This study deals with the resistive zone propagation in a superconducting coil during a quench, taking into account both the flux density distribution and the anisotropy of the thermal parameters. A Finite Difference Method is used to solve the heat diffusion equation and the flux density is calculated by means of a semi-analytical method. The 3-D model is suitable to describe the quench of thick coils and it can be applied to the study of thermal stability. As an application, a 10 kJ-solenoid is studied.
A new concept of large power air-cored cryogenic A.C. synchronous machine with a high number of poles is being studied in our laboratory. 3-D analytical models have been applied in order to study the field distribution. The main electric quantities have been expressed in terms of design parameters. The first prototype will be presented.
Cementitious composites with recycled plastic often suffer from reduced strength. This study explores the partial substitution of cement with industrial by-products in plastic-based mortar mixes (PBMs) to enhance performance while reducing environmental impact. To achieve this, five hybrid machine learning (ML) models CNN-LSTM, XGBoost-PSO, SVM + K-Means, SVM-PSO, and XGBoost + K-Means were developed to predict flexural strength, production cost, and CO2 emissions using a large dataset compiled from peer-reviewed sources. The CNN-LSTM model consistently outperformed the other approaches, showing high predictive capability for both mechanical and sustainability-related outputs. Sensitivity analysis revealed that water content and superplasticizer dosage are the most influential factors in improving flexural strength, while excessive cement and plastic waste were found to negatively impact performance. The proposed ML framework was also successful in estimating production cost and CO2 emissions, demonstrating strong alignment between predicted and actual values. Beyond mechanical and environmental predictions, the framework was extended through the RA-PSO model to estimate compressive and tensile strengths with high reliability. To support practical adoption, the study proposes a graphical user interface (GUI) that allows engineers and researchers to efficiently evaluate durability, cost, and environmental indicators. In addition, the establishment of an open access data-sharing platform is recommended to encourage broader utilization of PBMs in the production of paving blocks and non-structural masonry units. Overall, this work highlights the potential of hybrid ML approaches to optimize sustainable cementitious composites, bridging the gap between performance requirements and environmental responsibility.
Scouring around the bridge structure is a major concern of the globe. Therefore, a precise estimation of the scour depth is essential to minimize bridge failure and provide preventive measures. This review paper aims to analyze the critical review of various artificial intelligence (AI) techniques utilized in the literature to estimate bridge abutment scour depth including artificial neural networks (ANN), adaptive neuro-fuzzy inference systems (ANFIS), gene expression programming (GEP), support vector machines (SVM), and extreme learning machines (ELM). The predictive power of each technique was assessed in terms of different performance indicators, such as correlation coefficient (R), mean square error (MSE), predicted values, Taylor's diagram, sensitivity analysis, and violin plot. This review paper highlights that by comparing different AI techniques, ELM and GEP techniques have superior performance, especially in predicting scour depth and dealing with complex and large datasets. However, various limitations and proposed solutions have been reported for techniques, such as ANN, ANFIS, SVM, and group method of data handling (GMDH). The main challenges in the ANN, ANFIS, SVM, and GMDH techniques were overfitting and hyperparameter tuning. Based on the performance of each technique, the current review paper found the satisfactory performance of the ELM technique because of its computation speed and precise estimation capability. Moreover, the proposed solutions would be helpful to researchers working in the field of hydraulics engineering, particularly scouring around the bridge abutment.
Abstract The construction sector faces significant sustainability and environmental challenges due to the extensive use of conventional concrete. In this regard, ultra-high-performance geopolymer concrete (UHPGC) offers a promising alternative, featuring both rubberized and non-rubberized formulations with unique benefits. Rubberized UHPGC enhances ductility and resilience by incorporating recycled materials, while non-rubberized variants provide superior strength and durability. A comprehensive review is essential to enhance the understanding of UHPGC, evaluate its role in sustainable development, and guide future research and policy in the construction industry. This review article provides an innovative analysis of UHPGC, emphasizing its novel integration of geopolymer binders and the incorporation of recycled rubber particles to enhance mechanical and environmental performance. It examines the evolution, properties, and applications of both materials, highlighting their rapid setting times, improved workability, and reduced shrinkage. Moreover, it underscores their superior compressive, tensile, and flexural strengths, as well as enhanced energy absorption, ductility, fracture energy, and crack resistance, making them suitable for high-stress environments. This review suggests that using crumb rubber as a fine aggregate replacement in geopolymer concrete generally reduces compressive strength at higher levels. However, small additions, around 2%, may improve strength, highlighting the need for careful optimization to balance the performance and strength. The addition of steel and polypropylene fibers to UHPGC enhances flexural toughness and fracture energy. The integration of rubber particles into ultra-high-performance rubberized geopolymer concrete (UHPRGC) significantly enhances its ductility, toughness, and energy absorption, allowing the material to withstand higher strains and preventing brittle failure, making it ideal for high-performance applications. However, the inclusion of rubber compromises compressive strength because of inadequate interfacial adhesion, enhancements in surface treatment and the optimization of composite formulations can effectively overcome these challenges, and merging customized rubber proportions with advanced polymeric or fiber reinforcements maintains mechanical integrity while capitalizing on rubber’s elasticity, suggesting the necessity for ongoing investigation into microstructural optimization and long-term performance to thoroughly harness the sustainability and functionality of UHPRGC.
