Review
Energy & Fuels
Zhelun Chen, Zheng O'Neill, Jin Wen, Ojas Pradhan, Tao Yang, Xing Lu, Guanjing Lin, Shohei Miyata, Seungjae Lee, Chou Shen, Roberto Chiosa, Marco Savino Piscitelli, Alfonso Capozzoli, Franz Hengel, Alexander Kuehrer, Marco Pritoni, Wei Liu, John Clauss, Yimin Chen, Terry Herr
Summary: This paper reviews and summarizes the literature on data-driven fault detection and diagnostics (FDD) for building HVAC systems, focusing on the process, systems studied, and evaluation metrics. It identifies challenges such as real-building deployment, performance evaluation, scalability, interpretability, cyber security, data privacy, and user experience that data-driven FDD methods still face despite promising performance reported in the literature.
Review
Energy & Fuels
William Nelson, Charles Culp
Summary: Energy consumption is a significant cost in building operations, and faults can lead to increased energy consumption and reduced thermal comfort. Recent advancements in automated fault detection and diagnostics, as well as machine learning algorithms, offer opportunities for more accurate results. However, there are still obstacles to overcome for widespread adoption in commercial and scientific domains.
Article
Construction & Building Technology
Jian Sun, Teja Kuruganti, Brian Fricke, Shenglan Xuan, Yanfei Li, Wayne Wilkerson, Carlos Cunningham
Summary: The building sector is the highest energy consumer and is prioritizing decarbonization and electrification. IoT is identified as a fundamental technology, but there is a lack of reliable, scalable, and affordable IoT-based automated fault and degradation diagnostic (AFDD) solutions.
JOURNAL OF BUILDING ENGINEERING
(2022)
Article
Construction & Building Technology
Guanjing Lin, Marco Pritoni, Yimin Chen, Raphael Vitti, Christopher Weyandt, Jessica Granderson
Summary: This research presents the development, implementation, and field testing of an automated control hunting fault correction algorithm based on lambda tuning open-loop rules. The algorithm was developed in a commercial FDD software and successfully tested among nine variable air volume boxes in an office building in the United States. The paper demonstrates the feasibility of using FDD tools to automatically correct control hunting faults, discusses scalability considerations, and proposes a path forward for the HVAC industry and academia to further improve this technology.
ENERGY AND BUILDINGS
(2023)
Article
Computer Science, Interdisciplinary Applications
Herve Pruvost, Andreas Wilde, Olaf Enge-Rosenblatt
Summary: Modern buildings require efficient operation of complex technical equipment, and users and building managers need guidance to avoid faults and energy waste. This study proposes an expert system that provides analysis and advice, which can be implemented on a larger scale.
JOURNAL OF COMPUTING IN CIVIL ENGINEERING
(2023)
Review
Thermodynamics
Vijay Singh, Jyotirmay Mathur, Aviruch Bhatia
Summary: This review study focuses on the latest research and developments in fault detection and diagnostics (FDD) of Heating Ventilation and Air Conditioning (HVAC) systems. The basics of FDD and the methods developed for it are discussed, with emphasis on the use of machine learning techniques. The paper also covers fault prognosis, fault modeling, and provides a comparative study of different FDD methods. Future challenges and the importance of more efficient FDD systems in reducing energy consumption are also discussed.
INTERNATIONAL JOURNAL OF REFRIGERATION
(2022)
Article
Construction & Building Technology
Marco Pritoni, Guanjin Lin, Yimin Chen, Raphael Vitti, Christopher Weyandt, Jessica Granderson
Summary: This paper presents a field study on the implementation of fault auto-correction algorithms in commercial FDD platforms. Through testing in multiple buildings and different systems, it successfully achieves automatic fault correction and improves the operation of HVAC systems.
BUILDING AND ENVIRONMENT
(2022)
Article
Construction & Building Technology
Yuan Gao, Shohei Miyata, Yasunori Akashi
Summary: This study uses knowledge distillation to reduce the number of model parameters and deployment cost while maintaining the accuracy of automated fault detection and diagnosis. The experimental results show that the knowledge distillation algorithm achieves high accuracy in image classification.
