# Service Life Prediction and Life Cycle Costs of Light Weight Partitions
## 2 Background  
The service life expectancy of building components plays a crucial role in ensuring the longevity, sustainability, and cost-effectiveness of constructed facilities. Predicting and understanding the factors influencing component service life facilitates informed design, construction, and maintenance decisions, leading to optimized building performance. Several studies have conducted research in this area and have focused on various aspects of building component durability and deterioration patterns. [PERSON] and [PERSON] conducted comprehensive research of the service life prediction of exterior cladding components in standard conditions and failure conditions [5], which included monitoring the physical and visual performance of building components. A recent study by [PERSON] et al. examines the deterioration patterns and life expectancy of exterior stone claddings, comparing dry-fixed and wet-fixed methods in both standard and marine environments. This study incorporated laboratory tests, field surveys, and probabilistic service life prediction methods, leading to statistical prediction of exterior stone cladding service life [6]. Furthermore, a study by [7] found that different mineral admixtures significantly influence the concrete's durability and can increase its life cycle expectancy. Moreover, research by [PERSON] et al. delved into the impact of maintenance on the service life of painted renderings, providing insight into the effectiveness of different maintenance strategies [8; 9]. Additionally, a study by [PERSON] et al. evaluated ten interior partition wall solutions in Spain by undertaking a comprehensive life cycle and eco-efficiency analysis [10]. [PERSON] et al. (2021) provided a multi-criteria analysis of insulated concrete wall technologies and wood-frame walls in residential buildings, focusing on optimizing building envelope-based life cycle cost and decreasing environmental impacts [11]. Furthermore, studies have, in engaging with building design and materials, developed critical aspects of life cycle assessment (LCA) and life cycle cost (LCC). [PERSON] et al. (2020) address the optimization of insulation thickness from a life-cycle cost perspective, focusing on the balance between energy efficiency and cost-effectiveness in building materials [12]. [PERSON] et al. (2014) conduct a thorough life cycle assessment and life cycle cost analysis of a typical Australian house, providing a detailed economic and environmental impact analysis to guide sustainable building design [13]. [PERSON] et al. (2012) combine life cycle assessment with energy simulation and optimization analysis to reduce building envelope environmental impact and life cycle energy demand [14]. [PERSON] et al. (2016) propose a comprehensive life cycle cost model that incorporates risk factors, enhancing the understanding and management of costs throughout the building life cycle [15]. [PERSON] et al. (2013) add to this by exploring the interaction between building materials and environmental factors, identifying various environmental zones in Malaysia that can be used to enhance design and maintenance strategies that will improve the durability and service life prediction of building components [16]. These studies emphasize the importance of LCA and LCC, providing valuable insights to sustainable and cost-effective building design and maintenance practices. The application of AI algorithms, such as Artificial Neural Networks (ANN) and Machine Learning (ML), has been increasingly recognized to enhance understanding and prediction in various engineering domains [17; 18; 19], and contribute to the prediction of service life expectancy and life cycle assessment costs. Despite this, few studies have integrated AI algorithms with the aim of achieving a more accurate projection of the service life and deterioration of building components, along with their service life of building components. [PERSON] et al.