Browsing by Author "Adarkwa, Offei Amanor"
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Item Pavement Crack Classification Based on Tensor Factorization(University of Delaware, 2012) Adarkwa, Offei AmanorCracks in pavements allow water to pervade the various layers reducing drastically its ability to perform the primary function of carrying traffic loadings. The various types of cracks that occur on road pavements raise concerns for engineers and infrastructure managers. Identifying the type of crack accurately and efficiently is essential in road maintenance as this will lead to the prescription of cost-effective maintenance and treatment procedures. In the past, various image processing techniques have been applied in the detection and classification of pavement cracks most of which employ machine learning methods. This thesis outlines the importance of tensor analysis and decomposition as an alternative means of pavement crack classification. Tensors are multidimensional arrays and are generalizations of scalars, vectors and matrices. Two main types of cracks; longitudinal and transverse cracking are considered in the study. Due to the nature of tensors, the training set of images used is analyzed in a 3-dimensional space which captures variation across all images and ensures a more robust tensor algorithm for accurate crack classification. The levels of accuracy obtained after using the algorithm implies that crack classification based on tensor decomposition is one that can be successfully employed by state agencies nationwide who use digital image processing systems as part of their pavement management programs.Item Tensor factorization in civil infrastructure systems(University of Delaware, 2015) Adarkwa, Offei AmanorIn today's world, advancements in data collection equipment and storage capabilities of computers have made it possible for researchers and asset managers to collect huge amounts of data generated from civil infrastructure systems. The conventional approach for analyzing structured data of this nature is to impose a matrix- structure on the data before analysis is done. Analysis tasks may include classification, clustering, regression and exploratory analysis. These are generally done with the aim of gaining in-depth knowledge about civil infrastructure systems that will be the basis of planning and future decision-making. However, data generated from civil infrastructure such as pavements and bridges may not necessarily have a matrix-structure. They may have an inherent multidimensional structure. Using analysis techniques that preserve this natural multidimensionality can provide additional knowledge about infrastructure systems previously unknown. In this dissertation, multiway data analysis, also known as tensor factorization are utilized to address the multiway nature of data from the National Bridge Inventory (NBI). The NBI is the largest bridge database with detailed information on the condition and general attributes of bridges nationwide. Interestingly, individual bridges have over a hundred items of data each. Given the fact that this data has been collected over several years, the NBI data base can be considered as a typical multidimensional data set in civil engineering. The two main tensor factorization techniques namely Tucker decomposition and PARAFAC decomposition are used to reduce the multidimensional NBI data sets into smaller sets that are used as a basis for clustering and prediction. Four applications on the NBI database are discussed in this work. The first application involves a study of structural deficiency of bridge design types across states in the US. The second application also seeks to cluster states based on structural deficiencies and functional obsolescence of bridges under different classifications of traffic volumes. The third analysis uses a time series prediction approach with the tensor factorization to predict future structural deficiencies of bridges in states. The final analysis is an exploratory analysis which focuses on the condition of bridges in the state of Oregon. Promising results yielded by each of the analysis will encourage civil engineering researchers to conceptualize and analyze data in various application areas as being naturally multidimensional leading to more accurate knowledge discovery in systems.