The Southern Ocean is the birthplace of the fiercest waves on the Earth that play a fundamental role in the global climate by regulating momentum, heat, and gas exchanges between the atmosphere and the ocean. At high latitudes, waves interact with Antarctic sea ice, another crucial player of the Earth's climate, modulating its expansion in winter and retreat in summer, hence affecting the global albedo. Despite the importance of waves for the climate system, global wave models are considerably biased in the Southern Ocean, due to the scarcity of observations in these remote waters. This study presents a unique data set of simultaneous observations of winds, surface currents, and ocean waves acquired underway during the Antarctic Circumnavigation Expedition (ACE) that went around the Southern Ocean from December 2016 to March 2017 (Austral summer). Discrepancies in the estimation of surface current velocities are demonstrated from a comparison of the data set against satellite observations and model predictions. It is discussed how these observations underpin the setup, calibration, and validation of the WaveWatch-III wave model over a domain covering the entire Southern Hemisphere. The calibrated model is then evaluated for the accuracy of wave predictions in the Southern Ocean. The wave heights and periods are well predicted by the model although, a slight overestimation of wave heights is present. This study shows that large biases of wave predictions at high latitudes are linked to the ice cover around Antarctica. It is also demonstrated that accounting for surface current has limited contribution in the reduction of wave prediction biases in the Southern Ocean. The observations are also used to thoroughly assess the performance of global wave models, namely the CAWCR hindcast and the ECMWF ERA-5 reanalysis, in this region. The CAWCR hindcast notably overestimates wave heights in the Southern Ocean however, the biases are shown to be considerably reduced in the ERA-5 analysis that benefits from data assimilation. The effects of data assimilation on wave predictions in the Southern Ocean are fully assessed by comparing the MFWAMv4 wave model's performance with and without data assimilation. Results show improved model performance due to assimilation of remotely sensed observations.

Australian public policy references to building information modelling (BIM), a 3D digital attributable object based built environment representation capability, continue to evolve. Whilst BIM is heralded as a means to lift productivity in the architecture, construction and engineering industry, developments are converging with other emerging narratives. The shifting intent has meant different objectives, opportunities and challenges are presented, particularly for broad and socially orientated applications that involve BIM. However, public policy responses remain unresolved, whether to reconcile or leverage these Big Picture perspectives or overcome prevailing limitations. As a result, directions for public procurement of infrastructure projects in view of BIM remain unclear. In progressing policy orientated debates, the current thesis presents constructive-interpretivist research to develop public procurement policy options aimed at capitalising on the opportunity afforded by BIM for infrastructure projects. From the abductive conceptual study, unique theoretical informational systems-based perspectives and concepts are developed to interpret BIM and its place in context. BIM systems (technology, modelling and model) are distinguished from other forms and features of informational systems. An idealised frame for policy development is proposed, setting the relativity of BIM systems at scale and evolutionary economics-based orders of rules. Application of the theoretical perspectives is presented and validated through a deductive case study of nested “systems”. Focusing on the roads sector between early 2017-2018, the resulting constructed narrative vertically profiles Australian organisational, project, industry and government positions and policies in view of BIM. Comparative qualitative analyses expose policy distinctions and coordination risks in terms of “BIM”, BIM systems, pre-BIM states and other systems characteristics. Predicted transformational implications draw from the consistent omission or narrow referencing of “rules” relating to BIM and the roads sector. However, outcomes highlight that despite indicative embryonic features, the underlying pre-text of analogous BIM characteristics (including functionality) is also qualified. Even before getting to BIM, the necessity and value propositions for consolidated or integrated and granular detailing and performance about the built environment is underdeveloped. Responding to the identified issues and risks, the research qualifies the demand for BIM to assist decision makers understand the extent and investment of proposed policy changes in response to BIM. The point of BIM is reset to engender scaled productivity towards “digital infrastructure” and reconcile business as usual presumptions and the creative-destructive implications of change. The recommended policy options target multi-stakeholder interventions and mechanisms that range from the development of strategic internal positioning on BIM for the road agency and extend through to a proposed intergovernmental agreement that advances capability in the Australian built environment sector. In preparation for BIM rather than “BIM” per se, the public policy options are developed as a first pass step for the future development of more comprehensive, longer term programs. From the theoretical perspectives, this includes acknowledging data or digital based approaches as a necessary but qualified step towards predicted future directions predicated on informational systems outcomes.