BUILDING AND ENVIRONMENT
(2023)
Article
Construction & Building Technology
Athila Santos, Na Liu, Muhyiddine Jradi
Summary: The digital transformation enables new services and efficient management in the energy sector. Building Automation Control Systems (BACS) are proposed to mitigate building performance issues. Automated step response test tools are scarce in the building sector, leading to manual testing and resource allocation issues. Therefore, AUSTRET is introduced as a parallel automated software to enhance step response testing in buildings.
Review
Thermodynamics
Pedro Barandier, Antonio J. Marques Cardoso
Summary: Heat Pumps (HPs) market has seen significant growth in recent years, with predictions indicating a further increase of over 200% by 2030. This technology offers a more efficient solution for heating and cooling, and its dependence on renewable energy sources makes it even more important. To ensure the availability and reliability of HPs, proper Fault Detection and Diagnostics (FDD) is crucial. This study focuses on reviewing common faults in HPs and the diagnostic methods developed in the past 30 years, ranging from rule-based approaches to deep learning techniques. Refrigerant undercharge, which is the most investigated fault, can not only significantly degrade system performance but also indicate refrigerant leakage with potential environmental impacts depending on the type of refrigerant used.
APPLIED THERMAL ENGINEERING
(2023)
Article
Construction & Building Technology
Hyomun Lee, Eunho Kang, Dongsu Kim, Jongho Yoon
Summary: A data-driven model using an artificial neural network (ANN) was developed to estimate the maximum power point (MPP) of building-applied PV systems and validated using real-world data. The study demonstrates that the model can be practically applied to fault detection and diagnostics (FDD) with acceptable accuracy.
JOURNAL OF BUILDING ENGINEERING
(2023)
Review
Energy & Fuels
Ula Hijjawi, Subhash Lakshminarayana, Tianhua Xu, Gian Piero Malfense Fierro, Mostafizur Rahman
Summary: This paper provides a comprehensive review of different data analysis methods for defect detection in PV systems. The methods are categorized into imaging-based techniques (IBTs) and electrical testing techniques (ETTs). IBTs include infrared thermography, electroluminescence imaging, and light beam induced current, while ETTs include current-voltage characteristics analysis, earth capacitance measurements, time domain reflectometry, power losses analysis, and voltage and current measurements. The paper also critically analyzes the advantages and disadvantages of each method and discusses challenges related to data availability, real-time monitoring, accurate measurements, computational efficiency, and dataset distribution. The paper concludes with potential future directions for PV defect detection systems.
Review
Green & Sustainable Science & Technology
Jianli Chen, Liang Zhang, Yanfei Li, Yifu Shi, Xinghua Gao, Yuqing Hu
Summary: This paper comprehensively reviews the state-of-the-art computing-based fault detection and diagnosis (FDD) methods for HVAC systems in buildings. It classifies the reviewed methods into knowledge-based and data-driven approaches, discusses important topics such as data availability and quality, and identifies remaining challenges and future research directions for FDD development.
RENEWABLE & SUSTAINABLE ENERGY REVIEWS
(2022)
Review
Energy & Fuels
Liang Zhang, Matt Leach, Yeonjin Bae, Borui Cui, Saptarshi Bhattacharya, Seungjae Lee, Piljae Im, Veronica Adetola, Draguna Vrabie, Teja Kuruganti
Summary: A comprehensive literature review of over 100 FDD-sensor-related papers revealed a focus on FDD algorithms rather than sensors, with less attention on sensor hardware topics compared to software topics. Sensor engineering aspects are often neglected, and important sensor topics such as cost-effectiveness and sensor schema/layout/location are under-studied in the current research landscape.
ADVANCES IN APPLIED ENERGY
(2021)
Review
Energy & Fuels
Simon P. Melgaard, Kamilla H. Andersen, Anna Marszal-Pomianowska, Rasmus L. Jensen, Per K. Heiselberg
Summary: This review provides an overview of fault detection and diagnosis (FDD) in building systems, revealing that FDD for buildings is still in its early stages globally with inconsistent use of terminologies and definitions. A lack of data statements in reviewed articles could impact reproducibility and practical development in this field.
Article
Construction & Building Technology
Sara Gilani, William O'Brien
Summary: This study evaluates the potential for natural ventilation under climate change, finding that with warming temperatures, the usability of natural ventilation with rule-based control generally decreases, and improper window use by occupants may lead to overheating. In temperate climates, natural ventilation can significantly reduce cooling energy use, but the stochastic model suggests a reduction in energy saving rates.
