Bridges are a critical piece of infrastructure in the network of road and rail transport system. Many of the bridges in Norway (in Europe) are at the end of their lifespan, therefore regular inspection and maintenance are critical to ensure the safety of their operations. However, the traditional inspection procedures and resources required are so time consuming and costly that there exists a significant maintenance backlog. The central thrust of this paper is to demonstrate the significant benefits of adapting a Unmanned Aerial Vehicle (UAV)-assisted inspection to reduce the time and costs of bridge inspection and established the research needs associated with the processing of the (big) data produced by such autonomous technologies. In this regard, a methodology is proposed for analysing the bridge damage that comprises three key stages, (i) data collection and model training, where one performs experiments and trials to perfect drone flights for inspection using case study bridges to inform and provide necessary (big) data for the second key stage, (ii) 3D construction, where one built 3D models that offer a permanent record of element geometry for each bridge asset, which could be used for navigation and control purposes, (iii) damage identification and analysis, where deep learning-based data analytics and modelling are applied for processing and analysing UAV image data and to perform bridge damage performance assessment. The proposed methodology is exemplified via UAV-assisted inspection of Skodsberg bridge, a 140 m prestressed concrete bridge, in the Viken county in eastern Norway.

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Human reliability has a high contribution in maintenance performance, safety, and cost efficiency of any production process. To improve human reliability, the causes of human errors should be identified and the probability of human errors should be quantified. Analysis of human error is very case specific in which the context of the field should be taken into account. The aim of this study is to identify the causes of human errors and, improve human reliability in maintenance activities in cable manufacturing industry. The central thrust of this paper is to employ the three most common HRA techniques -HEART (Human Error Assessment and Reduction Technique), SPAR-H (Standardized Plant Analysis Risk-Human Reliability), and BN (Bayesian Network) for estimating human error probabilities, and then to check the consistency of results obtained. The case study results demonstrated that the main causes of human error during maintenance activities are time pressure, lack of experience, and poor procedure. Moreover, the probabilities of human error obtained by employing the three techniques are almost similar and consistent.

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