An analytical representation of the potential created by charged sectors of a ring has been investigated. The surface charge density of the ring is supposed to be constant along a radius, assuming that a Fourier series development allows simplification into a linear superposition of sinusoidal surface densities. In addition to the obvious electrostatic devices, this approach also applies-because of the similarity of the magnetic equations-to permanent magnets with a uniform magnetization. Laplace's equation is solved by means of separation of variables. A Hankel transform, followed by a numerical integration of a Laplace transform, leads to the identification of the coefficients. Most generally, this integration involves elliptic functions which allow a fast and precise numerical treatment. At any point in space, the potentials are computed with the same simplicity and nearly the same accuracy. The proposed method introduces no systematic error and requires only a small memory-size desk computer. It is also much faster than purely numerical methods such as FEM or FDM; any change in the geometrical dimensions can also be achieved without previous reconditioning. The results of this method compare favorably with those already obtained for homopolar distributions and also with previous experiments on an eight-pole-pair distribution of magnets.
The cyc/ical pressure variations and infernal fJows induced by tide are otten the major cause of generol instability of the harbour buildings.We proposed a model wh/ch tries ta est/mate the impact of cyclical hydraulic actions on bank behav/our.The present paper discusses mathematical linearisation procedures for the nonlinear equation of the free surface /n unconfined homogeneous aquifers.We study in this case the governing movement equation of tidal cyclic oscillation transmitted through the porous media of the vertical bonk.A new linear torm is admitted and justitied, and the analytical solution is developed.f<esuftsore shawn with a graph of groundwafer table fluctuation in the bonk which is due ta the osciflations of the woter level of ocean-tide effects in coastal aquifer.The linear form involves a water table raising level in the bank, this leve! is greater thon the average seo level.The envelope curves can define the "beoten" zone in soil generated by tide This onolysis shows that some Interessant points could be used in horbour building conception and safety sfudy.
This paper deals with an implementation of a stator flux oriented method on a DSP board to drive an induction motor. This method is based on the integration of EMF directly provided by two extra coils located on the equivalent /spl alpha/, /spl beta/ reference axes of the stator. The open-loop integration problem is solved thanks to an online offset compensation algorithm which offers robustness especially at low speed. Experimental results of speed control are presented and commented to show the effectiveness of this method.
Determining the elements of the equivalent scheme of an induction machine is not trivial. Knowing that the values of the parameters change with the operating point, we can only find an optimal set which is suitable to describe the dynamic behaviour of the machine. The calculations of the vector elements of the parameters are made using optimization methods. Some of these slowly converge while others diverge if the initial vector is far from the solution. Marquardt's method does not present these disadvantages. Taking into account some realistic constraints, we apply this method here in order to find electrical and mechanical parameters during the starting phase of an induction machine. Results of experiments and simulations are favourably compared.
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.
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.
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.
