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.
Geosynthetic reinforced soil (GRS) walls involve the use of geosynthetic reinforcement (polymer material) within the retained backfill, forming a reinforced soil block where transmission of overturning and sliding forces on the wall to the backfill occurs. Key advantages of GRS systems include the reduced need for large foundations, cost reduction (up to 50%), lower environmental costs, faster construction and significantly improved seismic performance as observed in previous earthquakes. Design methods in New Zealand have not been well established and as a result, GRS structures do not have a uniform level of seismic and static resistance; hence involve different risks of failure. Further research is required to better understand the seismic behaviour of GRS structures to advance design practices. The experimental study of this research involved a series of twelve 1-g shake table tests on reduced-scale (1:5) GRS wall models using the University of Canterbury shake-table. The seismic excitation of the models was unidirectional sinusoidal input motion with a predominant frequency of 5Hz and 10s duration. Seismic excitation of the model commenced at an acceleration amplitude level of 0.1g and was incrementally increased by 0.1g in subsequent excitation levels up to failure (excessive displacement of the wall panel). The wall models were 900mm high with a full-height rigid facing panel and five layers of Microgird reinforcement (reinforcement spacing of 150mm). The wall panel toe was founded on a rigid foundation and was free to slide. The backfill deposit was constructed from dry Albany sand to a backfill relative density, Dr = 85% or 50% through model vibration. The influence of GRS wall parameters such as reinforcement length and layout, backfill density and application of a 3kPa surcharge on the backfill surface was investigated in the testing sequence. Through extensive instrumentation of the wall models, the wall facing displacements, backfill accelerations, earth pressures and reinforcement loads were recorded at the varying levels of model excitation. Additionally, backfill deformation was also measured through high-speed imaging and Geotechnical Particle Image Velocimetry (GeoPIV) analysis. The GeoPIV analysis enabled the identification of the evolution of shear strains and volumetric strains within the backfill at low strain levels before failure of the wall thus allowing interpretations to be made regarding the strain development and shear band progression within the retained backfill. Rotation about the wall toe was the predominant failure mechanism in all excitation level with sliding only significant in the last two excitation levels, resulting in a bi-linear displacement acceleration curve. An increase in acceleration amplification with increasing excitation was observed with amplification factors of up to 1.5 recorded. Maximum seismic and static horizontal earth pressures were recorded at failure and were recorded at the wall toe. The highest reinforcement load was recorded at the lowest (deepest in the backfill) reinforcement layer with a decrease in peak load observed at failure, possibly due to pullout failure of the reinforcement layer. Conversely, peak reinforcement load was recorded at failure for the top reinforcement layer. The staggered reinforcement models exhibited greater wall stability than the uniform reinforcement models of L/H=0.75. However, similar critical accelerations were determined for the two wall models due to the coarseness of excitation level increments of 0.1g. The extended top reinforcements were found to restrict the rotational component of displacement and prevented the development of a preliminary shear band at the middle reinforcement layer, contributing positively to wall stability. Lower acceleration amplification factors were determined for the longer uniform reinforcement length models due to reduced model deformation. A greater distribution of reinforcement load towards the top two extended reinforcement layers was also observed in the staggered wall models. An increase in model backfill density was observed to result in greater wall stability than an increase in uniform reinforcement length. Greater acceleration amplification was observed in looser backfill models due to their lower model stiffness. Due to greater confinement of the reinforcement layers, greater reinforcement loads were developed in higher density wall models with less wall movement required to engage the reinforcement layers and mobilise their resistance. The application of surcharge on the backfill was observed to initially increase the wall stability due to greater normal stresses within the backfill but at greater excitation levels, the surcharge contribution to wall destabilising inertial forces outweighs its contribution to wall stability. As a result, no clear influence of surcharge on the critical acceleration of the wall models was observed. Lower acceleration amplification factors were observed for the surcharged models as the surcharge acts as a damper during excitation. The application of the surcharge also increases the magnitude of reinforcement load developed due to greater confinement and increased wall destabilising forces. The rotation of the wall panel resulted in the progressive development of shears surface with depth that extended from the backfill surface to the ends of the reinforcement (edge of the reinforced soil block). The resultant failure plane would have extended from the backfill surface to the lowest reinforcement layer before developing at the toe of the wall, forming a two-wedge failure mechanism. This is confirmed by development of failure planes at the lowest reinforcement layer (deepest with the backfill) and at the wall toe observed at the critical acceleration level. Key observations of the effect of different wall parameters from the GeoPIV results are found to be in good agreement with conclusions developed from the other forms of instrumentation. Further research is required to achieve the goal of developing seismic guidelines for GRS walls in geotechnical structures in New Zealand. This includes developing and testing wall models with a different facing type (segmental or wrap-around facing), load cell instrumentation of all reinforcement layers, dynamic loading on the wall panel and the use of local soils as the backfill material. Lastly, the limitations of the experimental procedure and wall models should be understood.
