Supporting huge population, megacities are definitely the hot spots of production, consumption, and waste generation. Without careful investment and planning, megacities will be overwhelmed by burgeoning negative impacts on the environment, natural resources, and human health, as well as a host of social and economic issues. The unprecedented combination of economic and population growth since the Reform and Open Policy has led China into transition from a largely rural society to a predominantly urban one. Chinese cities, without question, have not escaped the danger of the series of problems during the rapid progress of urbanization. Therefore, monitoring the spatial-temporal patterns of urban sprawl and their impact on the environment is of critical importance for urban planning and sustainable development, especially in developing Chinese cities such as Shanghai. To date, few studies have focused on the urban trajectories of Shanghai over the past 30 years from a remote sensing perspective. Most of the studies were concentrated on the technical issues of image processing and classification. Moreover, research on spatial metrics has focused on analyzing remote sensing classification results rather than on the use of interpreting, assessing, and verifying urban simulation results. Furthermore, many researches merely focused on baseline projection and very few studies took into consideration urban growth scenarios so far. As yet there have been no reported scenario simulations of future Shanghai growth with several land-use categories within urban areas. The overall objective of this research is to investigate the integration of remote sensing, spatial metrics, and spatial-temporal models in the monitoring, analysis, and simulation of urban growth in Shanghai, China. The specific objectives are to: 1). monitor urban dynamics over time with multi-sensor remote sensing images; 2). quantify spatial-temporal properties of urban growth and representing the urban morphological structures by means of spatial metrics; and 3). simulate the geographic extent, patterns, and detailed catalogs of urban growth under different scenarios using Markov-Cellular Automata (Markov-CA) model to support decision making for a more sustainable Shanghai. Through this study, the combined approach using remotely sensed data with change detection techniques, spatial metrics, and a scenarios-based simulation model proved to be effective to understand, represent, and predict the spatial-temporal dynamics of urban growth. In detail, the segmented-based hierarchy classification and visual interpretation were effective methods to extract urban and industrial land with high-resolution remotely sensed images. Direct change detection using variables derived from tasseled cap transformation was efficient for monitoring impervious surface sprawl. Spatial metrics is a quick and executable way to assessing the impact of urban sprawl on landscape dynamic. Markov-CA model is a useful tool to simulate the scenarios of future urban developments and therefore provides the policy options for sustainable urban planning. The research results of urban trajectories and impervious surface sprawl showed that Shanghai experienced high-speed urban sprawl and the rate of urban expansion, however, was not homogeneous spatially and temporally. The general annual urban expansion speed was 34.8 km2 per year; nevertheless, it reached 80.2 km2 per year recent six years from 2001 to 2007, while it touched the bottom speed around 14.3 km2 per year during 1979-1989. The expanded area in the Puxi region was 5.23 times of its original area while that of Pudong region was 19.94 times of its original area during 1979-2007. The research results of landscape analysis demonstrated that greenbelt becomes fractured while infrastructural and commercial area is more and more aggregated in the central Shanghai area, and satellite images such as SPOT Pan, XS and Landsat TM with 10-30 meter resolution are sufficient for the landscape dynamic research in central Shanghai area. The results of scenarios-based simulation indicated that built-up areas in Shanghai will increase significantly in 2025 and Shanghai will experience less urban sprawl and retain a better environment in 2025 under service-oriented center (SOC) than under baseline (NS) or manufacturing-dominant center (MDC) scenario. If favorable policy for MDC scenario is adopted, however, there will be a lot of manufacturing industries gathering in Shanghai and more agricultural lands will be encroached. The present research focused on the analysis of physical and morphological aspects of urban growth. Urban land-use dynamics are, however, intrinsically linked with socio-economic, political, or demographic drivers. Trying to fill in the missing link between traditional urban geography and urban remote sensing & urban simulation and to improve understanding of the interactions between human and natural aspects in the urban socio-ecosystem is the major focus in the next phase of the Ph.D. research. Keywords: Urban growth, Spatial-temporal pattern, Remote sensing, Spatial metrics, Scenarios-based simulation, Shanghai / QC 20110224

Drilled shafts are deep foundation elements created by excavating cylindrical shafts into the ground and filling them with concrete. Given the types of structures they support, failure to meet their performance criteria can jeopardize public safety and cause severe financial losses. Consequently, quality control measures are warranted to ensure these foundations meet design specifications, particularly with respect to their structural integrity and geotechnical capacity. Due to their inaccessibility, non-destructive testing (NDT) techniques have received much attention for drilled shaft quality control. However, there are limitations in the NDT tools currently used for structural integrity testing. Moreover, there is no current NDT tool to evaluate conditions underlying drilled shaft excavations and aid in verifying geotechnical capacity. The main objective of this research is to examine the development of new NDT methodologies to address some of the limitations in the inspection of drilled shaft structural integrity and geotechnical conditions underlying their excavations. The use of stress waves in large laboratory models is first examined to evaluate the performance of ray-based techniques for detecting anomalies. The study then continues to investigate the improvements offered by using a full waveform inversion (FWI) approach to analyze the stress wave data. A hybrid, multi-scale FWI workflow is recommended to increase the chance of the convergence of the inversion algorithms. Additionally, the benefits of a multi-parameter FWI are discussed. Since FWI is computationally expensive, a sequential optimal experimental design (SOED) analysis is proposed to determine the optimal hardware configurations for each application. The resulting benefit-cost curves from this analysis allow for designing an NDT survey that matches the available resources for the project. / Civil Engineering