Impact-resistant design of reinforced concrete (RC) structures has a prominent role in mitigating hazards caused by extreme events. An example of such a structure is the RC barrier that serves as a protective measure in mountainous areas to protect lives and built infrastructure. Impact actions can be divided into localised and global actions. Local actions are controlled by the amount of impact force generated at the point of contact, and it is referred herein as contact force. The magnitude of contact force can be much higher than the quasi-static force, which can cause permanent damage to the structure. Gabion cushion layers are commonly used to protect localised damage in RC passive defence measures. Gabions are expensive and require extensive maintenance. Other structural concrete elements exposed directly to collision hazards or an abrasive environment are still required to design to withstand the contact forces developed during projected impact scenarios. Localised damage in the form of denting, spalling and punching failure are primarily resulted in RC members subjected to low-velocity impact actions. Predicting localised damage by impact action is more complex than global effects as the prediction depends on the determination of highly transient forces and stresses. These impact forces are highly influenced by the material behaviour and structural dynamic behaviour of colliding objects, which have not been incorporated in the expressions stipulated in contemporary codes of practices. Due to the complex nature of the impact, a significant amount of studies in the literature to date on localised damage are based on empirical data obtained from experimentation or numerical simulations. The major limitation with the empirical modelling is uncertainties over the scope of applicability as the derived relationships employ a specific range of impact events. Models developed based on theories also have limitations, but they are more transparent and can be amended to suit the conditions of impact, thereby has the merit of generality. Because of that, theoretical models consisting of closed-form expressions and are executable on MATLAB and Excel are proposed in this research project to determine the localised failure of RC structures. The recent development of the Hunt and Crossley theoretical model for accurately predicting the impact force has been reported for windborne debris and hail impact. The main drawback of this methodology is that the determination of model parameters requires calibration against impact experimentations using spherical impactors. In this research, an inexpensive experimental procedure based on the use of compression testing of cylindrical specimens of impactor and target objects is proposed to determine the model parameters. The innovation presented in this study waives away the need for costly and time-consuming impact experimentations and preparation of spherical samples. The accuracy of the proposed methodology to predict the impact force has been validated against impact experimentations and finite element simulations for impact by granite on concrete surfaces. This deterministic methodology can be capitalised for predicting damage to concrete. Denting and spalling of concrete on the impact surface is caused by the stress developed in the vicinity of the contact region. This study elaborates the adaptation of an analytical model derived from the fundamental theories of solid mechanics for the determination of transient effective stress contours. The accuracy of the simulated stress contours has been verified employing FE simulations which were previously validated against experimental measurements. The calculated stress contours at the instance of peak impact force and hardness of the target that is subjected to strike are to be employed to estimate the degree of damage in the proposed procedure. This theoretically based model to predict the occurrence of denting and spalling has been verified with large-scale experimentations on RC barrier specimens using solid steel and rock impactors. A deterministic analytical model has been developed to predict the occurrence of punching failure in RC based on conventional “free-body diagram” analysis. The proposed model accounts for the transient actions of impact (impact force and inertia force) generated within the shear plug in combination with material resistant forces. Given that the impact force can be predicted using the previously developed deterministic methodology, a numerical procedure that can be implemented on MATLAB or Excel to calculate the inertial resistance is introduced. The proposed punching shear failure predictive model has been verified by pendulum style impact experimentations on RC wall specimens. Further validations are incorporated employing drop test results on RC beam specimens reported in the literature. Predictive models presented in this thesis to determine the local response behaviour of RC are beneficial in the design of RC structures that are constructed in the proximity of collision hazards. Worked examples and step-by-step procedures are illustrated individually in the corresponding chapters of the thesis. In order to facilitate the uptake of the proposed methodology for industrial applications, complete design guidelines combining the deterministic approaches investigated in this research project with detailed calculation steps are presented towards the end of the thesis.