BUILDING RESEARCH AND INFORMATION
(2021)
Review
Construction & Building Technology
Zakia Afroz, H. Burak Gunay, William O'Brien
ENERGY AND BUILDINGS
(2020)
Article
Construction & Building Technology
Max St-Jacques, Scott Bucking, William O'Brien
ENERGY AND BUILDINGS
(2020)
Review
Construction & Building Technology
William O'Brien, Fereshteh Yazdani Aliabadi
ENERGY AND BUILDINGS
(2020)
Review
Construction & Building Technology
Elie Azar, William O'Brien, Salvatore Carlucci, Tianzhen Hong, Andrew Sonta, Joyce Kim, Maedot S. Andargie, Tareq Abuimara, Mounir El Asmar, Rishee K. Jain, Mohamed M. Ouf, Farhang Tahmasebi, Jin Zhou
ENERGY AND BUILDINGS
(2020)
Article
Construction & Building Technology
Daniel Lowcay, H. Burak Gunay, William O'Brien
JOURNAL OF BUILDING PERFORMANCE SIMULATION
(2020)
Article
Thermodynamics
Brent Huchuk, William O'brien, Scott Sanner
SCIENCE AND TECHNOLOGY FOR THE BUILT ENVIRONMENT
(2020)
Article
Construction & Building Technology
Brent Huchuk, Scott Sanner, William O'Brien
Summary: Researchers used machine learning and thermostat data to build customized thermal models for multi-hour predictions. They found that using lasso and ridge regression combined with features such as thermostat runtime and solar energy, along with 20 minutes of historical data, provided the lowest prediction errors.
JOURNAL OF BUILDING PERFORMANCE SIMULATION
(2022)
Article
Construction & Building Technology
Brodie W. Hobson, Brent Huchuk, H. Burak Gunay, William O'Brien
Summary: Indoor climate and lighting in office buildings are typically operated using static and conservative setpoints, which may not be optimal for real occupants. Occupant-centric control can estimate occupants' preferences to improve energy efficiency and comfort.
JOURNAL OF BUILDING PERFORMANCE SIMULATION
(2021)
Article
Thermodynamics
Ahmed Abdeen, William O'Brien, Burak Gunay, Guy Newsham, Heather Knudsen
Summary: The study found discrepancies between occupant-related assumptions in the National Building Code of Canada and recent measurement-based studies, resulting in potential increase in heating energy consumption but decrease in cooling energy consumption. Heating energy consumption is more significant, and current occupant-related NBC assumptions may yield different optimal upgrades in certain cases compared to new energy-related occupant assumptions.
SCIENCE AND TECHNOLOGY FOR THE BUILT ENVIRONMENT
(2021)
Article
Thermodynamics
Tareq Abuimara, Brodie W. Hobson, Burak Gunay, William O'Brien
Summary: This study investigates the effective construction of VAV AHU configurations to supply outdoor air to building spaces through data analysis and building performance simulation. Results show that occupancy-based VAV control minimizes instances of under-ventilation and improves outdoor air distribution efficiency, while reducing energy consumption.
SCIENCE AND TECHNOLOGY FOR THE BUILT ENVIRONMENT
(2022)
Article
Thermodynamics
Mohammad Derakhti, William O'Brien, Scott Bucking
Summary: Developing an accurate energy model remains challenging due to the complexity of building parameters and measurement difficulties. This study found that monthly calibration was unable to reflect actual building operation conditions, highlighting the necessity for hourly calibration. The resolution of measured data significantly affects the estimated impact of energy saving measures implemented on calibrated models.
SCIENCE AND TECHNOLOGY FOR THE BUILT ENVIRONMENT
(2022)
Article
Thermodynamics
Pedram Nojedehi, William O'Brien, H. Burak Gunay
Summary: This study investigates a methodology that integrates BIM with maintenance management system logs, providing greater context and improving decision-making efficiency through automatic data exchange and visualization. The methodology reduces the reliance on external programming languages and improves data exchange efficiency. A case study highlights the benefits of focusing on specific spaces in enhancing productivity and energy efficiency for facility management teams.