Master of Science / Department of Civil Engineering / Stacey Kulesza / In Kansas, mechanically stabilized earth (MSE) retaining walls are typically backfilled with coarse aggregate. Current backfill material testing procedures used by the Kansas Department of Transportation (KDOT) utilize on-site observations for construction quality assurance and the American Association of State Highway and Transportation Officials standard T 288-12 (“Standard Method of Test for Determining Minimum Laboratory Soil Resistivity”). AASHTO T 288-12 is designed to test a soil sample’s electrical resistivity (ER) that correlates to its corrosion potential. However, the test, based on material passing through a No. 10 sieve, is inappropriate for coarse aggregate typically used by KDOT as the aggregate will be retained on a No. 10 sieve and potentially leads to over-conservative designs. However, ER imaging provides a two-dimensional (2D) profile of bulk ER of backfill material, thereby yielding more information regarding backfill uniformity compared to traditional sampling. The objective of this study was to characterize bulk ER of in-place MSE wall backfill aggregate. In this study, MSE walls selected by KDOT were tested using ER imaging during construction to determine bulk ER of the backfill. Variations within backfill ER may be a result of varying aggregate material, inclusions of fines, thoroughness of compaction, and the presence of water. ER imaging was used on five walls: four MSE walls and one gravity retaining wall that contained no reinforcement. One MSE wall contained metal reinforcement, while the other four walls contained geosynthetic. The ER imaging field method produced a 2D profile that depicted ER uniformity for bulk analysis. A post processing algorithm was generated to remove the subjective nature of the ER imaging results. The program determines the bulk ER based upon the ER imaging results. These results indicate that the laboratory analysis of AASHTO T 288-12 under-estimates the bulk ER of in-situ backfill material. Identification of a material’s bulk ER will help characterize the ER of aggregates in a complementary KDOT project. Results of this study will be used to recommend an in-situ test method for aggregate used by KDOT.

The physical properties of the coarse fraction of the till (0.4 to 20 cm) and the surface boulders have been studied at two different sites in Sweden. The research work included: development of a new image analysis software for 3D size and shape measurements of particles; lithological analysis on multiple size fractions in till and magnetic susceptibility survey on coarse till clasts, surface boulders and local bedrock. The new 3D image analysis method provides an enormous amount of size and shape data for each particle in the coarse fraction (2 to 20 cm) in till. The method is suitable for field study, cost effective and the software is executable in Matlab. The field imaging method together with the image analysis software give non subjective results of size and shape of coarse particles and makes it feasible and easy to study representative sample size, which is one tonne for testing clasts of size up to 20 cm. The lithological analysis of the multiple size fraction of the till clasts has been investigated on six different size fractions of the till (0.4 to 20 cm); the result of the different samples from the two sites shows that this method can potentially be used as a stratigraphic tool in the areas where there is no unique indicator lithologies. The magnetic susceptibility has been made on the surface boulders, the 6-20 cm till fraction and on insitu bedrock outcrops near to the study sites. The method has good potential for determining stratigraphic relationships between different till units as well as for determining the provenance ofcoarse clasts and surface boulders. / QC 20110413

During the urbanization process, vehicles quantity increase with expansion in population. Under this situation, bus transportation system also suffers from bus delay. Bus delay could be caused by a series of factors, for instance, overload passengers, traffic jam, traffic accident and other unpredictable situations. Therefore, choosing crucial elements to efficiently evaluate bus delay is a complex problem in bus delay researches and operation management. The thesis propose an approach to evaluate and explain bus delay by two elements: traffic congestion and passengers’ waiting time. Those two elements would represent the action of external and internal factors on bus operation. This approach could be adaptive to explain the reasons for bus delays, thus to help the optimization of bus lines and give useful information for decision making of transportation company. To achieve the research aim, a GIS-T database was created by combining the GIS database and TIS database. Spatial data as well as attribute data are combined in the database to represent the crucial information for bus delay. Based on GIS-T the database, the impact of traffic congestion and passengers’ waiting time was calculated using the bus line simulation. By implementing the above steps, the main cause of bus delay was studied. A case study application of this method is narrated; focusing on optimize the bus system of Guiyang city, South China. Different methods are used to find out the problem of system and the reason for delay. Moreover, optimization suggestion is proposed according to result. Compared with other methods, the two-factor method has the advantage of locating the reason of delay for each station. The time performance is not superior to other methods. By comparing the situation of adjacent station, the proportion of traffic congestion and overload passenger in bus delay was determined. The two-factor method is applicable for other transit system in different cities which has similar structure as Guiyang. However, for cities with other structure, a feasibility should be made to select an appropriate model.

