This study explores the use of infrared thermography (IRT) technology for the non-destructive evaluation of ballast fouling in railway tracks, focusing on the influence of parent rock types and fouling materials. Utilizing thermal imaging, the research investigates how variations in ballast conditions affect surface temperature, which serves as an indicator of structural integrity and health. The experimental setup involved ballast samples derived from three different rock types—basalt, limestone, and andesite—fouled with commonly encountered materials like sand and clay at varying percentages. Results demonstrate that fouling level and type significantly influence the thermal signatures captured by IRT passive camera. Notably, ballast derived from darker rocks exhibited higher temperatures, indicating greater emissivity, while fouled ballast showed distinct temperature patterns compared to clean samples, emphasizing the potential of thermal imaging in detecting and quantifying fouling in ballast layers. This research underscores the viability of IRT passive camera in the routine maintenance and monitoring of railway infrastructure, providing a foundation for further development of integrated diagnostic tools for railway management systems.

Building Information Modelling (BIM) is a digitalisation tool that is widely adopted in construction industry. It is a three-dimensional digital replica of asset(s) such as buildings, which contain architectural information and building details (e.g. dimensions, materials, parts, and components). It has evolved from 2D CAD models (or blueprints) in the past to 3D CAD models embedded with information layers (e.g., construction time sequence or 4D-BIM), resulting in automation in construction. BIM has now been essential in various countries; for example, new UK BIM standards require asset owners to keep and maintain building information. BIM adopts an interoperable concept that can benefit the whole life-cycle assessment (LCA) and circularity of the built environments. Its applications extend to six dimensions (6D) where time sequence, cost and carbon footprint can now be reported in real time. These attributes are essential to stakeholders and critically help reduce any unexpected consumption and waste over the life cycle of a project. This study builds on the development of 6D BIM of an existing building to enrich circular value chains and stakeholder engagement. This paper highlights the development of 6D BIM, and, subsequently, the stakeholder interviews to address challenges, barriers, benefits, and effectiveness of 6D-BIM applications for stakeholder engagements across circular value chains. Snowballing sampling method has been used to identify stakeholder interviews to obtain new insights into the digital valorisation for stakeholder engagement. The outcome of this study will exhibit new insights and practical paradigms for BIM applications in built environments.

Railway track health monitoring and maintenance are crucial stages in railway asset management, aiming to enhance the train operation quality and service life. For this aim, various inspection means (using diverse non-destructive testing techniques) have been applied, however, these means are mostly not able to monitor whole railway track network or track underlying layers (e.g., ballast and subgrade). The use of remote sensing techniques, such as Interferometric Synthetic Aperture Radar (InSAR), can expedite the defect diagnosis process for railway tracks, elevating the scope of health monitoring to a network-wide level. The Ground Penetrating Radar (GPR) has emerged as a particularly reliable method, especially for detecting structural deficiencies in underlying layers. As a result, combining the two distinct non-destructive testing techniques – GPR and InSAR – presents a promising strategy for efficient railway asset management. Recognizing the significance of embracing newer and more advanced monitoring strategies, this paper reviews the fusion of GPR and InSAR methodologies, and explores the potential integration of machine learning models to develop a predictive health monitoring and condition-based maintenance approach for railway tracks.

Rib-to-deck (RD) welded joints in orthotropic steel decks (OSDs) of bridges demonstrates two major fatigue failure models, including the toe-to-deck (TTD) cracking and root-to-deck (RTD) cracking. Generally, the sole failure model is employed in the fatigue assessment of RD joints, causing a hot dispute on the dominant failure model. In this paper, the fatigue crack growth (FCG) in RD joints has been evaluated considering uncertainties and mixed failure models. A probabilistic fatigue crack growth (PFCG) model is at first established for the RD joint, in which two crack-like initial flaws are assumed at the weld toe and root of the RD joint. After that, the gaussian process regression is used to assist and boost the PFCG simulation. Then, the PFCG model is implemented on a typical OSD with the random traffic model. Finally, the result of the PFCG model is discussed in detail, including the failure model, fatigue reliability and life prediction, and crack size evolution. It is revealed that both the TTD and RTD cracking models have a notable contribution to fatigue failure and could not be ignored. More crucial, a remarkable reduction can be observed in the fatigue reliability of RD joints when considering mixed failure models. This study not only highlights the influence of mixed failure models on the fatigue performance of welded joints, but also provide an insight into the application of novel machine learning tools in solving the traditional structural issue.