Plenary lectures

Prof. Guochang Li, Dean of the Faculty of Civil Engineering at Shenyang Jianzhu University,
Invited Lecture: TBA

Abstract of the presentation:  TBA

Assoc. Prof. Emmanuel Karlo Nyarko, Josip Juraj Strossmayer University of Osijek, Croatia
Invited Lecture: Concrete Data, Intelligent Predictions: ML for Smarter Structures

Abstract of the presentation:  This presentation deals with the application of advanced machine learning techniques in construction and focuses on their role in predicting material strength, wall load-bearing capacity and seismic vulnerability of buildings. Based on the interdisciplinary project "Intelligent Methods for Structures, Elements and Materials", details on the selection and implementation of different ML models — such as regression algorithms, ensemble methods and neural networks — tailored to specific engineering challenges will be explained. The presentation will give an insight into the rationale behind the chosen approaches, the integration of expertise and the practical results achieved, as well as possible limitations and how intelligent systems can improve prediction accuracy and support evidence-based design and safet

Prof. Ashok Vaseashta, International Institute for Clear Water, Manassas, USA
Invited Lecture: Integrating Artificial Intelligence (AI) and Smart Materials into critical infrastructures

Abstract of the presentation:  Integrating Artificial Intelligence (AI) into critical infrastructures—such as power grids, water systems, transportation networks, and healthcare facilities—marks a significant advancement in the management and resilience of essential services. AI technologies, in conjunction with smart materials, offer powerful tools for real-time data analysis, predictive maintenance, automated decision-making, and rapid anomaly detection, enabling infrastructure operators to optimize performance, reduce operational costs, and respond swiftly to disruptions. For example, AI-driven predictive analytics can anticipate equipment failures before they occur, while intelligent control systems can dynamically adjust resource allocation in response to fluctuating demand or emerging threats. Despite these benefits, the integration of AI also introduces new complexities and risks. Increased reliance on automated systems can create vulnerabilities to cyberattacks, data breaches, and unintended system behaviors. Furthermore, ethical considerations—such as transparency, accountability, and fairness—must be addressed to ensure public trust and regulatory compliance. The successful integration of AI in critical infrastructures requires a multidisciplinary approach that combines technical innovation with robust governance, continuous risk assessment, and stakeholder collaboration. By carefully balancing innovation with security and ethical oversight, AI can significantly enhance the reliability, efficiency, and adaptability of critical infrastructures, ultimately supporting the safety and well-being of society as a whole.

Prof. Baoxin Qi,  School of Civil Engineering, Shenyang Jianzhu University, Liaoning Shenyang, China
Invited Lecture: TBA

Abstract of the presentation:  TBA

Prof. Ercan Işik,  Civil Engineering Department in Bitlis Eren University, Türkiye
Invited Lecture:  A Study on Comparative Analysis of RC Structures Before and After 2023 Kahramanmaraş Earthquakes
Abstract of the presentation:  This presentation aims to examine the effects of the Kahramanmaraş earthquakes that occurred on February 6, 2023 on reinforced concrete structures and to reveal the weaknesses of the structures and the reasons for their low seismic performance. For this purpose, reinforced concrete structures that suffered damage at different levels as a result of field observations were examined and compared by taking into account their pre-earthquake visuals. The comparisons were evaluated within the scope of earthquake and civil engineering. For this purpose, 50 different reinforced concrete structures were taken into account. Structural parameters, irregularities and damage levels were classified for all examined structures. In addition, the study tried to reveal the usability of rapid assessment methods by applying the 2019 Turkish Rapid Assessment Method to these structures.
Prof. Zhijian Yang,  School of Civil Engineering, Shenyang Jianzhu University, Liaoning Shenyang, China
Invited Lecture: TBA

Abstract of the presentation:  TBA

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