Quick and reliable assessment of the condition of bridges in a transportation network after an earthquake can greatly assist immediate post-disaster response and long-term recovery. However, experience shows that available resources, such as qualified inspectors and engineers, will typically be stretched for such tasks. Structural health monitoring (SHM) systems can therefore make a real difference in this context. SHM, however, needs to be deployed in a strategic manner and integrated into the overall disaster response plans and actions to maximize its benefits. This study presents, in its first part, a framework of how this can be achieved. Since it will not be feasible, or indeed necessary, to use SHM on every bridge, it is necessary to prioritize bridges within individual networks for SHM deployment. A methodology for such prioritization based on structural and geotechnical seismic risks affecting bridges and their importance within a network is proposed in the second part. An example using the methodology application to selected bridges in the medium-sized transportation network of Wellington, New Zealand is provided. The third part of the paper is concerned with using monitoring data for quick assessment of bridge condition and damage after an earthquake. Depending on the bridge risk profile, it is envisaged that data will be obtained from either local or national seismic monitoring arrays or SHM systems installed on bridges. A method using artificial neural networks is proposed for using data from a seismic array to infer key ground motion parameters at an arbitrary bridges site. The methodology is applied to seismic data collected in Christchurch, New Zealand. Finally, how such ground motion parameters can be used in bridge damage and condition assessment is outlined AM - Accepted manuscript
Road networks are highly exposed to natural hazard events, which can lead to significant economic and social consequences. In New Zealand, events such as the 2011 Christchurch earthquake, the 2016 Kaikōura earthquake, and the Cyclone Gabrielle in 2023 have demonstrated the severe consequences of road network disruptions. Traditional post event economic assessments often focus solely on clean-up and repair costs, neglecting the broader and more enduring impacts these events can have. Furthermore, business cases for resilience investments usually fail when quantifying the economic benefits of mitigation strategies, due to the underestimation of road disruption consequences. Importantly, not all road link disruptions contribute equally to these consequences, making the identification of critical road links a key step in resilience focused investment prioritization. Furthermore, traditional transportation asset management typically evaluates the life cycle of roads under normal conditions, such as traffic loads and standard environmental factors, while neglecting the influence of natural hazards. However, these events can significantly alter road deterioration and increase maintenance costs, emphasizing the need for integrating risk and resilience into transportation asset management approaches. This thesis presents a methodology to evaluate road criticality by assessing the economic consequences of road disruptions in combination with a hazard model in a prioritization index. Initially, the consequences are quantified through increased travel time, higher vehicle operating costs, and increased gas emissions. Thereafter, a new consequence model is introduced to estimate the increase in maintenance costs on alternative routes that absorb diverted traffic following a disruption. These consequence models are initially applied in a 'full-scan' analysis approach, where each road link is removed in turn to quantify its potential impact and, therefore, its criticality. Subsequently, a hazard model is integrated to develop a road prioritization index that combines the expected impacts of road disruptions, the individual road link criticality, and the probability of occurrence of natural hazard events. This index is designed to help road agencies in prioritizing mitigation strategies. Furthermore, the proposed methodology can also be applied to quantify the indirect economic impacts of natural hazard events. The methodology is demonstrated using New Zealand’s South Island inter-urban network as a case study, incorporating an earthquake-induced landslide model, with Python based simulations, providing road agencies a valuable tool to quantify the economic benefits of resilience investments
The devastating consequences of past events, such as the 2004 Indian Ocean and 2011 Tōhoku tsunamis, emphasise the need for continued improvement in resilience measures. Given that 80% of magnitude 8+ earthquakes occur on the Pacific Rim, New Zealand's tsunami risk is significant. This research develops a novel tsunami inundation model. The proposed model applies equations based on hydraulic principles, including energy conservation (friction loss). While it does not fully replicate hydrodynamic models, it maintains a two-dimensional approach and offers significant improvements over currently implemented simplified methods. It retains excellent computational efficiency (seconds to minutes) while achieving a significant increase in accuracy that is comparable to traditional hydrodynamic models, which typically take hours to days. Calibration of the roughness input variables to hydrodynamic modelling at Gisborne and Christchurch, New Zealand, optimised the model to achieve similarity index values of above 84% for inundation extent, while 77% of inundation depths were within ±1 m and over 93% within ±2 m. This research then produces the first nationally consistent tsunami exposure assessment for New Zealand using a physics-based modelling method. Using probabilistic shoreline wave amplitude data, the study generates high-resolution (10 m) inundation maps for seven return periods (50th and 84th percentiles). These maps are integrated with land cover and infrastructure data to quantify exposure and identify the most vulnerable locations. The results highlight exposure not only to the commonly studied cities but also to several provincial areas. The identification of exposure is the foremost step towards practical resilience efforts; however, understanding specific infrastructure impacts ensures that countermeasures and risk reduction practices are implemented. Therefore, a detailed evaluation of the NZTA Bridge Manual is conducted. Comparisons are made between the NZTA methodology and the rapid model developed in this research. The results reveal a significant overestimation of bridge and culvert exposure by NZTA methods. The study further highlights critical exposure locations for bridge and culvert assets. Flow depths calculated at bridge locations are significantly overestimated using the NZTA method compared to results derived from hydrodynamic modelling and the rapid model. This research then conducts component-level modelling of culvert assets, due to their identified vulnerability in the transportation network. At a 1:15 geometrical scale, laboratory experiments evaluated the response of different culvert set-ups to tsunami bores. The findings provide a detailed description into overtopping, flow regimes and pressure distributions and give laboratory experiments as validation studies for future numerical modelling and design improvements. Overall, this research performs a multi-modal tsunami inundation assessment, uniting macro-level exposure modelling with micro-level component responses by integrating modelling, exposure analysis, and experimental validation. The findings support refining current tsunami guidelines, improving infrastructure planning, and enhancing community preparedness. Overall, the study’s multi-model approach strengthens many elements of New Zealand’s ability to mitigate and respond to future tsunami events