As the environmental problems have attracted more and more attention over the recent years, the cement production in concrete industry has become a global concern. In the concrete construction industry, ordinary Portland cement (OPC), as an important binder in concrete, is identified to be the major cause of energy cost and carbon emissions Thus to reduce the environmental impact caused by the cement production, sustainable concrete with the use of less cement has become a necessity. Supplementary cementitious materials (SCMs) have been developed to be used as admixtures in concrete. Fly ash (FA) which is one of the most commonly used SCMs is a waste material from the combustion of coal in electricity stations. It has a large storage over the world and continues to be produced over time. However, the most significant problem of concrete with FA, especially large amount of FA, is the slow early age strength development. A comprehensive literature review on different properties of FA concrete are first provided including mechanical properties, setting time, heat of hydration, workability, self-compacting concrete, shrinkage and creep, several major durability properties (chloride ingress, sulfate attack, carbonation, alkali-silica reaction) and microstructure. Thus the general properties of FA concrete could be better understood. The hydration and strength properties of FA concrete are further investigated for concrete with the incorporation of FA from different regions. The FA were sourced from both Indonesia and Australia including Gladstone, Port Augusta and Bayswater. It was found that the main difference of the FA in different regions is the particle size distribution and the differences in chemical composition. Higher fineness of FA particles leads to higher hydration rate and strength. The degree of hydration and chemically bound water (WB) is linearly correlated with the compressive strength. To improve the early age strength of the overall high volume fly ash (HVFA) concrete mixes, The HVFA concrete mixes were optimized by improving the aggregate grading, reducing the water to binder (w/b) ratio and adjusting the paste to void volume ratio (Vp/Vv). It was found that the concrete mainly fail in pastes rather than aggregate. Thus, the early age strength of HVFA pastes by the adding of admixtures was especially determined. The admixtures used to improve the compressive strength of HVFA pastes were nano-CSH crystals, calcium formate (CF), and hydrated lime (HL). The underlying mechanism of how theses admixtures work in the pastes was investigated by hydration and microstructural testing approaches. It was found that the adding of single nano-CSH crystals, CF or HL could improve the compressive strength of HVFA pastes due to different mechanisms. However, the addition of combined CF and HL decreased the strength mainly due to cracks and pores caused by rapid hydration. As the adiabatic temperature rise is an important parameter that could significantly affect the properties of concrete, the adiabatic temperature rises of OPC and FA concrete were modelled from the heat of hydration curves. The accuracy of the modelling was successfully improved by the adjustment of hydration parameters. Finally, machine learning (ML), as a statistical tool, was used in this research to predict the compressive strength of HVFA composites with the addition of different admixtures. The accuracy of the model could be further improved by increasing the data sets.

Concrete as a building material has been utilised in human civilisation for centuries. Featuring both strength and durability, concrete structures have prolonged life span when carefully designed, constructed and maintained. Modern concretes employ a variety of ingredients to achieve different material and structural performance requirements. One of the major concerns in construction of concrete members is the cracking that takes place during construction or with 1-7 days after the concrete placement. This period is referred to as “early age”. Cracking tendency of early age concrete is governed by a race between the strength gain over time and the in-situ stresses experienced by the member. Concrete members are restrained by its hardening internally or by the adjacent existing members. During early ages, it is understood, when strength of the member is surpassed by the stress, which happens primarily due to temperature development, cracking is likely to happen. Among the identified factors, thermal deformation is considered as one of the major effects that the concrete experiences. As stated earlier, concrete expands due to heat accumulation due to chemical exothermic reaction, known as cement hydration and contracts when rate of reaction slows down. If the concrete member is left unrestrained, the member will not experience any stresses and, therefore, the concrete will have little to no cracking risk. However, dilatation of concrete elements is almost always restrained, to some degree, due to member geometry resulting in differential dilation due to thermal gradient, internal steel elements or restrained externally due to the presence of adjacent structural members. In this research, the author focussed on the stress and strength evolution based on thermal effects experienced by early age concrete members. The primary aim of this project is to establish a reliable and simple method that describes the temperature induced stress development of early age concrete. In fulfilling the aim of this research, the key contributions of the thesis comprise of four models proposed to assist designers with various early-age thermal stress issues with concrete elements. They are: A spreadsheet-based temperature field model (Model A) for early age concrete is presented. The model can calculate temperature profiles at locations within a hydrating concrete block. The model is based on the concept of effective thickness and revised heat compensation technique. The model has proven