Hybrid aI-based sensor optimization for structural health monitoring of multi-story buildings — case study / Mohamed Boukharouba (2025)

Public ISBD Titre : Hybrid aI-based sensor optimization for structural health monitoring of multi-story buildings — case study : HQ tower R+12 Type de document : document électronique Auteurs : Mohamed Boukharouba, Auteur ; Abdelmadjid Tadjadit, Directeur de thèse Editeur : [S.l.] : [s.n.] Année de publication : 2025 Importance : 1 fichier PDF (8.6 Mo) Présentation : ill. Note générale : Mode d'accès : accès au texte intégral par intranet. Mémoire de Projet de Fin d’Études : Génie Civil : Alger, École Nationale Polytechnique : 2025 Bibliogr. p. 92-94 Langues : Anglais (eng) Mots-clés : Structural health monitoring (SHM) Sensor placement optimization Ge-netic algorithm Distance matrix Damage-adaptive sensing Index. décimale : PC00625 Résumé : Structural Health Monitoring (SHM) plays a critical role in ensuring the safety and func- tionality of vital structures such as bridges, dams, and public buildings. To make SHM sys- tems more cost-effective, it is essential to optimize the number and placement of sensors, reducing implementation costs while maintaining reliable damage detection and structural assessment. This study addresses the problem of optimizing damage-adaptive sensor layout in struc- tural health monitoring (SHM) for multi-story buildings. A three-dimensional finite ele- ment model of a 12-story reinforced concrete tower (R+12) was developed using SAP2000, enabling the identification of high-risk damage zones based on internal force distributions. Steady-state vibration responses were generated, and a genetic algorithm was used to iden- tify the optimal sensor configuration for each damage scenario using distance matrices as damage-sensitive features. These scenario-based layouts were then merged into a unified configuration by analyzing sensor occurrence and importance scores. The final sensor set ensures sufficient coverage and sensitivity to structural degradation while maintaining a reduced number of sensors. The proposed approach provides a scalable and practical solution for SHM system design in complex structures with anticipated damage regions. Hybrid aI-based sensor optimization for structural health monitoring of multi-story buildings — case study : HQ tower R+12 [document électronique] / Mohamed Boukharouba, Auteur ; Abdelmadjid Tadjadit, Directeur de thèse . - [S.l.] : [s.n.], 2025 . - 1 fichier PDF (8.6 Mo) : ill. Mode d'accès : accès au texte intégral par intranet. Mémoire de Projet de Fin d’Études : Génie Civil : Alger, École Nationale Polytechnique : 2025 Bibliogr. p. 92-94 Langues : Anglais (eng) Mots-clés : Structural health monitoring (SHM) Sensor placement optimization Ge-netic algorithm Distance matrix Damage-adaptive sensing Index. décimale : PC00625 Résumé : Structural Health Monitoring (SHM) plays a critical role in ensuring the safety and func- tionality of vital structures such as bridges, dams, and public buildings. To make SHM sys- tems more cost-effective, it is essential to optimize the number and placement of sensors, reducing implementation costs while maintaining reliable damage detection and structural assessment. This study addresses the problem of optimizing damage-adaptive sensor layout in struc- tural health monitoring (SHM) for multi-story buildings. A three-dimensional finite ele- ment model of a 12-story reinforced concrete tower (R+12) was developed using SAP2000, enabling the identification of high-risk damage zones based on internal force distributions. Steady-state vibration responses were generated, and a genetic algorithm was used to iden- tify the optimal sensor configuration for each damage scenario using distance matrices as damage-sensitive features. These scenario-based layouts were then merged into a unified configuration by analyzing sensor occurrence and importance scores. The final sensor set ensures sufficient coverage and sensitivity to structural degradation while maintaining a reduced number of sensors. The proposed approach provides a scalable and practical solution for SHM system design in complex structures with anticipated damage regions.

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Modeling and simulation of hydrogen production processes using aspen plus / Ibrahim Elkhalil Ferroudj (2025) 

Public ISBD Titre : Modeling and simulation of hydrogen production processes using aspen plus Type de document : document électronique Auteurs : Ibrahim Elkhalil Ferroudj, Auteur ; Ammar Selatnia, Directeur de thèse Editeur : [S.l.] : [s.n.] Année de publication : 2025 Importance : 1 fichier PDF (2 Mo) Note générale : Mode d'accès : accès au texte intégral par intranet. Mémoire de Projet de Fin d’Études : Génie Chimique : Alger, École Nationale Polytechnique : 2025 Bibliogr. p. 122-131. - Annexes Mémoire confidentiel 5 ans jusqu'à Juin 2030 Langues : Anglais (eng) Mots-clés : Hydrogen production Syngas Electrolysis SOEC AEC PEM Methane Steam Biomass Gasification Energy efficiency Carbon utilization Index. décimale : PC00625 Résumé : This thesis addresses the critical need for sustainable energy by exploring diverse hydrogen and syngas production methods. Confronting renewable energy intermittency and fossil fuel environmental impacts, the study models and comparatively analyzes Solid Oxide (SOEC), Alkaline (AEC), and Proton Exchange Membrane (PEM) Electrolysis, Methane Steam Reforming (MSR), and Biomass Gasification. Using Aspen Plus, detailed thermodynamic and kinetic models were developed and validated. The research assesses energy efficiency, carbon utilization, economic feasibility, and environmental impact. Findings underscore the potential of integrated systems to boost efficiency and cut CO2 emissions, advancing robust and sustainable energy conversion and storage strategies. Modeling and simulation of hydrogen production processes using aspen plus [document électronique] / Ibrahim Elkhalil Ferroudj, Auteur ; Ammar Selatnia, Directeur de thèse . - [S.l.] : [s.n.], 2025 . - 1 fichier PDF (2 Mo). Mode d'accès : accès au texte intégral par intranet. Mémoire de Projet de Fin d’Études : Génie Chimique : Alger, École Nationale Polytechnique : 2025 Bibliogr. p. 122-131. - Annexes Mémoire confidentiel 5 ans jusqu'à Juin 2030 Langues : Anglais (eng) Mots-clés : Hydrogen production Syngas Electrolysis SOEC AEC PEM Methane Steam Biomass Gasification Energy efficiency Carbon utilization Index. décimale : PC00625 Résumé : This thesis addresses the critical need for sustainable energy by exploring diverse hydrogen and syngas production methods. Confronting renewable energy intermittency and fossil fuel environmental impacts, the study models and comparatively analyzes Solid Oxide (SOEC), Alkaline (AEC), and Proton Exchange Membrane (PEM) Electrolysis, Methane Steam Reforming (MSR), and Biomass Gasification. Using Aspen Plus, detailed thermodynamic and kinetic models were developed and validated. The research assesses energy efficiency, carbon utilization, economic feasibility, and environmental impact. Findings underscore the potential of integrated systems to boost efficiency and cut CO2 emissions, advancing robust and sustainable energy conversion and storage strategies.

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