Abstracts – Browse Results
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Adeyeye, K (2024) From product to service – strategies for upscaling smart home performance monitoring. Building Research & Information, 52(01–02), 107–28.
Al-Aomar, R, AlTal, M and Abel, J (2024) A data-driven predictive maintenance model for hospital HVAC system with machine learning. Building Research & Information, 52(01–02), 207–24.
- Type: Journal Article
- Keywords: Predictive modelling; maintenance planning; machine learning; building management; air handling unit;
- ISBN/ISSN: 0961-3218
- URL: https://doi.org/10.1080/09613218.2023.2206989
- Abstract:
Corrective and preventive maintenance strategies are typically employed to maintain an efficient functionality of different facility systems. This entails the evaluation of current conditions and the prediction of future conditions. Such prediction is highly needed for critical building systems such as Heating, Ventilation, and Air Conditioning (HVAC) of hospitals to maintain their functionality and extend their lifetime. Current literature highlights the benefits of adopting machine-learning algorithms for predictive modelling. Literature also reveals a gap in predictive modelling based on real-time sensor data and the prediction of both short-term and long-term future conditions. This paper presents a data-driven predictive maintenance model of a hospital’s HVAC system with a focus on the Air Handling Units (AHUs). The developed model adopts machine-learning using the sensor data acquired by the BMS and the database of the hospital’s CMMS. Support Vector Machine (SVM), Decision Trees (DT), and K-Nearest Neighbours (KNN) algorithms are used for the prediction of AHU’s short-term conditions. Prophet Forecasting and Seasonal Auto-Regressive Integrated Moving Average (SARIMA) algorithms are then used to predict the AHU’s long-term future conditions. The study also highlights the benefits of adopting the proposed model in terms of reduced maintenance cost and improved operational effectiveness of hospital AHUs.
Božiček, D, Almezeraani, Y and Košir, M (2024) Making sense of LCA results when evaluating multiple building designs – comparison of interpretation concepts. Building Research & Information, 52(01–02), 129–47.
Calcerano, F, Thravalou, S, Martinelli, L, Alexandrou, K, Artopoulos, G and Gigliarelli, E (2024) Energy and environmental improvement of built heritage: HBIM simulation-based approach applied to nine Mediterranean case-studies. Building Research & Information, 52(01–02), 225–47.
Ghansah, F A, Owusu-Manu, D, Edwards, D J, Thwala, W D, Yamoah Agyemang, D and Ababio, B K (2024) A framework for smart building technologies implementation in the Ghanaian construction industry: a PLS-SEM approach. Building Research & Information, 52(01–02), 148–63.
Kalla, M, Kalaycioglu, O, Hecht, R, Schneider, S and Schmidt, C (2024) Station biophilia – assessing the perception of greenery on railway platforms using a digital twin. Building Research & Information, 52(01–02), 164–80.
Kumari, P, Reddy, S R N and Yadav, R (2024) Indoor occupancy detection and counting system based on boosting algorithm using different sensor data. Building Research & Information, 52(01–02), 87–106.
Lai, H and Chiang, W (2024) Generative design of terraced concert hall – a case study of Taipei music and library centre. Building Research & Information, 52(01–02), 49–67.
Liang, H, Weng, Y, Tang, S W Y and Yeoh, J K W (2024) Automated filtering of façade defect images using a similarity method for enhanced inspection documentation. Building Research & Information, 52(01–02), 194–206.
Prieto, A J, Torres-González, M and Carpio, M (2024) Virtual web-based instruments in the evaluation of functional degradation of heritage timber buildings. Building Research & Information, 52(01–02), 181–93.
Saeidlou, S and Ghadiminia, N (2024) A construction cost estimation framework using DNN and validation unit. Building Research & Information, 52(01–02), 38–48.
Yıldız, B, Çağdaş, G and Zincir, I (2024) Architectural space classification considering topological and 3D visual spatial relations using machine learning techniques. Building Research & Information, 52(01–02), 68–86.
Yang, X, Zhong, H, Wang, Z, Du, P, Zhou, K, Zhou, H, Lai, X, Lau, Y L, Song, Y and Tang, L (2024) BEKG: A built environment knowledge graph. Building Research & Information, 52(01–02), 19–37.
Zhou, S ( (2024) Platforming for industrialized building: a comparative case study of digitally-enabled product platforms. Building Research & Information, 52(01–02), 4–18.