A multi-criteria evaluation and optimization of sustainable fiber-reinforced concrete developed with nylon waste fibers and micro-silica.

Nylon waste fibers similar to new nylon fibers possess high tensile strength and toughness; hence, they can be used as an eco-friendly discrete reinforcement in high-strength concrete. This study aimed to analyze the mechanical and permeability characteristics and life cycle impact of high-strength concrete with varying amounts of nylon waste fiber and micro-silica. The results proved that nylon waste fiber was highly beneficial to the tensile and flexural strength of concrete. The incorporation of a 1% volume of nylon waste fiber caused net improvements of 50% in the flexural strength of concrete. At the combined addition of 0.5% volume fraction of nylon fiber and 7.5% micro-silica, splitting tensile and flexural strength of high-strength concrete experienced net improvements of 49% and 55%, respectively. Nylon fiber-reinforced concrete exhibited a ductile response and high flexural toughness and residual strength compared to plain concrete. A low volume fraction of waste fibers was beneficial to the permeability resistance of high-strength concrete against water absorption and chloride permeability, while a high volume (1% by volume fraction) of fiber was harmful to the permeability-resistance of concrete. For the best mechanical performance of high-strength concrete, 0.5% nylon waste fiber can be used with 7.5% micro-silica. The use of micro-silica minimized the negative effect of the high volume of fibers on the permeability resistance of high-strength concrete. The addition of nylon waste fibers (at 0.25% and 0.5% volume) and micro-silica also reduced carbon emissions per unit strength of concrete.

Advanced machine learning algorithms to evaluate the effects of the raw ingredients on flowability and compressive strength of ultra-high-performance concrete.

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 (R2), 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 R2 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 R2 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.

Knowledge Mapping of the Literature on Fiber-Reinforced Geopolymers: A Scientometric Review.

This study examined the bibliographic data on fiber-reinforced geopolymers (FRGPs) using scientometrics to determine their important features. Manual review articles are inadequate in their capability to connect various segments of literature in an ordered and systematic manner. Scientific mapping, co-citation, and co-occurrence are the difficult aspects of current research. The Scopus database was utilized to find and obtain the data needed to achieve the study's aims. The VOSviewer application was employed to assess the literature records from 751 publications, including citation, bibliographic, keyword, and abstract details. Significant publishing outlets, keywords, prolific researchers in terms of citations and articles published, top-cited documents, and locations actively participating in FRGP investigations were identified during the data review. The possible uses of FRGP were also highlighted. The scientometric analysis revealed that the most frequently used keywords in FRGP research are inorganic polymers, geopolymers, reinforcement, geopolymer, and compressive strength. Additionally, 27 authors have published more than 10 articles on FRGP, and 29 articles have received more than 100 citations up to June 2022. Due to the graphical illustration and quantitative contribution of scholars and countries, this study can support scholars in building joint ventures and communicating innovative ideas and practices.

Geometric Optimization of Perovskite Solar Cells with Metal Oxide Charge Transport Layers.

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.

Flexural Strength Prediction of Steel Fiber-Reinforced Concrete Using Artificial Intelligence.

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.

Performance Optimization Approach of Polymer Modified Asphalt Mixtures with PET and PE Wastes: A Safety Study for Utilizing Eco-Friendly Circular Economy-Based SDGs Concepts.

Eco-friendly waste utilization helps in the development of sustainable infrastructures. Recently, researchers have focused on the production of road infrastructures using the circular economy concept of human safety. The objective of this study is to investigate and explore the utilization of optimum polymer waste content for the development of polymer-modified asphalt mixtures using response surface methodology (RSM). RSM based on Box-Behnken design (BBD) was employed to optimize experimental design and included three factors: X1, polymer type; X2, polymer contents; and X3, testing day. The optimized responses determined by the RSM were as follows: MS of 42.98 kN, MF of 5.08 mm, and MQ of 8.66 kN/mm, indicating a favorable and consistent precision in comparison with experimental values. Moreover, the Marshall characteristics of samples prepared with PE were quite improved compared to PET. In conclusion, the incorporation of such polymer wastes in road construction is a sustainable and cost-effective way of improving their engineering properties. This study will help in the development of sustainable road infrastructures supporting human safety and environmentally friendly practices.

