Comparison of the Properties of Mono-like, Monocrystalline, and Multicrystalline Silicon for Photovoltaic Applications.

Quasi-mono silicon (Mono-like) (ML-Si) offers improved crystal quality over multicrystalline silicon (mc-Si) while maintaining lower production costs. Manufactured via directional solidification with seeded growth, ML-Si achieves a crystallographic structure comparable to Czochralski-grown monocrystalline silicon (Cz-Si). This study compares the physical and chemical properties of ML-Si, Cz-Si, and mc-Si using scanning electron microscopy (SEM), X-ray diffraction (XRD) and EDX analysis performed with SEM analysis. Results show that ML-Si closely matches Cz-Si in surface morphology and crystal orientation. The EDX spectra indicate that Silicon is the dominant element. After alkaline texturing, the reflectivity of ML-Si is approximately 5% higher (absolute) than that of Cz-Si across the 300–1200 nm range, but significantly lower than that of mc-Si. This reduced reflectivity compared to mc-Si, combined with its lower dislocation density and fewer grain boundaries lead to improved structural uniformity and superior optical performance. Consequently, ML-Si combines the economic benefits of high performance of Cz-Si and the cost advantages of mc-Si production, making it as promising material for high-efficiency, low-cost photovoltaic applications

Effet des caractéristiques d'élasticité sur la déformabilité d'un massif de sol lors de l'excavation d'un ouvrage souterrain.

The excavation of underground structures, particularly shallow ones, causes significant deformations of the soil mass, potentially leading to instabilities both near the structure and at the surface. Predicting these movements and implementing stabilization measures remains complex due to the numerous factors involved, such as geological, geotechnical, and hydrogeological conditions, as well as the dimensions and depth of the excavation. The soil mass's response depends primarily on its capacity to deform without failure. This work investigates the influence of elasticity parameters on soil deformability during shallow excavation. Finite element modelling enables a parametric analysis of stiffness. The results demonstrate a high sensitivity of displacements to elastic parameters and highlight the need for reliable stiffness estimation to accurately predict soil mass behaviour and ensure the safety of underground structures.Le creusement d’ouvrages souterrains, notamment peu profonds, provoque des déformations importantes du massif de sol, pouvant entraîner des instabilités à proximité de l’ouvrage et en surface. La prévision des mouvements et des mesures de stabilisation reste complexe en raison des nombreux facteurs impliqués, tels que les conditions géologiques, géotechniques et hydrogéologiques, ainsi que les dimensions et la profondeur de l’excavation. La réponse du massif dépend principalement de sa capacité à se déformer sans rupture. Ce travail étudie l’influence des paramètres d’élasticité sur la déformabilité du sol lors du creusement d’une excavation peu profonde. Une modélisation par éléments finis permet de réaliser une analyse paramétrique de la rigidité. Les résultats montrent une forte sensibilité des déplacements aux paramètres élastiques et soulignent la nécessité d’une estimation fiable de la rigidité pour prédire correctement le comportement du massif et assurer la sécurité des ouvrages souterrains

Production of eco-friendly self-compacting concrete with recycled concrete aggregate

In recent years, recycled aggregate concrete (RAC) has become a major focus of research due to its positive environmental impact. The objective of this paper is to investigate the influence of dosage of recycled sand and gravel on the fresh properties of self compacting concrete (SCC). Experimental program was conducted on SCCs made with different rates of substitutions (25, 50, 75 and 100%) of natural sand and gravel (NS, NG) with recycled sand and gravel (RS, RG) by volume. All mixtures were obtained with a constant W/C ratio of 0.38, sand-to-mortar (S/M) ratio of 0.5 and a superplasticizer dosage of 1.5% of binder by weight. For fresh properties, tests were conducted for slump flow, V-funnel, L-box and resistance to segregation. Rheometer apparatus in the rheological testing of fresh SCC was used to determine the rheological parameters. The results indicated that an optimum replacement level of recycled sand and gravel of 50% can be achieved without adversely affecting key fresh concrete properties, including filling ability, passing ability, and segregation resistance. In addition, a clear correlation was established between the empirical and rheological parameters of the self-compacting recycled aggregates concrete

