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Concrete, a widely used building material, should minimize its environmental impact to align with the construction sector's Sustainable Development Goals. Researchers worldwide are investigating the possibility of e-waste in building materials incorporating various recyclable elements. This paper conducts a thorough review of research on e-waste as a building material. Various E-waste products are used as fine and coarse aggregate replacements in concrete. The study uses a bibliographic approach with the Scopus database to delve into the literature discussing E-waste in construction materials from 2007 to 2022. Data are extracted specifically from Scopus, which identifies 731 papers based on the keyword "Electronic waste used as building materials". The second step involves scientometric analysis, which focuses on patterns within the articles, such as the most prolific countries, sources, frequently used keywords, and reports containing relevant research. Each research phase contains a summary of the results obtained at various stages. Detailed quantitative and qualitative discussions are also conducted to achieve the three primary goals: a summary of quantitative data, a discussion of the existing application, and identifying future research directions. These findings will be useful for subsequent academic studies on transforming e-waste into building materials. The scientometric review provides a path for researchers from various countries to share new ideas and information while encouraging research collaboration. [ABSTRACT FROM AUTHOR]

In an age of depleting earth due to global warming impacting badly on the ozone layer of the earth system, the need to employ technologies to substitute those engineering practices which result in emissions contributing to the death of our earth has arisen. One of those technologies is one that can sufficiently replace overdependence on laboratory activities where oxides of carbon and other toxins are released. Also, it is one technology that brings precision to other engineering activities especially earthwork design and construction thereby reducing to lower ebb the release of carbon oxides due to inexact utilization of materials during geotechnical practices. In this review, the use of artificial intelligence techniques in geotechnics has been explored as a precise technique through which geotechnical engineering works don't impact on our planet due to precision. The intelligent learning algorithms of ANN, Fuzzy Logic, GEP, ANFIS, ANOVA and other natureinspired algorithms have been reviewed as they are applied in the prediction of geotechnical and geoenvironmental problems and system. It is a complex exercise to conduct experimental protocols during the design and construction of earthwork infrastructures. Most times, such experimental exercises don't meet the required condition for sustainable design and construction. At other times, certain errors as a result of experimental set up or human misjudgment may mar the accuracy of measurements and release unexpected emissions. The employment of the evolutionary learning methods has solved most of the lapses encountered in repeated laboratory measurements. So, in this review work, the relevant computational intelligent techniques employed at different times, under different laboratory protocols and utilizing different materials, have been presented as a comprehensive guide to future researchers in this innovative and evolving field of artificial intelligence. With this extensive review, a researcher would not have to look far to get a technical and state of the art guide in the utilization of various intelligent techniques that would enable engineering models in a more efficient, precise and more sustainable approach to forestall multiple practices that release carbon emissions into the environment. [ABSTRACT FROM AUTHOR]

This document is a correction notice for an article titled "Forecasting the rheological state properties of self-compacting concrete mixes using the response surface methodology technique for sustainable structural concreting." The correction states that the fourth author's name was spelled incorrectly and provides the correct spelling as Jorge Buñay. It also mentions typographical errors in the affiliations and provides the correct affiliations for each author. The correction notice is authored by Edwin Zumba, Nancy Velasco, Edison Marcelo Melendres Medina, Jorge Buñay, Nestor Augusto Estrada Brito, Kennedy C. Onyelowe, Nakkeeran Ganasen, and Shadi Hanandeh. [Extracted from the article]

Extreme vertex design (EVD) provides an efficient approach to mixture experiment design whereby the factor level possesses multiple dependencies expressed through component constraints formulation. Consequently, the derived experimental points are within the center edges and vertices of the feasible constrained region. EVD was deployed for the modeling of the mechanical properties of the problematic clayey soil-geogrid blends. Geogrids are geosynthetic materials that possess an open mesh-like structure and are mostly used for soil stabilization. The geotextile materials present a geosynthetic and permeable layer to support the soil and foundation by improvement of its stiffness characteristics and at a cheaper cost to procure compared to other construction materials and possess unique lightweight properties with greater strength improvement on the soil layer when used. Minitab 18 and Design Expert statistical software was utilized for the mixture design experiment computation; to fully explore the constrained region of the simplex, I-optimal designs with a special cubic design model were utilized to formulate the mixture component ratios at ten experimental runs. I-optimality and D-optimality of 0.39093 and 1747.474, respectively, were obtained with a G-efficiency of 64.8%. The generated laboratory responses were taken together with the mixture ingredients' ratio and taken as the system database for the model development. Statistical influence and diagnostics tests carried out on the generated EVD model indicate a good correlation with the experimental results. Graphical and numerical optimizations were incorporated using a desirability function that ranged from 0 to 1, which helped to arrive at the optimal combination of the mixture components. 0.2% of geogrid, 9.8% of water, and 90 % of soil yielded the optimal solution with a response of 41.270kN/m2 and a desirability score of 1.0. The model simulation was further carried out to test the model's applicability with the results compared with the actual results using student's t-test and analysis of variance. The statistical results showed a p-value>0.05 which indicates a good correlation. [ABSTRACT FROM AUTHOR]

Studying the rheological behavior of concrete, especially self-compacting concrete is vital in the design and structural integrity of concrete structures for design, construction, and structural material sustainability. Both analytical and numerical techniques have been applied in the previous research works to study precisely the behavior of the yield stress and plastic viscosity of the fresh self-compacting concrete with the associated flow properties and these results have not been systematically presented in a critical review, which will allow researchers, designers and filed operators the opportunity to be technically guided in their design and model techniques selection in order to achieve a more sustainable concrete model for sustainable concrete buildings. Also, the reported analytical and numerical techniques have played down on the effect of the shear strain rate behavior and as to reveal the viscosity changes of the Bingham material with respect to the strain rate. In this review paper, a critical study has been conducted to present the available methods from various research contributions and exposed the inability of these contributions to revealing the effect of the shear strain rate on the rheological behavior of the self-compacting concrete. With this, decisions related to the rheology and flow of the self-compacting concrete would have been made with apt and more exact considerations., (© 2023. The Author(s).)