SCIENCE AND TECHNOLOGY FOR THE BUILT ENVIRONMENT
(2022)
Review
Construction & Building Technology
Justin Berquist, Noah Cassidy, Marianne Touchie, William O'Brien, Jamie Fine
Summary: The performance of ventilation systems in high-rise residential buildings has a significant impact on resident wellbeing. Previous designs prioritized economic sustainability, but there is now a shift towards social and ecological sustainability. Decentralized heat/energy recovery ventilators show promise in improving social and ecological sustainability, but improvements in one aspect may negatively impact others.
Article
Thermodynamics
Justin Vezeau, Ruth Tamas, William O'Brien, Philip Agee
Summary: The functionality of commercial building thermostats is often not well understood by occupants, leading to user dissatisfaction and inefficient thermostat operation. This study examines the relationship between usability principles and thermostat interfaces through a comparative study. Results show that while the success rate for usability tasks is similar between control and experimental thermostats, the majority of participants preferred the additional features offered by the experimental interface. This paper explores new thermostat features to improve usability and introduces rapid prototyping as a solution for enhancing commercial building thermostat usability.
SCIENCE AND TECHNOLOGY FOR THE BUILT ENVIRONMENT
(2023)
Article
Construction & Building Technology
Jia Liang, Qipeng Zhang, Xingyu Gu
Summary: A lightweight PCSNet-based segmentation model is developed to address the issues of insufficient performance in feature extraction and boundary loss information. The introduction of generalized Dice loss improves prediction performance, and the visualization of class activation mapping enhances model interpretability.
AUTOMATION IN CONSTRUCTION
(2024)
Article
Construction & Building Technology
Gilsu Jeong, Minhyuk Jung, Seongeun Park, Moonseo Park, Changbum Ryan Ahn
Summary: This study introduces a contextual audio-visual approach to recognize multi-equipment activities in tunnel construction sites, improving monitoring effectiveness. Tested against real-world operation data, the model achieved remarkable results, emphasizing the potential of contextual multimodal models in enhancing operational efficiency in complex construction sites.
AUTOMATION IN CONSTRUCTION
(2024)
Article
Construction & Building Technology
Jin Wang, Zhigao Zeng, Pradip Kumar Sharma, Osama Alfarraj, Amr Tolba, Jianming Zhang, Lei Wang
Summary: This study presents a dual-path network for pavement crack segmentation, combining Convolutional Neural Network (CNN) and transformer. A lightweight CNN encoder is used for local feature extraction, while a novel transformer encoder integrates high-low frequency attention mechanism and efficient feedforward network for global feature extraction. Additionally, a complementary fusion module is introduced to aggregate intermediate features extracted from both encoders. Evaluation on three datasets confirms the superior performance of the proposed network.
AUTOMATION IN CONSTRUCTION
(2024)
Article
Construction & Building Technology
Pierre Gilibert, Romain Mesnil, Olivier Baverel
Summary: This paper introduces a flexible method for crafting 2D assemblies adaptable to various geometric assumptions in the realm of sustainable construction. By utilizing digital fabrication technologies and optimization approaches, precise control over demountable buildings can be achieved, improving mechanical performance and sustainability.
AUTOMATION IN CONSTRUCTION
(2024)
Review
Construction & Building Technology
Jorge Loy-Benitez, Myung Kyu Song, Yo-Hyun Choi, Je-Kyum Lee, Sean Seungwon Lee
Summary: This paper discusses the advancement of tunnel boring machines (TBM) through the application of artificial intelligence. It highlights the significance of AI-based management subsystems for automatic TBM operations and presents recent contributions in this field. The paper evaluates modeling, monitoring, and control subsystems and suggests research paths for integrating existing management subsystems into TBM automation.
AUTOMATION IN CONSTRUCTION
(2024)
Review
Construction & Building Technology
Alireza Shamshiri, Kyeong Rok Ryu, June Young Park
Summary: This paper reviews the application of text mining and natural language processing in the construction field, highlighting the need for automation and minimizing manual tasks. The study identifies potential research opportunities in strengthening overlooked construction aspects, coupling diverse data formats, and leveraging pre-trained language models and reinforcement learning.