Self-Healing Mechanisms and Performance of Calcium Sulfoaluminate (CSA) Cement Based Concrete: A Review

Calcium sulfoaluminate (CSA) cement has emerged as a promising alternative to ordinary Portland cement (OPC) due to its lower carbon footprint, rapid strength development, and excellent durability. One of its notable advantages is the potential for self-healing, which is crucial for extending the service life of concrete structures and reducing maintenance costs. This review explores the self-healing mechanisms and performance of CSA cement-based concrete. Key factors influencing self-healing include expansive hydration products such as ettringite, which can fill microcracks and restore mechanical integrity. Additionally, the high sulfate content in CSA cement promotes continued hydration and recrystallization, especially in the presence of moisture. Various studies have demonstrated that CSA concrete exhibits superior crack-sealing capabilities compared to OPC systems, particularly under wet–dry cycling and immersion conditions. The review also discusses the role of supplementary materials and admixtures in enhancing self-healing efficiency. While CSA cement shows significant promise, challenges remain in standardizing testing methods and optimizing mix designs for specific applications. Overall, CSA concrete's self-healing potential makes it a viable candidate for sustainable, durable infrastructure in future construction practices

The Role of Ai-Supported Models in the Damage Detection Process of Historical Buildings: A Review

The sustainable preservation of historical buildings is of great importance for the future preservation of cultural heritage. The advent of artificial intelligence (AI) technologies in recent years has led to significant advancements in damage detection for historical buildings, resulting in enhanced efficiency and speed. Consequently, there has been a notable proliferation of artificial intelligence-based damage detection models in the extant literature. This study aims to examine the role of artificial intelligence-supported models in the damage detection process of historical buildings. A comprehensive review of the extant literature was conducted, encompassing a total of 97 case studies. The analysis revealed that damages to historic buildings can be categorized into three primary classes: disaster damages, structural damages (including structural health monitoring), and surface damages. The study provides a comprehensive analysis of damage detection methods in historical buildings, offering significant insights into the performance of existing artificial intelligence models in each category. The effectiveness of artificial intelligence-supported models in damage detection for historical buildings has been evaluated, and the strengths and shortcomings in the existing literature have been identified. The study further highlights aspects that require improvement in existing approaches and provides recommendations for future research endeavors. This study emphasizes the significance of artificial intelligence-based damage assessment methods for the conservation of historical buildings, laying the groundwork for future research in this field

Predicting the Compressive Strength of UHPFRC Using Machine Learning and Soft Computing Models: Optimization of Fiber Content and Additives

Ultra-high-performance fiber-reinforced concrete (UHPFRC) is a relatively new material known for its superior mechanical properties, particularly its compressive strength (CS), making it suitable for advanced structural applications. Traditional experimental methods for predicting CS are time-consuming and costly. In this study, a dataset of 276 samples with 12 input parameters was compiled from existing literature to develop predictive analytical models. The input variables include cement, sand, water, superplasticizer, silica fume, fiber content, water–binder ratio, water–cement ratio, curing age, fiber aspect ratio, temperature, and fiber volume. The reported CS values range from 90 to 186 MPa. Five modeling techniques—Linear Regression (LR), Log Base Regression (LBR), Nonlinear Regression (NLR), M5P-tree, and Artificial Neural Network (ANN)—were employed to predict the compressive strength of UHPFRC. Among these models, ANN demonstrated the highest prediction accuracy across all evaluation criteria, followed by the M5P-tree model. Residual error analysis confirmed that the ANN produced the lowest prediction error. Sensitivity analysis revealed that temperature, curing age, and superplasticizer content significantly influence CS. Optimization results indicated that a fiber content between 2.05% and 2.09% yields maximum compressive strength. These findings provide valuable insights for optimizing UHPFRC mix design using machine learning approaches

Typological and Seismic Code Assessment of Stone Masonry Walls: The Case of Safranbolu Houses

