The seismic performance of soil profiles with potentially liquefiable deposits is a complex phenomenon that requires a thorough understanding of the soil properties and ground motion characteristics. The limitations of simplified liquefaction assessment methods have prompted an increase in the use of non-linear dynamic analysis methods. Focusing on onedimensional site response of a soil column, this thesis validated a soil constitutive model using in-situ pore pressure measurements and then assessed the influence of input ground motion characteristics on soil column response using traditional and newly developed metrics. Pore pressure recordings during the Canterbury Earthquake Sequence (CES) in New Zealand were used to validate the PM4Sand constitutive model. Soil profile characterization was key to accurate prediction of excess pore pressure response and accounting for any densification during the CES. Response during multiple earthquakes was captured effectively and cross-layer interaction demonstrated the model capability to capture soil response at the system-level. Synthetic and observed ground motions from the Christchurch earthquake were applied to the validated soil column to quantify the performance of synthetic motions. New metrics were developed to facilitate a robust comparison to assess performance. The synthetic input motions demonstrated a slightly larger acceleration and excess pore pressure response compared to the observed input motions. The results suggest that the synthetic motions may accumulate higher excess pore pressure at a faster rate and with fewer number of cycles in the shear response. This research compares validated soil profile subject to spectrally-matched pulse and non-pulse motions, emphasizing the inclusion of pulse motions with distinctive characteristics in ground motion suites for non-linear dynamic analysis. However, spectral matching may lead to undesired alterations in pulse characteristics. Cumulative absolute velocity and significant duration significantly differed between these two groups compared to the other key characteristics and contributed considerably to the liquefaction response. Unlike the non-pulse motions, not all of the pulse motions triggered liquefaction, likely due to their shorter significant duration. Non-pulse motions developed a greater spatial extent of liquefaction triggering in the soil profile and extended to a greater depth.
The susceptibility of precast hollow-core floors to sustain critical damage during an earthquake is now well-recognized throughout the structural engineering community in New Zealand. The lack of shear reinforcement in these floor units is one of the primary reasons causing issues with the seismic performance of these floors. Recent research has revealed that the unreinforced webs of these floor units can crack at drift demands as low as 0.6%. Such observation indicates that potentially many of the existing building stock incorporating hollow-core flooring systems in cities of relatively high seismic activity (e.g. Wellington and Christchurch) that probably have already experienced a level of shaking higher than 0.6% drift in previous earthquakes might already have their floor units cracked. However, there is little information available to reliably quantify the residual gravity load-carrying capacity of cracked hollow-core floor units, highlighting the need to understand the post-cracking behavior of hollow-core floor units to better quantify the extent of the risk that cracked hollow-core floor units pose.
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.