to be able to estimate the peak temperatures at the core and the surface of the slab to a reasonable accuracy. Following the development of Model A, a simplified predictive thermal stress model (Model B) is proposed. This model is validated by comparison with output from FEM which calculates the thermal stress of early age concrete as well as thermal recordings from in-situ measurement. Model B is able to predict the stress profile within a concrete member and the critical location of tensile stress which corresponds to be the point with largest temperature difference to the thermal core. To assist the use of Model B, a neural network (NN) model (Model C) for the prediction of the adiabatic temperature rise (ATR) of concrete mixes is developed. The model is trained with 14 sets of ATR parameters derived from the Triple Parameter Equation (TPE)expression. The parameters are determined through a gradient descent algorithm. The result suggests that the TPE represents the ATR of concrete mix with good accuracy. Lastly, to promote the use of steel fibres (SF) in improving the flexural strength of concrete and as a solution to early-age cracking, a simplified prediction model (Model D) which is able to estimate the effect of SF in flexural strength of concrete beam was proposed. Addition of steel fibres (SF) can improve the ductility, flexural and tensile strength properties of concrete. The model is based on a combination of classical bending stress model and probability distribution of the area of SF within the section.

Major blast events have occurred annually in several regions around the world. Accordingly, building codes, design standards and structural design recommendations are of paramount importance to protect occupants and property against unpredictable blast events. Cross-laminated timber (CLT) has recently emerged as a sustainable and lightweight engineered wood product. CLT offers several advantages as a construction material, in terms of both mechanical properties and environmental protection, including a high stiffness-to-weight ratio, a high two-way stiffness, and a low embodied carbon footprint. The increasing use of CLT in structural members combined with emerging threats highlight the importance of improving its resilience to blast loads. The study on the performance of CLT under blast loadings is significant to protect important structural elements and improve their resilience to blast loads. CLT possesses a lamellar structure, similar to that of marine seashells such as conch shells. A conch shell is primarily composed of brittle minerals (over 99% aragonite) but boasts a high fracture toughness due to its unique lamellar structure. By taking inspiration from the striking resemblance between the lamellar structure of the conch shell and CLT, this research aims to develop an innovative bioinspired CLT structure with superior resilience to blast loadings. Specifically, three main research areas are reviewed, namely blast loading, bio-inspired armour systems and cross-laminated timber. A comprehensive review is conducted on these topics to highlight the significance of protective structures against blast loading, the toughening mechanisms of biological armour systems, and the need for enhancing the performance of CLT under blast loadings. The review emphasises the lack of studies on the behaviour of CLT under blast loadings to improve its toughness and resilience in an explosion. Moreover, a striking resemblance between CLT and biological structures such as conch shell offers innovative solutions for increasing the toughness of CLT through bio-mimicking techniques. With this knowledge, the feasibility of mimicking the micro architecture of the conch shell on a larger scale to enhance the toughness of conch-like CLT is investigated. Programable 3D printing instructions were used to manipulate the 3D printer to develop tough conch-like prototypes. The prototypes were tested under single-edge notched tension to investigate their fracture behaviour. Then, a numerical model was developed and validated using these experimental data and an analytical solution. The model employed to examine the toughening mechanisms in the innovative proof of concept conch-like structure. A parametric study was also conducted to investigate the effect of different parameters on the toughening behaviour of the conch-like prototypes. A finite element (FE) model was proposed to simulate the behaviour of CLT under both quasi-static and dynamic loadings. The FE model was validated using experimental results and subsequently employed to simulate the bio-inspired CLT panel under both quasi-static and blast loads. An analytical solution was also proposed to capture the behaviour of CLT panels under blast loadings to validate the FE model. This validated FE model was used to conduct a numerical study on the performance of bio-inspired CLT under blast loading. In this study, the lamellar arrangement in the conch shell structure was mimicked to improve the toughness of a conch inspired CLT panel subjected to blast loadings. Several key parameters from the conch shell were also mimicked to enhance the toughness of CLT panels, namely the lamellar arrangement and the interlocking mechanisms. These bioinspired CLT panels were investigated by conducting numerical simulations of four-point bending tests. As such, several design recommendations were provided to enhance the performance of the conch-inspired CLT including changing the cross-section of timber planks in the middle layer of a CLT panel, introducing carbon fibre composite layers for ductility improvement, using pins to enhance interlocking mechanisms and adjusting the mechanical properties of the bonding adhesive. The bioinspired CLT panel was shown to exhibit several performance benefits over its benchmark counterpart, namely increased stiffness, strength and toughness. Finally, the conclusions of this research project and directions for future work are also provided.

