# Service Life Prediction and Life Cycle Costs of Light Weight Partitions
## 2 Background
explore the use of deep learning and regression algorithms to automate anomaly detection and predict building degradation, demonstrating the potential of AI to optimize the building of life cycle management [20]. [PERSON] et al. discuss combining ANN with ISO 15686 [21] to achieve more reliable forecasting of building deterioration, and focus on the relationship between factors affecting the service life of buildings, the value of building conditions, and the level of degradation. They seek to provide a more accurateprediction of building component age and degradation level, with the aim of contributing to better service life planning and maintenance strategies [22]. [PERSON] et al. introduce a mathematical model, using ANN to predict the service life of painted exterior surfaces by drawing on data from 160 buildings in Lisbon, Portugal. The model, informed by various degradation agents, estimates the mean service life of these surfaces by incorporating detailed factors that account for variations in degradation, and then offers values consistent with existing perceptions of the durability of painted coatings. This model aims to enhance economic and environmental performance and maintenance plans, contributing to more informed decisions in the design and construction stages [23]. [PERSON] and [PERSON] provide a case study of school buildings that demonstrates how artificial neural networks can be used in conjunction with life cycle cost analysis to optimize building retrofitting strategies [24]. Further extending this discussion, Building Information Modeling (BIM) has emerged as a powerful tool in this realm, whose ability to model structural and non-structural components provides a comprehensive life cycle assessment and cost analysis platform. Its capacity to model material properties and incorporate relevant data presents opportunities to employ it in life cycle assessment and life cycle cost analysis [25; 26; 27; 28; 29]. For instance, [PERSON] et al. (2020) use it to explore how LCA and LCC integration can be enhanced, proposing an automatic analysis within a BIM environment and demonstrating its utility through a case study conducted in the Netherlands, with the aim of advancing automatic sustainability simulations and predictive analyses in construction [30]. A study by [PERSON] et al. (2021) focuses on integrating LCC into BIM to optimize the life-cycle costs and resilience of building design, demonstrating that investing in resilience can significantly reduce life-cycle costs and improve building durability, particularly in earthquake-prone areas [31]. In the context of climate change, optimizing the life cycle management of buildings is critical to ensure sustainability and resilience. [PERSON] et al. (2020) discuss the importance of service life prediction for building component design and maintenance, with reference both to the importance of adjusting SLP to climate change, and using the life cycle assessment to analyze the embodied energy in buildings [32]; their findings highlight the need to not only consider the superstructure but also the floor structures in multistorey buildings, for the reason that the latter significantly influences the embodied impact [32]. Further, [PERSON] and [PERSON] (2011) employ life cycle assessment techniques to evaluate the embodied energy and environmental impacts of clay brick, hollow concrete block, and timber frame partition walls, finding timber study walls to be the most environmentally friendly, leading them to advocate the reduction of embodied energy in clay brick walls to improve sustainability [33]. The life cycle of building components significantly impacts the performance of critical infrastructures, particularly in extreme events, and can affect design and maintenance strategies. Proper maintenance plays a crucial role in reducing risks and enhancing the resilience and reliability of these structures, both under normal conditions and in extreme events [34].