The present investigation aims to examine the mechanical and durability properties of concrete that has been reinforced with a waste printed circuit board (WPCB) towards a low-carbon built environment. It assessed the fresh and hardened characteristics of the low-carbon concrete reinforced with WPCB fibres, after a curing period of 7 and 28 days. The evaluation was done by quantifying slump, compressive strength, split tensile strength, flexural strength, sorptivity, rapid, and acid tests. It further analysed eleven discrete concrete mixes with WPCB fibres at a weight percentage ranging from 1 to 5% in the cement mixture. The results indicate that incorporating WPCB fibre into concrete improves its mechanical strength. The results revealed that incorporating 5% WPCB fibre yielded the most favourable outcomes. The properties of WPCB fibre-reinforced concrete have been theoretically validated through Response Surface Methodology (RSM), which employs various statistical and mathematical tools to analyse the experimental data. The results derived from RSM were compared with the experimental results. It was found that the RSM model demonstrated a high level of accuracy (R 2 ≥ 0.98) in validating the mechanical properties of WPCB fibre concrete. The statistical model exhibited no indication of prediction bias and demonstrated a statistically significant outcome, with a p-value below 0.5., (© 2023. Springer Nature Limited.)

In this study, the replacement of raw rice husk, fly ash, and hydrated lime for fine aggregate and cement was evaluated in making raw rice husk-concrete brick. This study optimizes compressive strength, water absorption, and dry density of concrete brick containing recycled aggregates via Response Surface Methodology. The optimized model's accuracy is validated through Artificial Neural Network and Multiple Linear Regression. The Artificial Neural Network model captured the 100 data's variability from RSM optimization as indicated by the high R threshold- (R > 0.9997), (R > 0.99993), (R > 0.99997). Multiple Linear Regression model captured the data's variability the decent R 2 threshold confirming- (R 2  > 0.9855), (R 2  > 0.9768), (R 2  > 0.9155). The raw rice husk-concrete brick 28-day compressive strength, water absorption, and density prediction were more accurate when using Response Surface Methodology and Artificial Neural Network compared to Multiple Linear Regression. Lower MAE and RMSE, coupled with higher R 2 values, unequivocally indicate the model's superior performance. Additionally, employing sensitivity analysis, the influence of the six input parameters on outcomes was assessed. Machine learning aids efficient prediction of concrete's mechanical properties, conserving time, labor, and resources in civil engineering., (© 2023. Springer Nature Limited.)

A state-of-the-art review has been conducted in this work on soil constitutive modeling, which has emphasized on: soil type, ground-water conditions, loading conditions, structural behavior, constitutive relation discipline, and dimensions. By extension also, the soil constitutive applications were reviewed on the bases of: single discipline dealing with soil mechanical properties constitutive modeling which included slope stability problems, bearing capacity, settlement of foundations, earth pressure problems, soil dynamics, soil structure interaction, thermal and hydrological conditions; bi-discipline (coupled problems) which solve problems related to thermomechanical (freeze/thaw conditions), smoothed particle hydrodynamics (SPH) and hydromechanical (consolidation, collapse and liquefaction) conditions in soils and rocks and multi-discipline constitutive models which solve complex problems related to thermo-hydromechanical (THM) conditions in soils and rocks. This work has shown that smoothed particle hydrodynamics (SPH) and hydromechanical (HM) models, which belong to bi-discipline or coupled conditions are better suited for geotechnical applications, generally, while thermo-hydromechanical (THM) models, which belong to multi-discipline are better suited to solving freeze/thaw and thermal piles problems and these are proven with high performance and flexibility., Competing Interests: The authors declare no conflict of interest., (© 2023 The Authors.)

The behavior of undrained clay was extensively studied by many earlier researchers. A lot of constitutive models were developed to describe the behavior of undrained clay based on its mechanical properties. The aim of this research is to present an innovative constitutive model for undrained clay based on its consistency limits and water content. The main concept of this model is to estimate the mechanical properties of clay using earlier correlations with consistency limits, then implement the estimated mechanical properties in a hyperbolic model and calibrate the hyperbolic parameters to match the failure criteria of the undrained clay. To verify the validity of the developed constitutive model, it was applied on a standard problem which is a strip footing rested on undrained clay layer, the results confirmed the ability of the model to simulate the nonlinear behavior of undrained clay up to ultimate condition. The main advantage of this constitutive model is the ability to capture the reduction of mechanical properties of clay with the increase in its water content, which makes it ideal to study the impact of seepage on shallow foundation., Competing Interests: The authors declare no conflict of interest., (© 2022 Published by Elsevier Ltd.)