Urbanization and population growth intensify the problems associated with traffic congestion in metropolitan areas all over the world. Therefore, researchers always seek to find innovative solutions to enhance the performance of transport systems in terms of safety, mobility, and environmental sustainability. Consequently, the optimal design of signal control parameters concerning real-time traffic congestion has been the subject of extensive research for many years. The states of the art traffic signal control methods mainly use the data from infrastructure-based sensors such as loop detectors and video cameras. However, these sensors are mainly spot detectors and are only able to sense the presence of vehicles in specific parts of the network. Therefore, they are unable to provide an overall insight into the traffic situation in the whole network. As a result, the infrastructure-based signal controllers are not fully adaptive, and the acquired data from these types of sensors are only applied to make some minor changes in the predesigned signal plans. Furthermore, the infrastructure-based sensors are associated with some drawbacks such as high installation and maintenance cost as well as inaccuracy and rate of failure. This research is motivated by the recent advancements in communication technologies as well as intelligent transportation system applications. These technologies make it possible for vehicles equipped with onboard units (OBUs) to exchange their information such as position, speed, acceleration/deceleration with other equipped vehicles, and roadside units (RSUs). The collected data by the RSUs can then be used to realize the spatial real-time traffic situation in the network based on which the traffic signal controllers can make smarter and more informed decisions. Given the enriched data obtained from connected vehicles (CVs), the traffic signal control problem can be formulated based on data-driven and mathematical methods to provide an optimal signal control plan. Reviewing the literature concerning signal control strategies in a CV system, the following three main research gaps are identified and addressed in this research: Most of the current literature on traffic signal control in a connected vehicle system is tailored for a condition in which all or the majority of vehicles are connected. However, the generalisability of this type of research on this issue is problematic. Since these algorithms cannot work appropriately when there exists a mixture of ordinary and connected vehicles. During the last decade, a considerable amount of literature has been published on traffic signal control in a connected vehicle environment. However, only a few are concerned about the network level to consider the coordination between intersections. Most of the algorithms are simply designed for a single intersection without any consideration of the interaction between adjacent intersections. The majority of the existing network-wide signal control algorithms suffer from computational complexity which prevents them to be real-time implementable. To address the first research gap, this research develops data-driven estimation methods to estimate the traffic states based on the data acquired from a limited number of connected vehicles in mixed traffic of connected and ordinary vehicles. To deal with the second research gap, a rolling horizon optimization strategy is developed to determine the optimal signal plans of all intersections for the next time step considering the current estimated traffic situation from connected vehicles. The third gap in the literature is also addressed by introducing a network decomposition algorithm to reduce the computational complexity of the optimization problem to be real-time implementable. This study contributes to the literature in the following areas: Data-driven traffic states estimation algorithms are proposed to estimate the traffic condition even when only a limited number of vehicles are connected in a transport network (say at least 30%). Traffic state estimation algorithms in this research have an aggregated approach and do not record the vehicle trajectories in any form. Therefore, the privacy of drivers in charge of connected vehicles is protected. Connected vehicle data is the only required input for estimation methods and the proposed algorithms do not require the information of any infrastructure-based sensors. The flow estimation algorithm is also extended to fuse the data from connected vehicles and Bluetooth sensors to provide accurate traffic estimation results in situations with very low market penetration rates of connected vehicles. A rolling horizon optimization strategy is applied in this research to determine the optimal timing plans of all traffic signals in a network of intersections. A network decomposition algorithm is introduced to split the network into several smaller subnetworks and convert the centralized signal control optimization problem to a semi-centralized approach. The suggested semi-centralized control strategy has a significantly reduced computational time in comparison with its centralized counterpart. The affordable computational time makes the model applicable for real-time implementation. The integration of estimation and optimization algorithms results in better performance of the proposed traffic signal plan (in terms of mobility indexes such as travel time, number of stops, average speed, queue length, and emissions) compared with a base case actuated coordinated signal plan where the penetration rate of the connected vehicles is 30% or more.

