9.45% RHA was obtained for predicted responses and validated with a good level of accuracy. An overview of the different combinations of RHA and PETp indicates that concrete incorporating only RHA had the tendency to absorb the least water and exhibit low voids. However, the combination of 7.5% RHA and 7.5% PETp had a reduced water absorption when compared with a concrete mix containing 15% of either supplementary cementitious material. In general, eco-friendly concrete with improved durability can be produced by incorporating PETp and RHA.

The liquid limit (LL) is considered the most fundamental parameter in soil mechanics for the design and analysis of geotechnical systems. According to the literature, the LL is governed by different particle sizes such as sand content (S), clay content (C), and silt content (M). However, conventional methods do not incorporate the effect of all the influencing factors because traditional methods utilize material passing through a # 40 sieve for LL determination (LL40), which may contain a substantial number of coarse particles. Therefore, recent advancements suggest that the LL must be determined using material passing from a # 200 sieve. However, determining the liquid limit using # 200 sieve material, referred to as LL200 in the laboratory, is a time-consuming and difficult task. In this regard, artificial-intelligence-based techniques are considered the most reliable and robust solutions to such issues. Previous studies have adopted experimental routes to determine LL200 and no such attempt has been made to propose empirical correlation for LL200 determination based on influencing factors such as S, C, M, and LL40. Therefore, this study presents a novel prediction model for the liquid limit based on soil particle sizes smaller than 0.075 mm (# 200 sieve) using gene expression programming (GEP). Laboratory experimental data were utilized to develop a prediction model. The results indicate that the proposed model satisfies all the acceptance requirements of artificial-intelligence-based prediction models in terms of statistical checks such as the correlation coefficient (R2), root-mean-square error (RMSE), mean absolute error (MAE), and relatively squared error (RSE) with minimal error. Sensitivity and parametric studies were also conducted to assess the importance of the individual parameters involved in developing the model. It was observed that LL40 is the most significant parameter, followed by C, M, and S, with sensitivity values of 0.99, 0.93, 0.88, and 0.78, respectively. The model can be utilized in the field with more robustness and has practical applications due to its simple and deterministic nature.

The journal retracts the article titled “Mechanical, Durability, and Microstructural Evaluation of Coal Ash Incorporated Recycled Aggregate Concrete: An Application of Waste Effluents for Sustainable Construction” [...]

Geotechnical investigation, in hilly areas, for high-rise projects, becomes a problematic issue and costly process due to difficulties in mobilization and assembling the drilling equipment on mountainous terrains. The objective of this study is to map soil properties of study areas, especially at inaccessible sites, for reconnaissance. Digital soil maps for Tehsil Murree, Pakistan, have been developed using the emerging Geographical Information System (GIS). The research work involved the creation of an exhaustive database, by collecting and rectifying geotechnical data, followed by the digitization of the acquired data through integration with GIS, in an attempt to visualize, analyze and interpret the collected geotechnical information spatially. The soil data of 205 explanatory holes were collected from the available geotechnical investigation (GI) reports. The collection depth of soil samples, which were initially used for the design of deep and shallow foundations by different soil consultancies in the Murree area, was approximately 50 ft. below ground level. Appropriate spatial interpolation methods (i.e., the Kriging) were applied for the preparation of smooth surface maps of soil standard penetration tester number values, soil type and plasticity index. The accuracy of developed SPT N value and plasticity maps were then evaluated using the linear regression method, in which the predicted values of soil characteristics from developed maps and actual values were compared. SPT N value maps were developed up to a depth of 9.14 m below ground level and at every 1.52 m interval. The depth of refusal was considered in the developed maps. Soil type and plasticity maps were generated up to 15.24 m depth, again at every 1.52 m intervals, using color contours, considering the maximum predicted foundation depth for high-rise projects. The study has implications for academics and practitioners to map the soil properties for inaccessible sites using GIS, as the resulting maps have high accuracy.