Impact assessment of road infrastructure: A holistic approach

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Preliminary Hazard Analysis for UAV-assisted Bridge Inspection

Unmanned aerial vehicle (UAV) technology has found its way into a number of civilian applications in the last 20 years, predominantly due to lower costs and tangible scientific improvements. In its application to structural bridge inspection, UAVs provide two main functions. The first, being the most common, detects damage through visual sensors. The 2D imagery data can be used to quickly establish a basic knowledge of the structure’s condition and is usually the first port of call. The second reconstructs 3D models to provide a permanent record of geometry for each bridge asset, which could be used for navigation and control purposes. However, there are various problems associated with the use of UAVs for bridge inspection, in particular, in cold operating environments, such as Norway. This paper will integrate scenario prediction and assess hazards as well as the social and environmental loss in the case of UAV-assisted bridge inspection. Further, this paper will follow rather closely a three-phase process: hazard identification, hazard analysis, and hazard evaluation, all executed with qualitative data and methods by experts of a variety of fields, methodologies for recognition of the impact of cold operating environment on the performance of UAVs and UAV-pilots, creative interpretation of the hazard factors of identifiable problems, or even brainstorming about “imaging the unimaginable”.publishedVersio

Application of Artificial Neural Networks for Power Load Prediction in Critical Infrastructure: A Comparative Case Study

This article aims to assess the effectiveness of state-of-the-art artificial neural network (ANN) models in time series analysis, specifically focusing on their application in prediction tasks of critical infrastructures (CIs). To accomplish this, shallow models with nearly identical numbers of trainable parameters are constructed and examined. The dataset, which includes 120,884 hourly electricity consumption records, is divided into three subsets (25%, 50%, and the entire dataset) to examine the effect of increasing training data. Additionally, the same models are trained and evaluated for univariable and multivariable data to evaluate the impact of including more features. The case study specifically focuses on predicting electricity consumption using load information from Norway. The results of this study confirm that LSTM models emerge as the best-performed model, surpassing other models as data volume and feature increase. Notably, for training datasets ranging from 2000 to 22,000 instances, GRU exhibits superior accuracy, while in the 22,000 to 42,000 range, LSTM and BiLSTM are the best. When the training dataset is within 42,000 to 360,000, LSTM and ConvLSTM prove to be good choices in terms of accuracy. Convolutional-based models exhibit superior performance in terms of computational efficiency. The convolutional 1D univariable model emerges as a standout choice for scenarios where training time is critical, sacrificing only 0.000105 in accuracy while a threefold improvement in training time is gained. For training datasets lower than 22,000, feature inclusion does not enhance any of the ANN model’s performance. In datasets exceeding 22,000 instances, ANN models display no consistent pattern regarding feature inclusion, though LSTM, Conv1D, Conv2D, ConvLSTM, and FCN tend to benefit. BiLSTM, GRU, and Transformer do not benefit from feature inclusion, regardless of the training dataset size. Moreover, Transformers exhibit inefficiency in time series forecasting due to their permutation-invariant self-attention mechanism, neglecting the crucial role of sequence order, as evidenced by their poor performance across all three datasets in this study. These results provide valuable insights into the capabilities of ANN models and their effective usage in the context of CI prediction tasks.publishedVersio

Drone-based bridge inspection in harsh operating environment: risks and safeguards

The inability to effectively and systematically identify and measure the damage in bridges will lead to an acceleration and dangerous deterioration of the health state of these structures. To repair and replace the aging and damaged bridge infrastructures, and prevent catastrophic bridge collapse, there is an urgent need to develop reliable, innovative, and efficient approaches to the performance assessment and inspection of bridges. Unmanned Aerial Vehicles (UAVs), also knowns as drone, technology has found its way into a number of civilian applications including inspection in the last 20 years, predominantly due to lower cost and tangible scientific improvements. The intent of this paper is to map the current state-of-the-art drone-enabled bridge inspection practices and investigated their associated hazards and risks. This paper will integrate scenario prediction and, assess hazards as well as the social and environmental loss in the case of drone-enabled bridge inspection. Further, this paper will follow rather closely a three-phase process: hazard identification, hazard analysis, and hazard evaluation, all executed with qualitative data and methods by experts of a variety of fields, methodologies for recognition of the impact of cold operating environment on the performance of drone and drone -pilots, creative interpretation of the hazard factors of identifiable problems. The proposed Preliminary Hazard Analysis (PHA) is exemplified via drone-enabled inspection of Håkenby bridge, which is located in the Viken county, in the eastern part of Norway.publishedVersio

Hazards identification and risk assessment for UAV-assisted bridge inspections

Unmanned Aerial Vehicles (UAV) technology has found its way into several civilian applications in the last 20 years, predominantly due to lower cost and tangible scientific improvements. In its application to structural bridge inspection, UAVs provide two main functions. The first, being the most common, detect damage through visual sensors. The 2 D image data can be used to quickly establish a basic knowledge of the structure’s condition and is usually the first port of call. The second reconstructs 3D models to provide a permanent record of geometry for each bridge asset, which could be used for navigation and control purposes. However, there are various types of hazards and risks associated with the use of UAVs for bridge inspection, in particular, in a cold operating environment. In this study, a systematic methodology, which is an integration of hazard identification, expert judgment, and risk assessment for preliminary hazard analysis (PHA) in the UAV-assisted bridge inspection system is proposed. The proposed methodology is developed and exemplified via UAV-assisted inspection of Grimsøy bridge, a 71.3 m concrete bridge, located in the Viken county in eastern Norway.publishedVersio