The quality of data in spatial databases greatly affects the performance of location-based applications that rely on maps such as emergency dispatch, land and property ownership registration, and delivery services. The negative effects of such dirty map data may range from minor inconveniences to life-threatening events. Data cleaning usually consists of two steps - error detection and error rectification. Data cleaning is a demanding and lengthy process that requires manual interventions of data experts, in particular where for complex situations involving the consistency of relationships between multiple objects. This thesis presents computational methods developed to automate the detection of errors in map databases and ease the demand for human resources in error detection. These methods are intrinsic, ie., depend only on data being analysed, without the need for a reference dataset. Two models for ternary spatial relations were developed to enable the analyses not possible with existing binary spatial relations. First, the Refined Topological relations model for Line objects (RTL) examines whether the core line object is connected to its surrounding objects on both or only one of its ends. This distinction is particularly important in networks where connectedness determines the function of the object. Second, the Ray Intersection Model (RIM) casts rays between two peripheral objects and uses the intersection sets between these rays and the core object to model its relation to peripheral objects. This provides a basis for reasoning about the core object being between peripheral objects. Both models have been computationally implemented and demonstrated on error detection tasks in OpenStreetMap. The case studies on data for the State of Victoria, Australia demonstrate that the methods developed in this research are effectively detecting errors that could so far not be automatically identified. This research contributes to automated spatial data cleaning and quality assurance, including reducing experts' workload by effectively identifying potential errors.

In everyday communications, people talk about space by referring to places. While the common sense notion of place is understandable to humans, formalising place in a computational model remains a challenging issue. The strong context dependency, diverse metaphorical uses, indeterminacy of boundaries, and vernacular reference use are major challenges in making place knowledge digestible for computers. This research aims to utilise domain knowledge to study place-related questions and their corresponding answers, and to develop models and methods to answer the questions. In the context of place-related question answering, this study investigates what people expect from computers to understand about places, and how these place-related questions are answered in human-generated responses. First, a place model is designed for the question answering purpose using the collective domain knowledge extracted from literature. Later, the model is used to characterize the platial information in place-related questions and their human-generated answers. In the next step, the natural language questions are translated to GeoSPARQL queries to enable the spatial analysis for answering place-related questions. Finally, templates for answering where-questions are proposed to generate relevant responses similar to human-generated answers. The results of this study show that domain knowledge can be used to improve current methods of place-related question answering. Using domain knowledge, an encoding method is devised that can characterise large question answering corpora with minimal supervision. The encoding results are used to identify descriptive patterns inside the questions and answers. In the next step, a novel approach is designed using domain knowledge and object-based conceptualization of place to translate natural language questions to GeoSPARQL queries. The novelty of the approach is mainly to (1) use domain knowledge and avoid reinventing new terms, and (2) utilise FOL statements as the intermediate representation which can be later translated not only to GeoSPARQL but any other formal query languages with minimal efforts. The method is tested using the Geospatial Gold Standard dataset, and the results show significant improvements in extracting information and translating questions to queries in comparison to the state-of-the-art approaches. Finally, the relevance of answers to where-questions is investigated using templates of generic information (i.e., type, scale and prominence). The results show that generic representations can be used to characterise answers in a few frequent patterns and also to study relevance of answers to the questions. Moreover, the extracted knowledge can be captured using sequence prediction methods in a machine digestible manner. The results of this study can be used to test the relevance of machine-generated responses or to generate automatic responses similar to human-generated answers. Overall, this thesis contributes to the domain of geographic question answering with a focus on geographic places. The results of this study can be used in question answering systems to analyse and classify the questions, generate queries and formulate relevant responses. The results of this study show the importance of domain knowledge in improving the performance of existing question answering systems, and also provide useful insights about human answering behaviour.

