Predictive and Optimised Life Cycle Management Buildings and Infrastructure
Coordonnateur : Sarja Asko
Predictive and Optimised Life-Cycle Management sets out methodologies to meet the demands of the current trend towards sustainable civil engineering and building.
Encompassing all aspects of construction practice, from design through to demolition and the recycling of materials, Sarja provides tools for optimal property-value protection, including a description of an integrated and predictive Life-Cycle Maintenance and Management Planning System (LMS), which employs a wide range of techniques.
Clear and practical, this guide provides effective methodology required to change a reactive system of management to a predictive one, which will benefit practitioners and students involved in construction, from the architect to local and government authorities; from design engineers to facility managers.
1. Introduction 2. Theory, Systematics and Methods 3. Durability Control Methodology 4. Condition Assessment Protocol 5. Life Cycle Management Process 6. Visions for Future Developments 7. Terms and Conditions
Asko Sarja worked as Research Professor in Structural Engineering at Technical Research Centre of Finland (VTT), until his retirement at the end of 2004.
Date de parution : 06-2020
17.4x24.6 cm
Date de parution : 06-2006
Ouvrage de 656 p.
17.4x24.6 cm
Thèmes de Predictive and Optimised Life Cycle Management :
Mots-clés :
lifetime; engineering; target; service; quality; integrated; civil; structures; modelling; Network Level Optimisation; design; Component Level Data; Target Service Life; Lifetime Quality; Maximum Allowable Probability; Condition State Distribution; Service Life Modelling; Linear Optimisation Method; Lifetime Engineering; Sustainable Building; Bridge Management System; LCC Analysis; Design Service Life; Lifetime Safety Factor; Life Cycle; Object Level System; Life Cycle Planning; ELECTRE Iii; Short Term Optimisation; Network Level Analyses; Markov Chain Method; TLC; Network Level System; Condition Assessment Data; Degradation Models