Data-driven Approach to Support Bridge Asset Management

Economic growth and reduction of poverty lies in a well-planned, constructed, and maintained infrastructure that includes water and sanitation networks, airports, schools, health facilities, and highways systems. As bridges are an integral component of the nation’s highway system infrastructure, deficient bridges without timely maintenance may endanger the public and affect the economy on a broader scale. Currently, more than the 9.0% of the bridges in the U.S. are graded as structurally deficient, and the new estimate to address these bridges is $123 billion. Thus, in order to keep the level of safety and serviceability of these infrastructure assets, efforts in an accurate prediction of condition ratings, a better characterization of deficient bridges, and a focus on prioritization of deficient bridges can help. Currently, bridge stakeholders face budget constraints; thus, they need a systematic approach to better estimate maintenance budgets, make informed decisions in bridge design, and prioritize bridge maintenance. This dissertation research has two major objectives. The first objective is to provide a framework to predict and characterize superstructure deficiency. The second objective is to present a methodology to prioritize bridge maintenance. This dissertation used NBI databases as the main data source and utilized data mining techniques, multi-criteria decision analysis, and GIS to achieve the objectives of the study. Moreover, this dissertation follows a three-journal paper format. The first paper addresses the development of a framework to create predictive models of superstructure ratings for steel and prestressed concrete bridges. The second paper identifies a framework to characterize superstructure deficiency of steel bridges. The third paper presents a decision-making framework to prioritize bridge maintenance through using aggregate bridge ratings and average daily traffic (ADT). This dissertation contributes to the overall body of knowledge by establishing frameworks to develop reliable models to predict superstructure ratings, identify factors that accelerate superstructure deficiency, and prioritize bridge maintenance. The results of this dissertation can be used by any bridge stakeholder to complement their current bridge management programs.Civil Engineerin

Application of high resolution remote sensing to detect and map the pasture weed Paterson’s curse (Echium plantagineum) in Western Australia

This study investigated the utility of three types of remotely sensed data (field spectroscopy, airborne multispectral and satellite hyperspectral) for detecting and mapping Paterson’s curse (Echium plantagineum) in the Wheatbelt Region of Western Australia. Using different classification, statistical and quantitative validation approaches, the study found that spectral resolution and timing of image capture were the most important factors for discriminating Paterson’s curse and producing acceptable levels of mapping accuracy

Understanding the spatiotemporal heterogeneity in grassland dynamics in Kenya’s semi-arid pastures

Grassland biomes are one of the largest terrestrial ecosystems on the planet, providing critical ecological, social, and economic benefits. However, they are subjected to natural and anthropogenic stresses such as precipitation, temperature variability, and widespread land degradation. Invasive plant species, for example, pose enormous challenges regarding biodiversity loss and degradation. Therefore, we need to keep up with and improve our knowledge of how they change, especially over space and time, to make good decisions about their productivity, management, and conservation. However, questions remain in (i) mapping and invasion science regarding a methodological framework for mapping invasive plant species (Opuntia stricta) using satellite remote sensing, (ii) understanding the spatiotemporal relationship between grassland greenness, communities, precipitation, temperature, and grazing factors, and (iii) our understanding of the spatial variation in grassland community types and their palatability probability. As a result, this study aimed to better understand the spatiotemporal dynamics in Kenya’s heterogeneous semi-arid grasslands by characterising the grassland into grassland communities and palatable and non-palatable plants. Additionally, it evaluates the intra-seasonal drivers of grassland changes at a site-specific level in 2019. The results show that combining Sentinel-2 spectral data, vegetation, and topographic indices is sufficient to map Opuntia stricta in a complex, heterogeneous semi-arid landscape. Additionally, precipitation, temperature and grazing, though at different times, are the major drivers of intra-seasonal grassland dynamics in semi-arid areas. Furthermore, the study found Sentinel-2 imagery to be adequate in achieving fine-scale spatial variations in grassland communities and inferring palatability probability in heterogeneous semi-arid grasslands. Finally, these findings and recommendations can help us better understand grassland dynamics and uncertainty modelling, as well as improve our understanding of plant-animal interactions, which can lead to management implications for rangeland management in terms of productivity, conservation, and rehabilitation