This dissertation addresses a diverse range of applied aspects in ground motion simulation validation via the response of complex structures. In particular, the following topics are addressed: (i) the investigation of similarity between recorded and simulated ground motions using code-based 3D irregular structural response analysis, (ii) the development of a framework for ground motion simulations validation to identify the cause of differences between paired observed and simulated dataset, and (iii) the illustration of the process of using simulations for seismic performance-based assessment. The application of simulated ground motions is evaluated for utilisation in engineering practice by considering responses of 3D irregular structures. Validation is performed in a code-based context when the NZS1170.5 (NZS1170.5:2004, 2004) provisions are followed for response history analysis. Two real buildings designed by engineers and physically constructed in Christchurch before the 2010-2011 Canterbury earthquake sequence are considered. The responses are compared when the buildings are subjected to 40 scaled recorded and their subsequent simulated ground motions selected from 22 February 2011 Christchurch. The similarity of recorded and simulated responses is examined using statistical methods such as bootstrapping and hypothesis testing to determine whether the differences are statistically significant. The findings demonstrate the applicability of simulated ground motion when the code-based approach is followed in response history analysis. A conceptual framework is developed to link the differences between the structural response subjected to simulated and recorded ground motions to the differences in their corresponding intensity measures. This framework allows the variability to be partitioned into the proportion that can be “explained” by the differences in ground motion intensity measures and the remaining “unexplained” variability that can be attributed to different complexities such as dynamic phasing of multi-mode response, nonlinearity, and torsion. The application of this framework is examined through a hierarchy of structures reflecting a range of complexity from single-degree-of-freedom to 3D multi-degree-of-freedom systems with different materials, dynamic properties, and structural systems. The study results suggest the areas that ground motion simulation should focus on to improve simulations by prioritising the ground motion intensity measures that most clearly account for the discrepancies in simple to complex structural responses. Three approaches are presented to consider recorded or simulated ground motions within the seismic performance-based assessment framework. Considering the applications of ground motions in hazard and response history analyses, different pathways in utilising ground motions in both areas are explored. Recorded ground motions are drawn from a global database (i.e., NGA-West2 Ancheta et al., 2014). The NZ CyberShake dataset is used to obtain simulations. Advanced ground motion selection techniques (i.e., generalized conditional intensity measure, GCIM) are used for ground motion selection at a few intensity levels. The comparison is performed by investigating the response of an example structure (i.e., 12-storey reinforced concrete special moment frame) located in South Island, NZ. Results are compared and contrasted in terms of hazard, groundmotion selection, structural responses, demand hazard, and collapse risk, then, the probable reasons for differences are discussed. The findings from this study highlight the present opportunities and shortcomings in using simulations in risk assessment. i
The research is funded by Callaghan Innovation (grant number MAIN1901/PROP-69059-FELLOW-MAIN) and the Ministry of Transport New Zealand in partnership with Mainfreight Limited. Need – The freight industry is facing challenges related to climate change, including natural hazards and carbon emissions. These challenges impact the efficiency of freight networks, increase costs, and negatively affect delivery times. To address these challenges, freight logistics modelling should consider multiple variables, such as natural hazards, sustainability, and emission reduction strategies. Freight operations are complex, involving various factors that contribute to randomness, such as the volume of freight being transported, the location of customers, and truck routes. Conventional methods have limitations in simulating a large number of variables. Hence, there is a need to develop a method that can incorporate multiple variables and support freight sustainable development. Method - A minimal viable model (MVM) method was proposed to elicit tacit information from industrial clients for building a minimally sufficient simulation model at the early modelling stages. The discrete-event simulation (DES) method was applied using Arena® software to create simulation models for the Auckland and Christchurch corridor, including regional pick-up and delivery (PUD) models, Christchurch city delivery models, and linehaul models. Stochastic variables in freight operations such as consignment attributes, customer locations, and truck routes were incorporated in the simulation. The geographic information system (GIS) software ArcGIS Pro® was used to identify and analyse industrial data. The results obtained from the GIS software were applied to create DES models. Life cycle assessment (LCA) models were developed for both diesel and battery electric (BE) trucks to compare their life cycle greenhouse gas (GHG) emissions and total cost of ownership (TCO) and support GHG emissions reduction. The line-haul model also included natural hazards in several scenarios, and the simulation was used to forecast the stock level of Auckland and Christchurch depots in response to each corresponding scenario. Results – DES is a powerful technique that can be employed to simulate and evaluate freight operations that exhibit high levels of variability, such as regional pickup and delivery (PUD) and linehaul. Through DES, it becomes possible to analyse multiple factors within freight operations, including transportation modes, routes, scheduling, and processing times, thereby offering valuable insights into the performance, efficiency, and reliability of the system. In addition, GIS is a useful tool for analysing and visualizing spatial data in freight operations. This is exemplified by their ability to simulate the travelling salesman problem (TSP) and conduct cluster analysis. Consequently, the integration of GIS into DES modelling is essential for improving the accuracy and reliability of freight operations analysis. The outcomes of the simulation were utilised to evaluate the ecological impact of freight transport by performing emission calculations and generating low-carbon scenarios to identify approaches for reducing the carbon footprint. LCA models were developed based on simulation results. Results showed that battery-electric trucks (BE) produced more greenhouse gas (GHG) emissions in the cradle phase due to battery manufacturing but substantially less GHG emissions in the use phase because of New Zealand's mostly renewable energy sources. While the transition to BE could significantly reduce emissions, the financial aspect is not compelling, as the total cost of ownership (TCO) for the BE truck was about the same for ten years, despite a higher capital investment for the BE. Moreover, external incentives are necessary to justify a shift to BE trucks. By using simulation methods, the effectiveness of response plans for natural hazards can be evaluated, and the system's vulnerabilities can be identified and mitigated to minimize the risk of disruption. Simulation models can also be utilized to simulate adaptation plans to enhance the system's resilience to natural disasters. Novel contributions – The study employed a combination of DES and GIS methods to incorporate a large number of stochastic variables and driver’s decisions into freight logistics modelling. Various realistic operational scenarios were simulated, including customer clustering and PUD truck allocation. This showed that complex pickup and delivery routes with high daily variability can be represented using a model of roads and intersections. Geographic regions of high customer density, along with high daily variability could be represented by a two-tier architecture. The method could also identify delivery runs for a whole city, which has potential usefulness in market expansion to new territories. In addition, a model was developed to address carbon emissions and total cost of ownership of battery electric trucks. This showed that the transition was not straightforward because the economics were not compelling, and that policy interventions – a variety were suggested - could be necessary to encourage the transition to decarbonised freight transport. A model was developed to represent the effect of natural disasters – such as earthquake and climate change – on road travel and detour times in the line haul freight context for New Zealand. From this it was possible to predict the effects on stock levels for a variety of disruption scenarios (ferry interruption, road detours). Results indicated that some centres rather than others may face higher pressure and longer-term disturbance after the disaster subsided. Remedies including coastal shipping were modelled and shown to have the potential to limit the adverse effects. A philosophical contribution was the development of a methodology to adapt the agile method into the modelling process. This has the potential to improve the clarification of client objectives and the validity of the resulting model.
