The School of Pedagogical and Technological Education (Greek: Ανώτατη Σχολή Παιδαγωγικής και Τεχνολογικής Εκπαίδευσης) or ASPETE (Α.Σ.ΠΑΙ.Τ.Ε.) is a Greek University which specializes in training teachers.Ε.Ε.Τ.Ε.
This study developed machine learning models to predict the swelling pressure (PS) and swelling potential (SP) of fibre-reinforced expansive soils using a compiled dataset of 187 experimental specimens collected from published literature. The input variables included fibre characteristics (length, aspect ratio, content, tensile strength), soil consistency and compaction parameters (liquid limit, plasticity index, fine content, maximum dry density, optimum moisture content), and swelling characteristics of untreated soil (PS0 and SP0). Tree-based gradient boosting models and artificial neural networks were implemented and evaluated using an 80
The present work introduces an accurate computational model by comparing Artificial Bee Colony (ABC), Genetic (GA), Grey Wolf (GWO), Harris Hawks (HHO), Particle Swarm (PSO), and Salp Swarm (SSO) optimized least squares support vector machine (LSSVM) models to estimate the bearing capacity of reinforced concrete (RC) piles (PU). Based on a substantial dataset, key geotechnical and structural parameters were identified as both highly influential and multicollinear. The Particle Swarm Optimization-based model demonstrated superior performance (variance accounted for = 95.49, root mean square error = 70.801, correlation = 0.9858, and mean absolute error = 51.521) compared to other hybrid variants, demonstrating enhanced robustness and generalization. Analyses revealed that most alternative models exhibited either underfitting or overfitting, primarily due to problematic multicollinearity among critical input features. This research presents a novel analysis of how feature multicollinearity affects model-fitting behavior in geotechnical predictive modeling.
This study develops an improved hybrid ensemble learning framework for estimating the compressive strength (CS) of Fly Ash-blended green concrete. Specifically, a gradient boosting regressor (GBR) and an improved grey wolf optimiser (IGWO) were used to construct an efficient hybrid ensemble paradigm, GBR-IGWO. The outcomes of the proposed GBR-IGWO framework were compared with four ensemble learning algorithms, two standard models, and three neural network models. The developed GBR-IGWO achieved the highest accuracy with R2 of 0.9965 and 0.9632 in the training and testing stages, respectively. The influence of cement replacement on the compressive strength of green concrete was further investigated at different curing ages to evaluate the generalisation capability of the proposed framework. Separate laboratory experiments were also performed to validate the proposed model. A graphical user interface (GUI) was developed for quick estimation of CS of Fly Ash-blended green concrete. The developed GUI can be used to predict the CS using different input parameters, namely content of aggregates, cement, and fly ash, percentage of cement replacement, water-binder ratio, age, and the chemical compositions of fly ash, i.e. calcium oxide (CaO) and silicon dioxide (SiO2). The web version of the GUI can be accessed from .
Geotechnical engineering has been a critical aspect of construction and infrastructure throughout human history, enabling ancient civilizations to develop impressive structures. Their ability to select appropriate sites, to use suitable materials, and to employ innovative construction techniques allowed them to create structures that have not only endured for millennia but continue to inspire modern engineering. One notable example is the use of Mechanically Stabilized Earth (MSE) systems in antiquity, which involved reinforcing soil with various materials to improve strength and stability. While modern MSE systems typically incorporate synthetic materials such as geotextiles, the core principle of soil reinforcement has ancient origins. This study examines several geotechnical structures from antiquity that utilized MSE techniques, showcasing the technological achievements of past civilizations and providing insight into their geotechnical expertise. A particular focus is placed on sections of the Great Wall of China, specifically those constructed during the Han Dynasty, which incorporated a form of soil reinforcement. To assess the effectiveness of these historical MSE systems, computational / numerical models were developed using the Bishop Analysis for slope stability and the Finite Element Stress Analysis Method. Additionally, the study evaluates the structural vulnerability of these constructions in the absence of MSE systems, highlighting their critical role in stability and longevity. The findings from the numerical simulations are presented and discussed, offering valuable perspectives on the contributions of ancient geotechnical engineering practices.
this study presents a comparative analysis of secondary teachers' training in England and Greece. It examines the impact of school infrastructure and funding on secondary science and electronics education. Greek secondary education is highly centralized, emphasizing theoretical knowledge with limited practical and laboratory experiences, particularly in rural areas, while teachers' training courses focuses on disciplinary expertise with minimal classroom practice. In contrast, England integrates theoretical and practical learning, providing well-equipped laboratories, handson experimentation, and structured teacher training through PGCE programs. Comparative analyses reveal that Greece's limited funding and infrastructure constraints contribute to lower student outcomes, whereas England's higher investment supports experiential learning and higher performance in international assessments such as PISA. Findings highlight the critical role of teacher preparation, curriculum design, and resource allocation in shaping students' competencies in science and electronics.
