Self-learning and Self-repairing Technologies to Establish Autonomous Building Maintenance

Cortiços, Nuno Dinis

doi:10.1051/matecconf/201927804004

Cited by 6 publications

(2 citation statements)

References 19 publications

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“…The self-learning aspect of this building refers to the ability of this building to adapt and learn from all sorts of aspects of its environment [7]. For example, if we put this building in a certain place, the intelligent system would be able to study collect information about the culture of that place and adapt itself to that culture [8].…”

Section: Introductionmentioning

confidence: 99%

Self-learning Buildings: integrating Artificial Intelligence to create a building that can adapt to future challenges

Maksoud

Al-Beer

Mushtaha

et al. 2022

IOP Conf. Ser.: Earth Environ. Sci.

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Adaptability is a crucial quality in nature, and Artificial Intelligence (AI) provides leverage for adaptability in Architecture. In this paper, AI is integrated to create Self-learning buildings that can adapt to future challenges. The aim of this study is to make buildings that collect data from their environment through sensors and adapt themselves according to these data. The approach followed in this study is divided into different phases. Phase 1 starts by making an extensive research on the use of AI in Architecture. The data that was gathered from that research in phase 1 was used as guidelines to design the building in phase 2. The design of the building that is in phase 2 follows a parametric approach with the help of machine learning in the form of computational design tools. An algorithm was designed with Rhino modeling & Grasshopper Scripting to generate forms that not only biomimicks the Coral Growth process but also adapt that form to the selected site of the project. Phase 3 shows the selection process for the generated experimental studies. Multiple analyses were made such as sunlight, radiation, and shadow analysis to select the best performing form in terms of energy use. In phase 4, the form is developed to increase the building’s performance. In phase 5, performance analyses are done to prove that resultant form is a climate or environmentally responsive form which have high levels of adaptability. The analysis showed that the radiation exposure of this building is between 200 and 300 kWh/m². The shadow analysis shows the building form provides a shadow length of 8 hours. The analyses proves that the building’s form reduces its energy use thus makes it adaptable. In the last phase, an AI engine system is used to predict the future expansion of the building. Integrating technology in the architecture of future buildings provides adaptable buildings and helps save some of the energy used by buildings and thus build a sustainable planet.

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Section: Introductionmentioning

confidence: 99%

Self-learning Buildings: integrating Artificial Intelligence to create a building that can adapt to future challenges

Maksoud

Al-Beer

Mushtaha

et al. 2022

IOP Conf. Ser.: Earth Environ. Sci.

View full text Add to dashboard Cite

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“…The 21st century is characterized by an increasing rationalization of the use of natural resources [1]. The highest materials exploitation is noticeable, among others, in the Architectural, Construction, and Engineering sector [2]. According to sustainable development, postulates and regulations are being developed to significantly improve design and construction qualities and counteract climate changes [3].…”

Section: Introductionmentioning

confidence: 99%

Geometrical transformations of reticulated pavilion canopies as a method for structural optimization

2020

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The growing need for rational use of natural resources becomes crucial in all sectors of the current economy. Minimizing the use of materials in construction is becoming an important design aspect. One of the interdisciplinary design methods is geometry optimization, which, thanks to modern computational methods, brings the desired material reduction in constructions. The article examines the impact of using the form-finding method on the example of preliminary geometrical optimization of the reticulated structures. The influence of the dynamic relaxation and geometrical transformations in structure optimization and natural resource minimization is examined. The article aims to indicate the impact of optimization aspects on obtaining efficient load-bearing structures. The research was carried out on the example of a small-scale canopy. Case-studies compare fifteen different geometries generated with the use of mathematical and parametric formulas. The study’s conclusions indicate that interdisciplinary tools supporting the process of the initial design phase help in architectural design.

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AI-big data analytics for building automation and management systems: a survey, actual challenges and future perspectives

et al. 2022

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In theory, building automation and management systems (BAMSs) can provide all the components and functionalities required for analyzing and operating buildings. However, in reality, these systems can only ensure the control of heating ventilation and air conditioning system systems. Therefore, many other tasks are left to the operator, e.g. evaluating buildings’ performance, detecting abnormal energy consumption, identifying the changes needed to improve efficiency, ensuring the security and privacy of end-users, etc. To that end, there has been a movement for developing artificial intelligence (AI) big data analytic tools as they offer various new and tailor-made solutions that are incredibly appropriate for practical buildings’ management. Typically, they can help the operator in (i) analyzing the tons of connected equipment data; and; (ii) making intelligent, efficient, and on-time decisions to improve the buildings’ performance. This paper presents a comprehensive systematic survey on using AI-big data analytics in BAMSs. It covers various AI-based tasks, e.g. load forecasting, water management, indoor environmental quality monitoring, occupancy detection, etc. The first part of this paper adopts a well-designed taxonomy to overview existing frameworks. A comprehensive review is conducted about different aspects, including the learning process, building environment, computing platforms, and application scenario. Moving on, a critical discussion is performed to identify current challenges. The second part aims at providing the reader with insights into the real-world application of AI-big data analytics. Thus, three case studies that demonstrate the use of AI-big data analytics in BAMSs are presented, focusing on energy anomaly detection in residential and office buildings and energy and performance optimization in sports facilities. Lastly, future directions and valuable recommendations are identified to improve the performance and reliability of BAMSs in intelligent buildings.

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Self-learning and Self-repairing Technologies to Establish Autonomous Building Maintenance

Cited by 6 publications

References 19 publications

Self-learning Buildings: integrating Artificial Intelligence to create a building that can adapt to future challenges

Self-learning Buildings: integrating Artificial Intelligence to create a building that can adapt to future challenges

Geometrical transformations of reticulated pavilion canopies as a method for structural optimization

AI-big data analytics for building automation and management systems: a survey, actual challenges and future perspectives

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