Proceedings of the 8th International Conference on Civil, Structural and Transportation Engineering (ICCSTE 2023)
Due to limited budgets, bridge managers need to be aware of the different factors affecting the m... more Due to limited budgets, bridge managers need to be aware of the different factors affecting the maintenance of their bridge stock. Since traffic levels are intensifying along with the likelihood of extreme events (as a result of climate change), the safety and reliability of road networks are at risk. This places immediate emphasis on the need for strategic investment policies to maintain and improve the network. Organisations rely heavily on the data collected at the time of inspection in order to prioritise maintenance tasks, however a budget that can address all substandard bridges is no longer viable due to restricted investment and effects of the coronavirus pandemic. Therefore, a method or tool for making informed choices is needed to show the effects of particular decisions. This paper will review current literature on how maintenance is prioritised both within the research community and in practice. A focus will then be placed on a toolkit designed to assist with the management of structures, with a look at how different budgets affects both the short-term and long-term condition of the bridges and how inspector bias affects the prioritisation results.
Pattern Recognition Approach for Road Collision Hotspots Analysis: Case Study of Northern Ireland
In order to address road safety effectively, it is essential to understand all the factors, which... more In order to address road safety effectively, it is essential to understand all the factors, which attribute to the occurrence of a road collision. This is achieved through road safety assessment measures, which are primarily based on historical crash data. Recent advances in uncertain reasoning technology have led to the development of robust machine learning techniques, which are suitable for investigating road traffic collision data. These techniques include supervised learning (e.g. SVM) and unsupervised learning (e.g. Cluster Analysis). This study extends upon previous research work, carried out in Coll et al. [3], which proposed a non-linear aggregation framework for identifying temporal and spatial hotspots. The results from Coll et al. [3] identified Lisburn area as the hotspot, in terms of road safety, in Northern Ireland. This study aims to use Cluster Analysis, to investigate and highlight any hidden patterns associated with collisions that occurred in Lisburn area, which ...
Probabilistic graphical models are an attractive approach used for modelling complex systems, as ... more Probabilistic graphical models are an attractive approach used for modelling complex systems, as the nature of the network allows uncertainty in the system to be accounted for. In particular, Bayesian networks, and their temporal extension, Dynamic Bayesian networks, are investigated. Difficulties often arise in the learning procedure, as the computational complexity of the network increases exponentially with each new variable. This can lead to a restriction on the number of variables to be included in the network and results in a network that is not truly representative of the system. This research wishes to demonstrate how these methods can benefit from using advanced computer technologies for implementing various learning algorithms, resulting in faster computation and the ability to handle larger data sets with an increased number of variables.
Proceedings of the 8th International Conference on Civil, Structural and Transportation Engineering (ICCSTE 2023)
Due to limited budgets, bridge managers need to be aware of the different factors affecting the m... more Due to limited budgets, bridge managers need to be aware of the different factors affecting the maintenance of their bridge stock. Since traffic levels are intensifying along with the likelihood of extreme events (as a result of climate change), the safety and reliability of road networks are at risk. This places immediate emphasis on the need for strategic investment policies to maintain and improve the network. Organisations rely heavily on the data collected at the time of inspection in order to prioritise maintenance tasks, however a budget that can address all substandard bridges is no longer viable due to restricted investment and effects of the coronavirus pandemic. Therefore, a method or tool for making informed choices is needed to show the effects of particular decisions. This paper will review current literature on how maintenance is prioritised both within the research community and in practice. A focus will then be placed on a toolkit designed to assist with the management of structures, with a look at how different budgets affects both the short-term and long-term condition of the bridges and how inspector bias affects the prioritisation results.
Pattern Recognition Approach for Road Collision Hotspots Analysis: Case Study of Northern Ireland
In order to address road safety effectively, it is essential to understand all the factors, which... more In order to address road safety effectively, it is essential to understand all the factors, which attribute to the occurrence of a road collision. This is achieved through road safety assessment measures, which are primarily based on historical crash data. Recent advances in uncertain reasoning technology have led to the development of robust machine learning techniques, which are suitable for investigating road traffic collision data. These techniques include supervised learning (e.g. SVM) and unsupervised learning (e.g. Cluster Analysis). This study extends upon previous research work, carried out in Coll et al. [3], which proposed a non-linear aggregation framework for identifying temporal and spatial hotspots. The results from Coll et al. [3] identified Lisburn area as the hotspot, in terms of road safety, in Northern Ireland. This study aims to use Cluster Analysis, to investigate and highlight any hidden patterns associated with collisions that occurred in Lisburn area, which ...
Probabilistic graphical models are an attractive approach used for modelling complex systems, as ... more Probabilistic graphical models are an attractive approach used for modelling complex systems, as the nature of the network allows uncertainty in the system to be accounted for. In particular, Bayesian networks, and their temporal extension, Dynamic Bayesian networks, are investigated. Difficulties often arise in the learning procedure, as the computational complexity of the network increases exponentially with each new variable. This can lead to a restriction on the number of variables to be included in the network and results in a network that is not truly representative of the system. This research wishes to demonstrate how these methods can benefit from using advanced computer technologies for implementing various learning algorithms, resulting in faster computation and the ability to handle larger data sets with an increased number of variables.
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Papers by Adele Marshall