Conservation Environments' Effect on the Compressive Strength Behaviour of Wood-Concrete Composites.

This paper addresses the issues in making wood-concrete composites more resilient to environmental conditions and to improve their compressive strength. Tests were carried out on cubic specimens of 10 × 10 × 10 cm3 composed of ordinary concrete with a 2% redwood- and hardwood-chip dosage. Superficial treatments of cement and lime were applied to the wood chips. All specimens were kept for 28 days in the open air and for 12 months in: the open air, drinking water, seawater, and an oven. Consequently, the compressive strength of ordinary concrete is approximately 37.1 MPa. After 365 days of exposure to the open air, drinking water, seawater, and the oven, a resistance loss of 35.84, 36.06, 42.85, and 52.30% were observed, respectively. In all environments investigated, the untreated wood composite concrete's resistance decreased significantly, while the cement/lime treatment of the wood enhanced them. However, only 15.5 MPa and 14.6 MPa were attained after the first 28 days in the cases of the redwood and the hardwood treated with lime. These findings indicate that the resistance of wood-concrete composites depends on the type of wood used. Treating wood chips with cement is a potential method for making these materials resistant in conservation situations determined by the cement's chemical composition. The current study has implications for researchers and practitioners for further understanding the impact of these eco-friendly concretes in the construction industry.

Mechanical Performance of Polymeric ARGF-Based Fly Ash-Concrete Composites: A Study for Eco-Friendly Circular Economy Application.

At present, low tensile mechanical properties and a high carbon footprint are considered the chief drawbacks of plain cement concrete (PCC). At the same time, the combination of supplementary cementitious material (SCM) and reinforcement of fiber filaments is an innovative and eco-friendly approach to overcome the tensile and environmental drawbacks of plain cement concrete (PCC). The combined and individual effect of fly ash (FA) and Alkali resistance glass fiber (ARGF) with several contents on the mechanical characteristics of M20 grade plain cement concrete was investigated in this study. A total of 20 concrete mix proportions were prepared with numerous contents of FA (i.e., 0, 10, 20, 30 and 40%) and ARGF (i.e., 0, 0.5, 1 and 1.5%). The curing of these concrete specimens was carried out for 7 and 28 days. For the analysis of concrete mechanical characteristics, the following flexural, split tensile, and compressive strength tests were applied to these casted specimens. The outcomes reveal that the mechanical properties increase with the addition of fibers and decrease at 30 and 40% replacement of cement with fly ash. Replacement of cement at higher percentages (i.e., 30 and 40) negatively affects the mechanical properties of concrete. On the other hand, the addition of fibers positively enhanced the flexural and tensile strength of concrete mixes with and without FA in contrast to compressive strength. In the end, it was concluded that the combined addition of these two materials enhances the strength and toughness of plain cement concrete, supportive of the application of an eco-friendly circular economy. The relationship among the mechanical properties of fiber-reinforced concrete was successfully generated at each percentage of fly ash. The R-square for general relationships varied from (0.48-0.90) to (0.68-0.96) for each percentage of FA fiber reinforced concrete. Additionally, the accumulation of fibers effectively boosts the mechanical properties of all concrete mixes.

Development of Construction Material Using Wastewater: An Application of Circular Economy for Mass Production of Bricks.

Water is one of the necessary ingredients for construction materials. Billions of gallons of clean water are wasted during the development of fired clay bricks. Similarly, the waste of clean water is a global issue. In this study, we develop fired clay bricks with the help of wastewater for the first time and compare these with clay bricks produced using groundwater, which is the conventional method. Both destructive (i.e., compressive strength (CS)) and non-destructive (i.e., ultrasonic pulse velocity (UPV)) tests are conducted on all fired clay brick specimens as per the American Society for Testing and Materials (ASTM). Physical (i.e., dimensions) and durability (water absorption, efflorescence, etc.) tests are also conducted. All kinds of brick satisfied the standard requirements of physical and durability characteristics. Similar or better strength of bricks were achieved using wastewater. The study concludes that the testing results of wastewater bricks were significantly 15-25% higher compared with groundwater-fired clay bricks. A large amount of wastewater can be used to develop bricks, and clean water can be saved to attain circular economy goals. Therefore, this study will help not only in developing low-cost bricks but also in saving clean water.