AUTOMATION IN CONSTRUCTION
(2024)
Article
Construction & Building Technology
Zhengyi Chen, Hao Wang, Keyu Chen, Changhao Song, Xiao Zhang, Boyu Wang, Jack C. P. Cheng
Summary: This study proposes an improved coverage path planning system that leverages building information modeling and robotic configurations to optimize coverage performance in indoor environments. Experimental validation shows the effectiveness and applicability of the system. Future research will focus on further enhancing coverage ratio and optimizing computation time.
AUTOMATION IN CONSTRUCTION
(2024)
Review
Construction & Building Technology
Yonglin Fu, Junjie Chen, Weisheng Lu
Summary: This study presents a review of human-robot collaboration (HRC) in modular construction manufacturing (MCM), focusing on tasks, human roles, and interaction levels. The review found that HRC solutions are applicable to various MCM tasks, with a primary focus on timber component production. It also revealed the diverse collaborative roles humans can play and the varying levels of interaction with robots.
AUTOMATION IN CONSTRUCTION
(2024)
Article
Construction & Building Technology
Qiong Liu, Shengbo Cheng, Chang Sun, Kailun Chen, Wengui Li, Vivian W. Y. Tam
Summary: This paper presents an approach to enhance the path-following capability of concrete printing by integrating steel cables into the printed mortar strips, and validates the feasibility and effectiveness of this approach through experiments.
AUTOMATION IN CONSTRUCTION
(2024)
Article
Construction & Building Technology
Honghu Chu, Lu Deng, Huaqing Yuan, Lizhi Long, Jingjing Guo
Summary: The study proposes a method called Cascade CATransUNet for high-resolution crack image segmentation. This method combines the coordinate attention mechanism and self-cascaded design to accurately segment cracks. Through a customized feature extraction architecture and an optimized boundary loss function, the proposed method achieves impressive segmentation performance on HR images and demonstrates its practicality in UAV crack detection tasks.
AUTOMATION IN CONSTRUCTION
(2024)
Article
Construction & Building Technology
Daniel Lamas, Andres Justo, Mario Soilan, Belen Riveiro
Summary: This paper introduces a new method for creating synthetic point clouds of truss bridges and demonstrates the effectiveness of a deep learning approach for semantic and instance segmentation of these point clouds. The proposed methodology has significant implications for the development of automated inspection and monitoring systems for truss bridges.
AUTOMATION IN CONSTRUCTION
(2024)
Article
Construction & Building Technology
Kahyun Jeon, Ghang Lee, Seongmin Yang, Yonghan Kim, Seungah Suh
Summary: This study proposes two enhanced unsupervised text classification methods for domain-specific non-English text. The results of the tests show that these methods achieve excellent performance on Korean building defect complaints, outperforming state-of-the-art zero-shot and few-shot text classification methods, with minimal data preparation effort and computing resources.
AUTOMATION IN CONSTRUCTION
(2024)
Article
Construction & Building Technology
Yoonhwa Jung, Julia Hockenmaier, Mani Golparvar-Fard
Summary: This study introduces a transformer-based natural language processing model, UNIfORMATBRIDGE, that automatically labels activities in a project schedule with Uniformat classification. Experimental results show that the model performs well in matching unstructured schedule data to Uniformat classifications. Additionally, the study highlights the importance of this method in developing new techniques.
AUTOMATION IN CONSTRUCTION
(2024)
Article
Construction & Building Technology
De-Graft Joe Opoku, Srinath Perera, Robert Osei-Kyei, Maria Rashidi, Keivan Bamdad, Tosin Famakinwa
Summary: This paper introduces a digital twin technology combining Building Information Modelling and the Internet of Things for the construction industry, aiming to optimize building conditions. The technology is implemented in a university library, successfully achieving real-time data capture and visual representation of internal conditions.
AUTOMATION IN CONSTRUCTION
(2024)
Article
Construction & Building Technology
Zaolin Pan, Yantao Yu
Summary: The construction industry faces safety and workforce shortages globally, and worker-robot collaboration is seen as a solution. However, robots face challenges in recognizing worker intentions in construction. This study tackles these challenges by proposing a fusion method and investigating the best granularity for recognizing worker intentions. The results show that the proposed method can recognize multi-granular worker intentions effectively, contributing to seamless worker-robot collaboration in construction.
AUTOMATION IN CONSTRUCTION
(2024)