Safranbolu has preserved this texture throughout history by developing spatially and stylistically with the contributions of different civilizations along with its unique qualities and determining features. Safranbolu houses, shaped by traditional values, are important heritage elements that bridge the past and the future. Although these buildings have been exposed to various natural disasters over the years, they have managed to largely preserve their architectural integrity. However, it is estimated that these historical structures in Safranbolu, located approximately 65 km from the North Anatolian Fault, are under threat of earthquakes. Especially the stone masonry walls, which are widely used in Safranbolu houses, are known to be more sensitive to seismic effects and carry a great risk in terms of structural integrity. This study aims to examine in detail the compliance of opening ratios in stone masonry walls with seismic design provisions to protect Safranbolu houses and ensure their safe transfer to future generations.  Within the scope of the study, the typology and grouping of the ground floor masonry stonewalls of 100 Safranbolu houses built in the 18th and 19th centuries according to the rules specified in the 2007 “Regulation on Buildings to be Built in Earthquake Zones” were analyzed and structural compliance analysis was performed. The structural compliance analysis revealed that 15% of the masonry walls conformed to modern design standards, while 85% did not, and that structural improvements are needed to reduce seismic vulnerability

A Data-Driven Approach for Estimation and Multi-Objective Optimization of Concrete Mix Design

The study focuses on establishing the optimum concrete mix of ratios through a comprehensive analysis of experimental results. For this purpose, 62 numbers of concrete mixtures have been considered by varying the level of key ingredients- cement, water, fine aggregate and coarse aggregate. Using experimental data, Genetic Expression Programming (GEP) has been used to develop predictive equations for compressive strength and slump with cement, water, and coarse aggregates and fine aggregates as inputs. These equations are useful to estimate compressive strength and workability of concrete for particular ingredients. Moreover, mathematical multi objective optimization has been conducted by Genetic Algorithm (GA) using these equations as basic functions and optimum content of cement, water, fine aggregate and coarse aggregate have been determined for obtaining maximum compressive strength, maximum slump at lowest cost. Further, multi objective optimizations of different grades of concrete with slump and cost separately have also been carried out to determine these ingredients. Thus, by implementing the present results a more accurate number of mixed proportions with desired compressive strength, and slump can be obtained at minimum cost

Investigation of Bending Response in Hollow Core Beams: Experimental and Analytical Insights

The idea of using hollow cores in bridges is quite common, but it can be effectively applied to reinforced concrete beams in RCC buildings. The major advantage of use hollow cores is the reduction in the overall self-weight of the beam. This leads to a lower dead load on columns and foundations, which may help to cut down the amount of concrete and steel needed—making the structure more economical. While many researchers have investigated the structural behavior of hollow core beams under flexure, shear, and torsion, but there has not been much focus on beams where the hollow core is typically placed below the neutral layer of beam. The flexural performance of three hollow core beam specimens was examined in this study and compared with that of a conventional solid beam. The hollow cores were passed longitudinally below the neutral layer of those three specimens with 5%, 10% and 15 % core replacement of cross section The flexure test was conducted over simply supported beam specimens using two-point loading. Flexural stiffness, maximum deflections, crack pattern, ultimate load capacity, and deflection variations with load were all thoroughly investigated. To support the test outcomes, nonlinear finite element analysis was also performed on the same specimens. Both experimental and analytical results indicate that hollow core beams demonstrate flexural behaviour in terms of load-carrying capacity, deflection, stiffness, and failure patterns comparable to that of solid beams

Enhanced Funnel Control Strategies for Wind Turbine Speed Regulation – FC Symmetric with scaling.

This paper investigates a symmetric Funnel Control (FC) strategy enhanced with a gain scaling mechanism for wind turbine speed regulation. The proposed approach guarantees prescribed transient and steady-state performance by constraining the tracking error within identical upper and lower performance boundaries. Unlike classical funnel control, which offers limited tuning flexibility, the gain-scaled symmetric FC introduces an additional tuning parameter that significantly improves transient behavior while preserving stability and constraint satisfaction. A nonlinear wind turbine model including aerodynamic torque and drivetrain dynamics is considered. Simulation results obtained in MATLAB/Simulink demonstrate that increasing the scaling gain leads to substantial reductions in rise time, settling time, and tracking error. Quantitative performance indices such as IAE, ITAE, MSE, RMSE, and AMSE confirm the effectiveness of the proposed strategy. The results highlight the suitability of symmetric gain-scaled funnel control for practical wind energy applications operating under uncertainty and variable wind conditions