A digital information model framework for uas-enabled bridge inspection

Unmanned aerial systems (UAS) provide two main functions with regards to bridge inspections: (1) high-quality digital imaging to detect element defects; (2) spatial point cloud data for the reconstruction of 3D asset models. With UAS being a relatively new inspection method, there is little in the way of existing framework for storing, processing and managing the resulting inspection data. This study has proposed a novel methodology for a digital information model covering data acquisition through to a 3D GIS visualisation environment, also capable of integrating within a bridge management system (BMS). Previous efforts focusing on visualisation functionality have focused on BIM and GIS as separate entities, which has a number of problems associated with it. This methodology has a core focus on the integration of BIM and GIS, providing an effective and efficient information model, which provides vital visual context to inspectors and users of the BMS. Three-dimensional GIS visualisation allows the user to navigate through a fully interactive environment, where element level inspection information can be obtained through point-and-click operations on the 3D structural model. Two visualisation environments were created: a web-based GIS application and a desktop solution. Both environments develop a fully interactive, user-friendly model which have fulfilled the aims of coordinating and streamlining the BMS process.publishedVersio

Automatic Crack Segmentation for UAV-assisted Bridge Inspection

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.publishedVersio

Security Aspects of Social Robots in Public Spaces: A Systematic Mapping Study

Background: As social robots increasingly integrate into public spaces, comprehending their security implications becomes paramount. This study is conducted amidst the growing use of social robots in public spaces (SRPS), emphasising the necessity for tailored security standards for these unique robotic systems. Methods: In this systematic mapping study (SMS), we meticulously review and analyse existing literature from the Web of Science database, following guidelines by Petersen et al. We employ a structured approach to categorise and synthesise literature on SRPS security aspects, including physical safety, data privacy, cybersecurity, and legal/ethical considerations. Results: Our analysis reveals a significant gap in existing safety standards, originally designed for industrial robots, that need to be revised for SRPS. We propose a thematic framework consolidating essential security guidelines for SRPS, substantiated by evidence from a considerable percentage of the primary studies analysed. Conclusions: The study underscores the urgent need for comprehensive, bespoke security standards and frameworks for SRPS. These standards ensure that SRPS operate securely and ethically, respecting individual rights and public safety, while fostering seamless integration into diverse human-centric environments. This work is poised to enhance public trust and acceptance of these robots, offering significant value to developers, policymakers, and the general public.publishedVersio

Risk-Based Analysis of Drilling Waste Handling Operations. Bayesian Network, Cost-effectiveness, and Operational Conditions

As the offshore industry expands into the Arctic and sub-Arctic areas, the oil and gas exploration activities generate all kinds of waste, varying from contaminated runoff water to material packaging; however, the majority of the waste is associated with the drilling cuttings from drilling activities. Offshore Arctic projects have a high degree of technical and social complexity. The technological challenges of drilling at remote location coupled with the extreme weather conditions makes the operation of drilling waste handling in this environment very demanding and risky. Hence, the competence to reduce the adverse impacts of undesirable events during the drilling waste handling activities depends in part upon the effectiveness of our rigorous risk management plan and clear understanding of the effect of the Arctic operating environment on the drilling waste handling systems. The aim of this research study is to evaluate, identify, and propose a methodology for drilling waste handling practices by considering the complex and fast-changing nature of the Arctic operational conditions. Moreover, the study seeks to foster an integrated interdisciplinary understanding of technical and operational risks associated with drilling wastes and their management by implementing the risk-based analysis. This includes identifying and assessing risks throughout the logistical chain of handling of petroleum related waste. Furthermore, to assure the operational performance of waste handling systems, the study focuses on developing and introducing the concept of a dynamic model for spare parts transportation in Arctic conditions by considering the time-independent and time-dependent covariates. The first part of the study describes the main factors that may influence the operation and performance of the waste handling technologies and processes under Arctic conditions. Then, the current industry practice for managing and disposing of drilling waste are studied. Afterwards, the pros and cons of the common offshore and onshore disposal options are reviewed. Thereafter, a step-by-step methodology is developed for the identification of suitable drilling waste handling systems for Arctic offshore drilling. The application of the methodology is demonstrated by a case study of drilling waste handling practices of an oil field in the Barents Sea (part of Norwegian and Russian Arctic). In the second part of this research study, a risk-based cost-effectiveness analysis model is developed. This model seeks to identify the drilling waste handling practice that is expected to provide the highest level of benefit for a given level of cost, and which has a minimal impact on the HSE (health, safety and environment). Moreover, to avoid inadequacies of the traditional risk assessment approaches and manage the major risk elements connected with handling of drilling wastes, a dynamic Bayesian network (DBN) based risk assessment model is developed. The proposed DBN based risk model combines prior operating environment information with actual observed data from weather forecasting to predict the future potential hazards and/or risks. Furthermore, to assure the availability of production facilities, including waste handling systems, a dynamic model for spare parts transportation called Dynamic Spare Parts Transportation Block Diagram (DSTBD) is described and introduced. The DSTBD model analysed the effect of the time-independent and time-dependent covariates on the spare parts transportation operation. The result of the study shows that working in the cold Arctic environments has the potential if not managed properly to cause a significant negative effect on the cost elements and the risk of events. Moreover, the result from the temporal link or dynamic Bayesian network based risk analysis demonstrates that these negative impacts of the peculiar Arctic risk influencing factors on the reliability of the waste handling system and the risk of marine pollutions, is more significant with time. Furthermore, the DSTBD analysis results demonstrate that the operating environment of the Arctic region increases the spare parts transportation time significantly, particularly, during winter season, when transporting the spare parts from the south-western part of Norway to northern Norway

Marketing structures and strategies of Nordic SME’s: A Norwegian case study

Presentation held at The International Conference in Kokkola, Finland, 10.10. - 13.10.2016