Purpose The utilisation of emerging technologies for the inspection of bridges has remarkably increased. In particular, non-destructive testing (NDT) technologies are deemed a potential alternative for costly, labour-intensive, subjective and unsafe conventional bridge inspection regimes. This paper aims to develop a framework to overcome conventional inspection regimes' limitations by deploying multiple NDT technologies to carry out digital visual inspections of masonry railway bridges. Design/methodology/approach This research adopts an exploratory case study approach, and the empirical data is collected through exploratory workshops, interviews and document reviews. The framework is implemented and refined in five masonry bridges as part of the UK railway infrastructure. Four NDT technologies, namely, terrestrial laser scanner, infrared thermography, 360-degree imaging and unmanned aerial vehicles, are used in this study. Findings A digitally enhanced visual inspection framework is developed by using complementary optical methods. Compared to the conventional inspection regimes, the new approach requires fewer subjective interpretations due to the additional qualitative and quantitative analysis. Also, it is safer and needs fewer operators on site, as the actual inspection can be carried out remotely. Originality/value This research is a step towards digitalising the inspection of bridges, and it is of particular interest to transport agencies and bridge inspectors and can potentially result in revolutionising the bridge inspection regimes and guidelines.

Building performance analysis applications have focused on the evaluation of specific designs based on static, uniform indoor environments. In reality, people live in a dynamic environment, neither indoor environments nor building occupants are static, and that would make thermal sensation experienced by an occupant in a building unstable and challenging to evaluate through the time. The current field survey methodology to evaluate thermal comfort in buildings according to Performance Measurement Protocols for Commercial Buildings (PMPCB) is based on instrumental measurement of indoor climate and questionnaires to be answered by building occupants in a specific space at the exact time. Some studies have questioned this approach due to the inconsistency of physical measurement, sampling procedures, and doubtful estimations of some other variables. These are likely to contribute to the incredibility of the survey and possibly affect the overall prediction accuracy. Nowadays, the advancement of IoT technology has the potential to transform human-building interaction and improve building energy performance. It has been estimated that the connected IoT devices are around 9 billion worldwide, and this number expected to grow to reach 50 billion by 2020. In the built environment, the ability to control building indoor environmental variables can have a substantial impact on improving indoor environmental quality and reducing energy consumption, such control mostly achieved by using sensor technology. Thus, this paper presents a unique approach to measure real-time human thermal comfort in the indoor environment. The proposed approach can predict occupants' thermal satisfaction level of an indoor environment throughout the building's operation. The implementation of environmental sensors and a pilot run to evaluate thermal satisfaction in real-time has been tested. The thermal model in ASHRAE standard 55 has used to evaluate thermal comfort.

Various energy simulation tools are used to predict energy consumption in buildings at different stages from design to post-occupancy and maintenance. The inaccuracy and insufficiency of inputs used for building energy simulation (BES) often cause a discrepancy between the predicted and actual energy consumption. Inaccurate energy consumption estimations affect the accomplishment of the sustainability goals and reduction of energy consumption and CO2 emissions in buildings. The review of the existing literature suggests that the potential causes of the aforementioned uncertainty in building energy predictions are divided into 2 categories: human error (in design, construction, energy modelling, etc.) and the inaccuracy and insufficiency of inputs in BES. This research proposes the way forward for BES tools to improve their accuracy by enhancing the precision of various energy simulation inputs, integration of real-time data and use of machine learning and other emerging technologies.