GEOTECHNICAL AND ENVIRONMENTAL IMPACTS OF STEEL SLAG IN EMBANKMENTS

Steel slag is a byproduct of iron and steel production by the metallurgical industries. Annually, 21 million tons of steel slag is produced in the United States. Most of the slag is landfilled, which represents a significant economic loss and a waste of valuable land space. Steel slag has great potential for the construction of highway embankments; however, its use has been limited due to its high swelling potential and alkalinity. The swelling potential of steel slags may lead to deterioration of the structural stability of highways, and high alkalinity poses an environmental challenge as it affects the leaching behavior of trace metals. This study seeks a methodology that promotes the use of steel slag in highway embankments by minimizing these two main disadvantages. Accelerated swelling tests were conducted to evaluate the swelling behavior of pure steel slag and water treatment residual (WTR) treated steel slag, where WTR is an alum-rich by-product of drinking water treatment plants. Sequential batch tests and column leach tests, as well as two different numerical analyses, UMDSurf and WiscLEACH, were carried out to check the environmental suitability of the methods. Tests were conducted to study the effect of a common borrow fill material that encapsulated the slag in the embankment and the effects of two subgrade soils on the chemical properties of slag leachate. The results indicated that an increase in WTR content in the steel slag-WTR mixtures yields a decrease in pH and most of the leached metal concentrations, except aluminum. The change in the levels of pH, after passing through encapsulation and subgrade, depends on the natural pHs of materials.

Soft computing based parameter identification in pavements and geomechanical systems

Accurate estimation of road pavement geometry and layer material properties through the use of proper nondestructive testing and sensor technologies is essential for evaluating pavement’s structural condition and determining options for maintenance and rehabilitation. For these purposes, pavement deflection basins produced by the nondestructive Falling Weight Deflectometer (FWD) test data are commonly used. The nondestructive FWD test drops weights on the pavement to simulate traffic loads and measures the created pavement deflection basins. Backcalculation of pavement geometry and layer properties using FWD deflections is a difficult inverse problem, and the solution with conventional mathematical methods is often challenging due to the ill-posed nature of the problem. In this dissertation, a hybrid algorithm was developed to seek robust and fast solutions to this inverse problem. The algorithm is based on soft computing techniques, mainly Artificial Neural Networks (ANNs) and Genetic Algorithms (GAs) as well as the use of numerical analysis techniques to properly simulate the geomechanical system. A widely used pavement layered analysis program ILLI-PAVE was employed in the analyses of flexible pavements of various pavement types; including full-depth asphalt and conventional flexible pavements, were built on either lime stabilized soils or untreated subgrade. Nonlinear properties of the subgrade soil and the base course aggregate as transportation geomaterials were also considered. A computer program, Soft Computing Based System Identifier or SOFTSYS, was developed. In SOFTSYS, ANNs were used as surrogate models to provide faster solutions of the nonlinear finite element program ILLI-PAVE. The deflections obtained from FWD tests in the field were matched with the predictions obtained from the numerical simulations to develop SOFTSYS models. The solution to the inverse problem for multi-layered pavements is computationally hard to achieve and is often not feasible due to field variability and quality of the collected data. The primary difficulty in the analysis arises from the substantial increase in the degree of non-uniqueness of the mapping from the pavement layer parameters to the FWD deflections. The insensitivity of some layer properties lowered SOFTSYS model performances. Still, SOFTSYS models were shown to work effectively with the synthetic data obtained from ILLI-PAVE finite element solutions. In general, SOFTSYS solutions very closely matched the ILLI-PAVE mechanistic pavement analysis results. For SOFTSYS validation, field collected FWD data were successfully used to predict pavement layer thicknesses and layer moduli of in-service flexible pavements. Some of the very promising SOFTSYS results indicated average absolute errors on the order of 2%, 7%, and 4% for the Hot Mix Asphalt (HMA) thickness estimation of full-depth asphalt pavements, full-depth pavements on lime stabilized soils and conventional flexible pavements, respectively. The field validations of SOFTSYS data also produced meaningful results. The thickness data obtained from Ground Penetrating Radar testing matched reasonably well with predictions from SOFTSYS models. The differences observed in the HMA and lime stabilized soil layer thicknesses observed were attributed to deflection data variability from FWD tests. The backcalculated asphalt concrete layer thickness results matched better in the case of full-depth asphalt flexible pavements built on lime stabilized soils compared to conventional flexible pavements. Overall, SOFTSYS was capable of producing reliable thickness estimates despite the variability of field constructed asphalt layer thicknesses.

Analyse de profils de chaussées: Effet du gel saisonnier et de la dégradation du pergélisol

Des techniques adaptées aux contextes routiers sont nécessaires pour maintenir et réhabiliter des chaussées construites sur pergélisol ou en contexte de gel saisonnier. Plusieurs problématiques peuvent engendrer une augmentation des coûts de réparation et entretien, une diminution de la durée de vie des chaussées et des problèmes reliés à la sécurité des usagers de la route. L’objectif du projet consiste donc à élaborer un outil d’aide à la décision, qui contribuerait à localiser les zones sensibles au gel saisonnier et à la dégradation du pergélisol, à discerner les causes de dégradation des chaussées dues au gel saisonnier et à sélectionner les meilleures stratégies d’atténuation et de réfection à moindre coût. Le projet de recherche est divisé en deux volets distincts. Le premier volet traite des problématiques de gel de chaussées en contexte de gel saisonnier. Actuellement, il existe des méthodes de diagnostic qui permettent de détecter les endroits où un problème de gélivité est susceptible d’être présent. Par contre, ces méthodes ne permettent pas de discerner si le problème de gel est en profondeur ou en surface de la chaussée; en d’autres mots si le problème est lié à un soulèvement différentiel du sol ou à un soulèvement de fissures. De plus, les méthodes utilisées ne sont pas adaptées aux chaussées en contexte municipal. Selon les problématiques connues de certains sites, il a été possible de développer un abaque permettant de différencier si la problématique de gel se situe en surface ou en profondeur dans une chaussée. Puis, une analyse d’imagerie 3D a été réalisée pour complémenter l’abaque créé. À l’aide de cette technologie, une nouvelle méthode sera mise au point pour détecter des problématiques de gel grâce aux profils transversaux. Le deuxième volet porte sur les chaussées construites sur pergélisol. Les méthodes actuelles de détection de la dégradation du pergélisol sous les chaussées manquent de précision et ont besoin d’être raffinées, surtout dans le contexte actuel de réchauffement climatique. Pour ce faire, trois sites d’essais ont été étudiés sur l’Alaska Highway au Yukon. En fonction de différentes analyses telles que des analyses de profils longitudinaux, de la densité spectrale et de longueurs d’onde, des tendances ont été décelées pour caractériser l’instabilité du pergélisol.