The ability to accurately model and simulate the interactions between pedestrians and the natural environment is a matter of interest in the crowd dynamics field. A primary objective is to optimise the design of entry and exit points and thus provide safe passage in crowded venues such as schools, theatres, mosques, airports, railway stations, concert halls and football stadiums. Therefore, understanding the dynamics of crowd behaviour is important for improving the safety of crowds. People’s movements are affected by interactions with other individuals and the environment. The interactions between humans and physical objects are of particular concern in crowd movement, especially during an emergency, and require further study. Pedestrian simulation has been recognised as a tool that provides a robust framework for understanding crowd dynamics in a complex environment and for predicting crowd density during an extreme event. However, for pedestrian simulations to produce reliable numerical simulation outputs, they must be calibrated using reliable experimental data so that they can produce reasonable results. Therefore, investigating the effects of factors such as pedestrian competition levels in normal and emergency conditions, and crowd density on the behaviour of pedestrians is an important topic. In this study, we performed experiments focusing on the interaction of crowds and their surrounding physical situation; specifically, we observed how pedestrians avoid obstructions in a compound indoor environment at different speed levels (low–high) and density levels (low–high). This research aimed to study the effect of the various sizes of obstacles (1.2 m, 2.4 m, 3.6 m and 4.8 m) on human behaviour (walking and running) at particular density levels (or flow rates). Several factors that affect the movement of pedestrians around objects were studied using macro-and micro-level approaches. The results were then utilised to enhance a pedestrian simulation model developed at the University of Melbourne over the past 10 years. The outcome of this study was used to investigate the obstacles' positions, the exit locations, and the placement of obstacles around the exit to improve the movement of crowds under normal and emergency conditions.

Bridges are a critical component of transportation network. The long-term maintenance of bridges represent around 30% of the financial value of transport infrastructure. As the bridge ages, it requires adequate maintenance against deterioration to ensure safety and serviceability. Therefore, periodic inspections for regular condition monitoring are vital for timely implementation of the maintenance strategies. The purpose of this doctoral research is to reduce the burden of data collection for bridge management systems (BMS) by developing innovative non-destructive testing (NDT) techniques, which could quickly check the bridge elements for damage identification. This study mainly focused on the bridge concrete deck and bearings. Current bridge deterioration models are based on the condition state of the individual elements of the bridge. The bridge condition data is conventionally collected through visual and physical inspection of the respective elements, which are labour intensive and expensive in term of time and money. Furthermore, these techniques are subjective and unable to detect the concealed subsurface defects, which could be more expensive to repair later on if they are unable to be detected in time. For the effective bridge condition rating, the integration of NDT techniques and the conventional visual inspections could potentially overcome the deficiencies associated with the conventional inspection methods. The largest portion of expenditures on bridge maintenance goes to the deck only. Subsurface delamination in concrete bridge decks is a widespread problem due to the corrosion of reinforcement in concrete deck. Infrared thermography (IRT) has been identified as an effective NDT technique that can remotely scan the concrete bridge members for subsurface delamination detection. However, there were still several uncertainties and deficiencies in using IRT for efficient bridge inspections. These uncertainties are highlighted and the potential advancements in knowledge are proposed in the current study. Since IRT uses the thermal profile of concrete surface to identify the subsurface damages, the surrounding environmental parameters have significant impact on IRT results. Specially, this study focused on investigating the optimum environmental conditions for IRT application on bridge deck exposed to direct solar radiations, the application of IRT for bridge members that are not exposed to direct solar radiations and the quantitative characterisation of subsurface defect using IRT. The research outcomes will contribute to the determination of optimum IRT inspection time for different defects (e.g. size and depth) under various weather conditions, in particular for defects in bridge members that have difficulties for the implementation of IRT (e.g. not directly exposed to sun radiation). The bridge bearings provide a resting surface for the superstructure. Repetitive traffic loading and harsh environmental conditions could cause significant deterioration of the mechanical stiffness of the bridge bearings, which may affect the performance of the overall bridge structural system. In this study, a remote radar-based NDT technique (IBIS-FS) was proposed for condition assessment of bridge bearings. By establishing the relationship between the fundamental frequency of the bridge superstructure and support conditions, a simplified analytical approach in conjunction with the IBIS-FS radar bridge inspection is proposed for effectively determining the current mechanical stiffness of bridge bearings.

