Numerical study on the curling and warping of hardened rigid pavement slabs

In-service hardened concrete pavement suffers from environmental loadings caused by curling and warping of the slab. Traditionally, these loadings are computed on the basis of treating the slab as an elastic material, and of evaluating separately the curling and warping components. This dissertation simulates temperature distribution and moisture distribution through the slabs by use of a developed numerical model that couples the heat transfer and moisture transport. The computation of environmental loadings treats the slab as an elastic-viscous material, which considers the relaxation behavior and Pickett effect of the concrete. The heat transfer model considers the impacts of solar radiation, wind speed, air temperature, pavement slab albedo, etc. on the pavement temperature distribution. This dissertation assesses the difference between documented models that aim to predict pavement temperature, highlighting their pros and cons. The moisture transport model is unique for the documented models; it mimics the wetting and drying events occurring at the slab surface. These events are estimated by a proposed statistical algorithm, which is verified by field rainfall data. Analysis of the predicted results examines on the roles of the local air RH (relative humidity), wind speed, rainy pattern in the moisture distribution through the slab. The findings reveal that seasonal air RH plays a decisive role on the slab‘s moisture distribution; but wind speed and its daily variation, daily RH variation, and seasonal rainfall pattern plays only a secondary role. This dissertation sheds light on the computation of environmental loadings that in-service pavement slabs suffer from. Analysis of the computed stresses centers on the stress relaxation near the surface, stress evolution after the curing ends, and the impact of construction season on the stress‘s magnitude. An unexpected finding is that the total environmental loadings at the cyclically-stable state divert from the thermal stresses. At such a state, the total stress at the daytime is roughly equal to the thermal stress; whereas the total stress during the nighttime is far greater than the thermal stress. An explanation for this phenomenon is that during the night hours, the decline of the slab‘s near-surface temperature leads to a drop of the near-surface RH. This RH drop results in contraction therein and develops additional tensile stresses. The dissertation thus argues that estimating the environmental loadings by solely computing the thermally-induced stresses may reach delusive results. It recommends that the total environmental loadings of in-service slabs should be estimated by a sophisticated model coupling both moisture component and temperature component.

PROBABILISTIC MODELING OF RAINFALL INDUCED LANDSLIDE HAZARD ASSESSMENT IN SAN JUAN LA LAGUNA, SOLOLÁ, GUATEMALA

The municipality of San Juan La Laguna, Guatemala is home to approximately 5,200 people and located on the western side of the Lake Atitlán caldera. Steep slopes surround all but the eastern side of San Juan. The Lake Atitlán watershed is susceptible to many natural hazards, but most predictable are the landslides that can occur annually with each rainy season, especially during high-intensity events. Hurricane Stan hit Guatemala in October 2005; the resulting flooding and landslides devastated the Atitlán region. Locations of landslide and non-landslide points were obtained from field observations and orthophotos taken following Hurricane Stan. This study used data from multiple attributes, at every landslide and non-landslide point, and applied different multivariate analyses to optimize a model for landslides prediction during high-intensity precipitation events like Hurricane Stan. The attributes considered in this study are: geology, geomorphology, distance to faults and streams, land use, slope, aspect, curvature, plan curvature, profile curvature and topographic wetness index. The attributes were pre-evaluated for their ability to predict landslides using four different attribute evaluators, all available in the open source data mining software Weka: filtered subset, information gain, gain ratio and chi-squared. Three multivariate algorithms (decision tree J48, logistic regression and BayesNet) were optimized for landslide prediction using different attributes. The following statistical parameters were used to evaluate model accuracy: precision, recall, F measure and area under the receiver operating characteristic (ROC) curve. The algorithm BayesNet yielded the most accurate model and was used to build a probability map of landslide initiation points. The probability map developed in this study was also compared to the results of a bivariate landslide susceptibility analysis conducted for the watershed, encompassing Lake Atitlán and San Juan. Landslides from Tropical Storm Agatha 2010 were used to independently validate this study’s multivariate model and the bivariate model. The ultimate aim of this study is to share the methodology and results with municipal contacts from the author's time as a U.S. Peace Corps volunteer, to facilitate more effective future landslide hazard planning and mitigation.