Ahmed Farouk Deifalla

Predicting the shear strength of concrete elements is a complex challenge influenced by numerous factors, with the type of concrete playing a decisive role in structural performance. Lightweight concrete, offering a superior strength-to-weight ratio and improved thermal properties compared to conventional weight concrete, has gained increasing adoption in structural applications. This study proposes a deep neural network (DNN) model optimized using the COVID-19 optimization algorithm to predict the shear strength of lightweight concrete elements. The optimization process determines the optimal initial weights and biases of the DNN, enhancing convergence and accuracy. The proposed model was evaluated against three international design codes—ACI, EC2, and JSCE—using experimental datasets. Results show that the COVID-19–optimized DNN closely simulates and tracks actual shear strength values, even in highly nonlinear data regions (e.g., samples 90–120), where traditional models produce larger deviations. Quantitatively, the proposed DNN achieved the lowest average error (0.692) compared to the higher errors from ACI, EC2, and JSCE models. These findings demonstrate the model’s superior predictive capability and its potential to enhance design accuracy for lightweight concrete structures.

Recent years have witnessed a surge in global interest and awareness regarding sustainability. Research highlights the crucial role of sustainability education in enhancing student awareness and influencing their sustainable behaviors. Sustainability awareness among university-level students is a crucial factor in shaping future attitudes and behaviors toward sustainable development. In Cairo, Egypt, where rapid urbanization and environmental challenges are prominent, assessing sustainability awareness can provide valuable insights for educational interventions and policy development. This study explores the level of sustainability awareness in Cairo, Egypt, concerning the academic levels and socio-educational profiles of the students. A questionnaire was developed and distributed among various students in different schools in Cairo, Egypt; 524 responses were collected within more than one year. The questionnaire responses were analyzed using several advanced numerical techniques, including but not limited to chi-squared tests, Spearman correlation, and multinomial logistic regression. The relationship between selected parameters on the level of sustainability awareness and its significance was identified. Findings reveal that while a significant portion of participants have encountered the term “sustainability,” their actual knowledge of sustainability concepts, such as recycling, renewable resources, and energy conservation, remains limited. Notably, a majority of students are not actively involved in any recycling practices. Despite these knowledge gaps, the study indicates that a considerable number of students engage in sustainable actions and lifestyles. The study concludes that fostering sustainability awareness requires a multi-stakeholder approach involving universities, schools, governments, and local municipalities. Recommendations include integrating mandatory sustainability courses into academic programs, supporting student-led sustainability initiatives both on and off campus, and implementing resource conservation measures within educational institutions. Moreover, the study emphasizes the need for collaborative efforts among all stakeholders who significantly influence individual sustainable knowledge and behaviors.

Currently, the need to repair and strengthen structures is very important. The reinforcement of structures aims to repair or bring existing structures into conformity, either for reasons of loss of initial properties or for reasons of refurbishment level linked to new standards or new uses. One of the methods which has met with great success in the field of upgrading reinforced concrete structures is exterior bonding using composite materials. This article summarizes some of the methods used for the strengthening of concrete elements, and compares them from a technical and environmental point of view.

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”

The increasing demand for sustainable construction materials has led to the incorporation of Palm Oil Fuel Ash (POFA) into concrete to reduce cement consumption and lower CO₂ emissions. However, predicting the compressive strength (CS) of POFA-based concrete remains challenging due to the variability of input factors. This study addresses this issue by applying advanced machine learning models to forecast the CS of POFA-incorporated concrete. A dataset of 407 samples was collected, including six input parameters: cement content, POFA dosage, water-to-binder ratio, aggregate ratio, superplasticizer content, and curing age. The dataset was divided into 70% for training and 30% for testing. The models evaluated include Hybrid XGB-LGBM, ANN, Bagging, LSSVM, GEP, XGB and LGBM. The performance of these models was assessed using key metrics, the coefficient of determination (R2), root mean square error (RMSE), normalized root means square error (NRMSE), mean absolute error (MAE) and Willmott index (d). The Hybrid XGB-LGBM model achieved the maximum R2 of 0.976 and the lowest RMSE, demonstrating superior accuracy, followed by the ANN model with an R2 of 0.968. SHAP analysis further validated the models by identifying the most impactful input factors, with the water-to-binder ratio emerging as the most influential. These predictive models offer the construction industry a reliable framework for evaluating POFA concrete, reducing the need for extensive experimental testing, and promoting the development of more eco-friendly, cost-effective building materials.