The facade system is one of the most important components in a building. Beyond aesthetics, it provides overall protection against weather and regulates thermal performance. Failures in the facade systems can be costly. Current cladding materials have several limitations related to durability, maintenance, corrosion, high thermal conductivity, high embodied energy, flammability, breathability, expansion and contraction, water ingress, cracks, heavyweight, and so on. Any of these can lead to facade failure, considerable financial loss, and pose a safety risk to the occupant. Thus, in developing the technology and introducing new materials and facade systems, facade failures and related costs are critical and need considerations. Two potential issues within facade systems that could adversely affect performance are corrosion of steel components and fire performance of cladding. Approximately twenty percent of the world’s annual steel production is lost because of corrosion. In Australia, corrosion may have cost up to $32 billion per annum, which is more than $1500 for every person in Australia each year. Two million fires are reported in Europe annually, and 70,000 people are hospitalised in Europe each year due to severe injuries caused by fire. Forty-two percent of building fires start on the exterior wall surface and the rest are related to the items inside the facade system which also spread the fire. Based on a comprehensive literature review on corrosion of steel in combination with moisture transfer simulations using Warme Und Feuchte Instationar (WUFI) software, the risk of moisture penetration and the potential corrosion of steel in a rain screen facade system are found to be small. A detailed practical guidance to design and specify steel components against corrosion is presented. With regards to improving the cladding fire performance, this thesis focused on the development and fabrication of a new type of cladding material (3D glass fibre reinforced polymer (GFRP) nanocomposite) with improved thermal stability, fire performance, and tensile properties. 3D GFRP nanocomposite samples were fabricated with 5% and 10% of sepiolite (Sep) and sepiolite-phosphate (SepP), and 5%, 10%, and 15% of ammonium polyphosphate (APP) flame retardant. Synthesis of SepP, dispersion analysis of nanoparticles into the polymer, and fabrication process have been studied. The characterisation of materials was conducted using scanning electron microscopy (SEM), helium ion microscopy (HIM), transmission electron microscopy (TEM), thermogravimetric analysis (TGA), and X-ray diffraction analysis (XRD). The thermal stability, fire behaviour, and tensile properties of the 3D GFRP nanocomposite was studied via TGA, cone calorimeter tests, and tensile tests, respectively. TGA results showed that the optimum amount of additives that improved the thermal stability and decomposition temperature is 15% flame retardants. According to the cone test, increasing the APP flame retardant percentage (between 0-15%) remarkably improved the fire reaction properties of 3D GFRP nanocomposite regardless of the presence of Sep/SepP nanoparticles. The effects of APP flame retardant in improving the fire performance of 3D GFRP nanocomposite are remarkably higher than those of Sep fibres and SepP nanoparticles. Among Sep, SepP, and APP, APP flame retardant is better in improving thermal and fire reaction properties while Sep fibres are better in improving tensile properties of the 3D GFRP nanocomposite. Furthermore, the Sep samples showed higher ultimate strength (6%-30%) and strain (2%-39%) than SepP samples. Also, higher percentages of Sep/SepP nanoparticles (10%) showed better tensile properties than lower percentages (5%) of them. The cone calorimeter test results of the 3D GFRP nanocomposite indicated a prospective cladding that can benefit the construction industry. With more and properly instrumented full-scale facade system tests in the future and manufacturing optimisation, a more robust approach, e.g. using computational fluid dynamic modelling, need to be developed that allows the use of results from bench-tests such as those from the cone calorimeter tests to infer the fire performance of facades with alternative cladding materials in full-scale tests.