Self-learning and self-repairing technologies to establish autonomous building maintenance

Abstract

Current maintenance models, applied to the building stock, struggle with the complexity and high costs associated with the necessary interventions to recover the original condition or repair the previous renovation works, related with the workers' experience, site conditions (logistic, climatic and environmental), technical skills, and experts' backgrounds. Thus, circumstances favor the introduction of monitoring by sensors, in active systems (ventilation, acclimatization, surveillance, fire-protection, et cetera), increasing efficiency and reducing operating costs. These goals are aligned with current requirements for passive construction solutions, considering the capacities of sensors and nano-technologies. All supported by Artificial Intelligence (AI), and its ability to learn and react, by monitoring the aging rate and external conditions impact on performance and conditioning the settings of renovation construction materials' and solutions' (future) composition. Nano-Technologies already proved their potential to transform the passive systems infinite structures, in traditional construction materials and solutions. By changing those into a semi-passive condition, able to react and adjust to adverse externalities slowing and/or inverting the performance losses. The research hypothesis an ecosystem to produce autonomous maintenance on buildings, through a digital condition assessment, on the actual system's components, with resort to nano-technology to reset those and trigger self-repairing; to extend properties life-cycle and lifespan, and improve efficiency to maintain high performance, favoring the user's experience.