För att uppskatta influensområdet till följd av skyddsinfiltartion finns ett antal analytiska modeller att tillämpa. Dessa modeller tar hänsyn till parametrar så som hydraulisk konduktivitet och magasinkoefficient, men de följer också med en rad antaganden som i praktiken inte kan uppfyllas. En alternativ tillvägagång för att bestämma influensområdet är därför med hjälp av numeriska modeller, som i större grad kan göras platsspecifika. Numeriska modeller är till följd av detta mer tidskrävande och behöver mer indata. I denna studie undersöktes vilken metod som är bäst lämpad för att bestämma skyddsinfiltrationens influensområden för en fallstudie i Bromstens industriområde, belägen cirka 15 km nordväst om Stockholm centrum. Två numeriska modeller med varierande underlag av platsspecifika data utvecklades över områdets geologi och grundvattenmagasin för att kunna simulera grundvattennivåer med och utan infiltration. Utöver detta beräknades influensområdet med fyra analytiska modeller. Modellerna testades sedan utifrån olika scenarion, där såväl dataupplösning som den platsspecifika kännedomen över området stegvis ökades. Platsspecifika data tillkom till följd av geotekniska undersökningar och hydrogeologiska tester. Studien ämnar även att besvara vilken data som är av störst vikt för att bestämma influensområdet med de analytiska respektive numeriska modellerna samt vilka skillnader som uppstår mellan analytiskt beräknade influensområden och numeriskt simulerade influensområden. Resultaten visar att de numeriska modellerna i huvudsak är känsligast med avseende på den hydrauliska konduktiviteten, samt att den enklare numeriska modellen är känslig för magasinkoefficienten, något som indikerar att denna modell inte uppnår jämvikt i enlighet med vad som observerats i fält. Utöver detta stod det klart att vattenavgivningstalet inte hade någon nämnvärd inverkan på resultaten. Bland de analytiska modellerna råder den största känsligheten i magasinkoefficienten, följt av konduktiviteten. För Sichardts formel, som inte tar hänsyn till magasinkoefficienten var konduktiviteten den känsligaste parametern. Akvifärens mäktighet, vilken reviderades mellan scenario 2 och 3, hade ingen betydande inverkan på de analytiska modellerna. Vidare visade infiltrationstestet på stora skillnader i skyddsinfiltrationens influensområde med avseende på de olika modellerna och dataunderlaget. Den minsta avvikelsen mätt i residualer observerades för den komplexa numeriska modellen under scenario 4, vilket motsvarar det scenario då dataunderlaget var som störst. Trots att detta scenario tillsammans med modell anses vara det dyraste fallet, anses detta vara det bästa och samtidigt mest tillförlitligt metoden för att uppskatta skyddsinfiltrationens influensområde. / To evaluate the area of influence due to artificial infiltration several analytical models are available. Some of the parameters taken into account by these models are the hydraulic conductivity and storage coefficient, but with these models some assumptions, which in reality cannot be fulfilled, are made. An alternative approach to evaluate the area of influence is therefore with numerical models, which in a greater extent account for the site-specific conditions. Due to this, numerical models are more time consuming and require more input data. This project aims to investigate the most effective approaches to evaluate the area of influence due to artificial infiltration for a case study in Bromsten, located 15 kilometers northwest of Stockholm. Two numerical models, with different background data due to the extent of site knowledge, were developed to represent the site's geological settings and groundwater properties to simulate the groundwaterlevels with and without infiltration. Moreover the area of influence were calculated with four analytical models. All of the models were then applied on four different scenarios, in which the data resolution and the site knowledge increased. Site-specific data was added as a result of geological surveys and hydrogeological tests. The study also aims to answer which data is most important in order to determine the area of influence with analytical and numerical models and what differences there are between the analytical solutions compared with the numerical solutions. Among the methods investigated, constructing a more complex model with data from scenario 4, the scenario with the greatest data supply, resulted in the most reliable results and was therefore the best method and the method to choose for this case-study. Other results indicated that the numerical models first of all are sensitive to the conductivity and that the more simpel numerical model is sensitive to the storage coefficient as well. The last result shows that this model does not reach the steady state conditions as observed in field, which highlights the importance of goetechnical investigation for the numerical models. Moreover none of the numerical models were sensitive to the specific yield. Among the analytical models the storage coefficient was the most important followed by the conductivity. For one of the analytical models (Sichardts formula) the conductivity was the most sensitive parameter. The thickness of the aquifer had no significant impact on the analytical models.