Purpose: Visual inspection and human judgement form the cornerstone of daily operations and maintenance (O&M) services activities carried out by facility managers nowadays. Recent advances in technologies such as building information modelling (BIM), distributed sensor networks, augmented reality (AR) technologies and digital twins present an immense opportunity to radically improve the way daily O&M is conducted. This paper aims to describe the development of an AR-supported automated environmental anomaly detection and fault isolation method to assist facility managers in addressing problems that affect building occupants’ thermal comfort.Design/methodology/approach: The developed system focusses on the detection of environmental anomalies related to the thermal comfort of occupants within a building. The performance of three anomaly detection algorithms in terms of their ability to detect indoor temperature anomalies is compared. Based on the fault tree analysis (FTA), a decision-making tree is developed to assist facility management (FM) professionals in identifying corresponding failed assets according to the detected anomalous symptoms. The AR system facilitates easy maintenance by highlighting the failed assets hidden behind walls/ceilings on site to the maintenance personnel. The system can thus provide enhanced support to facility managers in their daily O&M activities such as inspection, recording, communication and verification.Findings: Taking the indoor temperature inspection as an example, the case study demonstrates that the O&M management process can be improved using the proposed AR-enhanced inspection system. Comparative analysis of different anomaly detection algorithms reveals that the binary segmentation-based change point detection is effective and efficient in identifying temperature anomalies. The decision-making tree supported by FTA helps formalise the linkage between temperature issues and the corresponding failed assets. Finally, the AR-based model enhanced the maintenance process by visualising and highlighting the hidden failed assets to the maintenance personnel on site.Originality/value: The originality lies in bringing together the advances in augmented reality, digital twins and data-driven decision-making to support the daily O&M management activities. In particular, the paper presents a novel binary segmentation-based change point detection for identifying temperature anomalous symptoms, a decision-making tree for matching the symptoms to the failed assets, and an AR system for visualising those assets with related information.

As the construction of infrastructures becomes increasingly complex, it has often been challenged by construction delay with enormous losses. The delivery of complex infrastructures provides a rich source of data for new opportunities to understand and address schedule issues. Based on these data, many efforts have been made to identify key construction schedule risks and predict the probability of risk occurrence. Bayesian network is one of the most useful tools for risk inference. However, there are still two obstacles preventing the Bayesian network from being adopted popularly in construction schedule risk management: (1) the development of directed acyclic graph (DAG) and associated conditional probability tables (CPTs); and (2) the lack of observation data to trigger risk inference as evidence at the planning stage. This research aims to develop a novel Bayesian Monte Carlo simulation–driven approach for construction schedule risk inference of infrastructures, where the Bayesian network model can be developed in a more convenient way and be used without observation data required. It first constructs the key risk network with key risks and links through network theory–based analysis. Then the DAG structure of a Bayesian network is developed based on the topological structure of key risk network using deep-first search (DFS) and adapted maximum-weight spanning tree (A-MWST) algorithms. The CPTs are further developed using the leaky-MAX model. Finally, the Bayesian Monte Carlo simulation–driven risk inference method is developed for predicting and quantifying the probability of construction schedule risk occurrence. A real infrastructure project was selected as a case study to verify this developed approach. The results show that the developed approach is more appropriate to deal with risk inference of infrastructures considering its reliability, convenience, and flexibility. This research contributes a new way to construction schedule risk management and provides a novel approach for quantifying and predicting risk occurrence probability.

Various maturity models have been developed for understanding the diffusion and implementation of new technologies/approaches. However, we find that existing maturity models fail to understand the implementation of emerging digital twin technique comprehensively and quantitatively. This research aims to develop an innovative maturity model for measuring digital twin maturity for asset management. This model is established based on Gemini Principles to form a systematic view of digital twin development and implementation. Within this maturity model, three main dimensions consisting of nine sub-dimensions have been defined firstly, which were further articulated by 27 rubrics. Then, a questionnaire survey with 40 experts involved is designed and conducted to examine these rubrics. This model is finally illustrated and validated by two case studies in Shanghai and Cambridge. The results show that the digital twin maturity model is effective to qualitatively evaluate and compare the maturity of digital twin implementation at the project level. It can also initiate the roadmap for improving the performance of digital twin supported asset management.

The construction of infrastructures is becoming increasingly complex and risky under uncertainty, which often results in construction delay. Many efforts have been made to identify and control construction schedule risks. However, previous efforts only focused on the diversity of risks with assumption that risks are independent. The ignorance of (1) interdependencies between the originalities of risks within infrastructures as a sociotechnical system, and (2) links between risks impedes the reasonable explanation for and assessment of the construction delay of infrastructures. This paper aims to develop a novel hybrid approach to address these issues based on dialectical systems and network theory. It first identified construction schedule risks from the perspective of dialectical systems at the industry level, which were further verified by global expert interviews and refined by questionnaire survey based on the Chinese infrastructure industry. Then, using network theory–based analysis, the key challenges and solutions were identified at project level based on a real infrastructure case. Seven key risks and 20 key risk links were identified with effective risk mitigation strategies provided for this case project. This research provides a new approach to understanding and analyzing construction schedule risks for better delivery of infrastructures. The results are not only helpful for the Chinese infrastructure industry, but with revisions are also applicable for other regions to improve construction schedule risk management.

The climate change issue has been striking and bringing pressure on all countries and industries. The responsibility of the Architecture, Engineering, Construction and Facility Management (AEC/FM) industry is heavy because it accounts for over one-third of global energy use and greenhouse gas emissions. At the same time, the development of digital technology brings the opportunity to mitigate environmental issues. Therefore, this study intended to examine the state-of-the-art of digital development and transformation in the AEC/FM industry by collecting and reviewing the developed digital carbon footprint analysis tools in infrastructure, building, and city scopes. Specifically, this study (1) generated a review methodology for carbon footprint analysis results; (2) demonstrated the review results from the infrastructure, building, and city scopes, analysed and compared the results crossing the scopes from four aspects: carbon footprint analysis strategy, standards and protocols, rating systems, and general development level of digital tools; and (3) discussed the potential directions in the industry to address the environmental issues. This study indicated that the digitalisation level regarding carbon-related areas is still at an early stage, and efforts should be taken both academically and practically to drive the digital development confronting the harsh climate change issue.