Experimental and Numerical Investigation of Compressive Membrane Action in GFRP-Reinforced Concrete Slabs.

Experimental and numerical analyses of eight in-plane restrained slabs (1425 mm (length) × 475 mm (width) × 150 mm (thickness)) reinforced with glass fiber-reinforced polymer (GFRP) bars are reported in this paper. The test slabs were installed into a rig, that provided 855 kN/mm in-plane stiffness and rotational stiffness. The effective depths of the reinforcement in the slabs varied from 75 mm to 150 mm, and the amount of reinforcement changed from 0 to 1.2% with 8, 12, and 16 mm bar diameters. A comparison of the service and ultimate limit state behavior of the tested one-way spanning slabs shows that a different design approach is necessary for GFRP-reinforced in-plane restrained slabs that demonstrate compressive membrane action behavior. Design codes based on yield line theory, which considers simply supported and rotationally restrained slabs, are not sufficient to predict the ultimate limit state behavior of restrained GFRP-reinforced slabs. Tests reported a higher failure load for GFRP-reinforced slabs by a factor of 2, which was further validated by numerical models. The experimental investigation was validated by a numerical analysis, and the acceptability of the model was further confirmed by consistent results obtained by analyzing in-plane restrained slab data from the literature.

Understanding the barriers to NET-ZERO transport for rural roads: a Northern Ireland case study.

INTRODUCTION: Climate-related disasters have cost the world over £450 billion over the last 3 years. In the race to mitigate these effects, the UK government has committed to net-zero emissions by 2050. Transport provides the largest single sector contribution to CO2 emissions, the road network accounts for up to 91%. As the only UK country without a formal climate change bill Northern Ireland could compromise the overall effort. CASE DESCRIPTION: In this research a survey of road asset owners, managers, academics, consultants, public transport providers was undertaken to seek to understand the current barriers to adapting a dispersed rural road network in Northern Ireland for net-zero transport. The survey data was collected though an online form with a combination of multiple choice and open ended questions. Thematic analysis was used to code and analyse the data collected which enabled a discussion around the key expert opinions gathered. DISCUSSION AND EVALUATION: The paper presents details of the current road network in Northern Ireland and highlights some of the issues faced by asset owners. The survey questions were developed though engagement with transport professionals in Northern Ireland and focus predominantly on road use rather than the impact of current land management practices or environmental conditions such as flood risk. The response highlights a clear enthusiasm for change in the operation of the public road network which is hindered by a lack of government strategy and limited public consultation. CONCLUSIONS: The high response rate (41%) for the survey highlights the interest of those in the transport sector to engage in activities which can support a better understanding of how road networks contribute to CO2 emissions. Within the survey data a requirement for behavioural change was highlighted as a key step to reduce transport related emissions, the enthusiasm for change demonstrates this is the optimum time to engage with the public and develop clear transport strategies. More accurate findings and empirical evidence could have been established had the study considered specific, transport planning, environmental and land use conditions for Northern Ireland. This will be the focus of further research in this area to enable clear translation of the research to other countries.

Bridge Damage Detection Approach Using a Roving Camera Technique.

Increasing extreme climate events, intensifying traffic patterns and long-term underinvestment have led to the escalated deterioration of bridges within our road and rail transport networks. Structural Health Monitoring (SHM) systems provide a means of objectively capturing and quantifying deterioration under operational conditions. Computer vision technology has gained considerable attention in the field of SHM due to its ability to obtain displacement data using non-contact methods at long distances. Additionally, it provides a low cost, rapid instrumentation solution with low interference to the normal operation of structures. However, even in the case of a medium span bridge, the need for many cameras to capture the global response can be cost-prohibitive. This research proposes a roving camera technique to capture a complete derivation of the response of a laboratory model bridge under live loading, in order to identify bridge damage. Displacement is identified as a suitable damage indicator, and two methods are used to assess the magnitude of the change in global displacement under changing boundary conditions in the laboratory bridge model. From this study, it is established that either approach could detect damage in the simulation model, providing an SHM solution that negates the requirement for complex sensor installations.

Identification of Bridge Key Performance Indicators Using Survival Analysis for Future Network-Wide Structural Health Monitoring.

Machine learning and statistical approaches have transformed the management of infrastructure systems such as water, energy and modern transport networks. Artificial Intelligence-based solutions allow asset owners to predict future performance and optimize maintenance routines through the use of historic performance and real-time sensor data. The industrial adoption of such methods has been limited in the management of bridges within aging transport networks. Predictive maintenance at bridge network level is particularly complex due to the considerable level of heterogeneity encompassed across various bridge types and functions. This paper reviews some of the main approaches in bridge predictive maintenance modeling and outlines the challenges in their adaptation to the future network-wide management of bridges. Survival analysis techniques have been successfully applied to predict outcomes from a homogenous data set, such as bridge deck condition. This paper considers the complexities of European road networks in terms of bridge type, function and age to present a novel application of survival analysis based on sparse data obtained from visual inspections. This research is focused on analyzing existing inspection information to establish data foundations, which will pave the way for big data utilization, and inform on key performance indicators for future network-wide structural health monitoring.

A Robust Vision-Based Method for Displacement Measurement under Adverse Environmental Factors Using Spatio-Temporal Context Learning and Taylor Approximation.

Currently, the majority of studies on vision-based measurement have been conducted under ideal environments so that an adequate measurement performance and accuracy is ensured. However, vision-based systems may face some adverse influencing factors such as illumination change and fog interference, which can affect measurement accuracy. This paper developed a robust vision-based displacement measurement method which can handle the two common and important adverse factors given above and achieve sensitivity at the subpixel level. The proposed method leverages the advantage of high-resolution imaging incorporating spatial and temporal contextual aspects. To validate the feasibility, stability, and robustness of the proposed method, a series of experiments was conducted on a two-span three-lane bridge in the laboratory. The illumination changes and fog interference were simulated experimentally in the laboratory. The results of the proposed method were compared to conventional displacement sensor data and current vision-based method results. It was demonstrated that the proposed method gave better measurement results than the current ones under illumination change and fog interference.