The building industry is facing increasing demands for sustainable and efficient maintenance practices, driven by advancements in Industry 4.0 technologies. Maintenance 4.0 emphasizes proactive maintenance strategies, including Condition-based Maintenance (CbM) and Predictive Maintenance (PdM), significantly enhanced by Digital Twin (DT) technology. DT enables the real-time monitoring, simulation, and optimization of building assets, offering substantial improvements in asset management, energy efficiency, and system longevity. However, integrating these technologies into the building industry's maintenance processes remains a challenge. This paper provides a comprehensive review of current research on DT-enabled Maintenance 4.0, presenting a conceptual framework that integrates enabling technologies and outlines their technological pipelines. It discusses the state-of-the-art methodologies, challenges, and future directions for the implementation of Maintenance 4.0 in the building sector, highlighting the potential of DT systems in optimizing maintenance strategies and enhancing decision-making. The study identifies key areas for further research, including data standardization, AI integration, and hybrid modeling approaches.

Accurate failure prediction is critical to achieving Predictive Maintenance (PdM) for Indoor Air Quality (IAQ), which is highly related to resident well-being and operational effectiveness. However, most existing studies emphasise anomaly detection rather than prediction. To develop a precise and robust method for pre-emptive IAQ warning, this article integrated Convolutional Neural Network (CNN), Bidirectional Long Short-Term Memory (BiLSTM), and Muti-Head Attention (MHA) mechanism into a novel C-B-M model, synergistically incorporating feature extraction, temporal dependency analysis, and contextual weighting mechanisms. Additionally, a real-world dataset collected from various buildings in Singapore is employed in a detailed comparative experiment with other benchmark models for different prediction periods, dataset selection, and failure severity levels to illustrate the effectiveness and robustness of the proposed method. Finally, a Digital Twin (DT)-oriented failure prediction framework for the indoor climate is introduced and validated through the prototype system demonstrating the 3D building model and IAQ alert information.

Artificial bee colony (ABC) is a prominent algorithm that offers great exploration capabilities among various meta-heuristic algorithms. However, its monotonous and one-dimensional search strategy limits its searching performance in the solving process. Thus, to address this issue, a Q-learning based multi-strategy integrated ABC algorithm (QMABC) is proposed. In the QMABC, multiple search strategies are proposed to utilize different individual experiences and search approaches for solution updates. Then, Q-learning is employed for strategy selection. In comparison to previous studies, this paper introduces more effective state and action configurations within the framework of Q-learning. To evaluate the performance of the QMABC, CEC 2017 benchmark functions are adopted to compare it to different meta-heuristic algorithms including ABC based and non-ABC based algorithms. Moreover, applications in path planning are implemented to further verify the effectiveness of the QMABC. Overall, it should be highlighted that the proposed QMABC demonstrates superiority in both numerical and practical experiments.

Asphalt aging often leads to rapid degradation of road performance, which seriously affects the service life of asphalt pavement. Exploring the influence of asphalt oil sources, asphalt grades, and filler types on asphalt microcharacteristics in the asphalt aging process can provide an essential reference to guide asphalt pavement maintenance. In this study, we selected seven kinds of asphalt and three fillers commonly used in China for research. The pressurized aging vessel (PAV) and homemade ultraviolet (UV) aging equipment were used to perform thermo-oxidative aging and UV aging tests, respectively, of asphalt. The microcharacteristics of asphalt before and after aging were analyzed via attenuated total reflectance fourier transformation infrared spectroscopy and nuclear magnetic resonance ¹H spectroscopy. The results show that the oil source of asphalt exerted the most significant influence on the microcharacteristics of the aged asphalt, while the effect of the asphalt grade was relatively limited. The addition of fillers did not affect the aging mechanism of asphalt. UV and PAV aging generated apparent differences in the changes in the aged asphalt microstructure.