Zhe Luo

To maintain adequate depth of commercial waterways, large quantities of earthen material are dredged and stored on undeveloped placement areas adjacent to the waterway. As dredge placement areas become overwhelmed, an environmental and financial sustainable solution for the reuse of dredged soil is prioritized. In this study, locally dredged material from the Sabine-Neches Waterway was used to explore the potential of dredged material in the production of compressed stabilized earth bricks (CSEBs) for small-scale structures in the region. CSEB mixture designs were developed containing fly ash (FA), Portland cement (PC), hydrated lime (HL), water (W), dredged material (DM), and natural and synthetic fibers. Optimized mixtures designs reached the recommended compressive strength of over 1200 psi. Results showed that that the addition of fibers reduced the compressive and flexural strength of the bricks, with a maximum compressive strength of 1394 psi with a corresponding flexural strength of 381 psi being obtained with fiberless dredge bricks. Multiple coating systems were also tested to increase the resistance of the bricks to weathering and erosion. Results showed that the use of coatings reduced water absorption and increased the bricks resistance to erosion, making them more adept in regions commonly subjected to flooding and heavy wind-driven rains.

Sponsored by the Committee on Technical Advancement

This paper presents the risk assessment of a footing-on-slope system that uses the random finite element method. The effect of a soil autocorrelation structure on the probability of failure and the associated risk is quantified. In this study, the anisotropic spatial variability of the soil is described with a major principal scale of fluctuation, a minor scale of fluctuation, and a rotation angle, and the spatial variability is modeled using the rotated random field. The generated random field is mapped onto a finite element model, which can quantify the bearing capacity of a footing on a slope. Further, the K-means cluster method is adopted to calculate the sliding area of the soil mass. Following Monte Carlo simulation, the probability of failure and the corresponding risk for footings on slopes are evaluated for various soil spatial variability scenarios. The results show the importance of considering the anisotropy of soils when attempting to identify the worst-case scenarios for risk.

This paper presents a study of the system reliability of mechanically stabilized earth (MSE) walls by considering all four external failure modes: excessive eccentricity, lateral sliding, insufficient bearing capacity, and deep-seated instability. The focus of this study is (1) to quantify the correlation between any two failure modes, (2) to statistically examine the dependency between any two failure modes, (3) to estimate the system probability of failure using both Monte Carlo simulation (MCS) and formula of union, and (4) to validate the probability bounds of system reliability. Based on MCS, the probabilities of the intersections and unions of various combinations of failure modes are evaluated. The results from this study point to the importance of incorporating all failure modes in a series system of MSE walls and reveal the statistical correlation and independence among different failure modes.

This article presents a Bayesian approach for simultaneously calibrating models and soil parameters using field data on wall deflections and ground surface settlement from braced excavations. This approach is developed based on the Metropolis–Hastings algorithm, the Gibbs algorithm, and hybrid Markov chain Monte Carlo simulation. The Formosa excavation case history is adopted to demonstrate this approach. The predictions are made by using both calibrated models and soil parameters, and the predicted excavation-induced responses from the models are shown to have greater accuracy and less variability than the responses obtained when updating the soil parameters only. This Bayesian approach is effective given the availability of reasonably estimated prior distributions and field observation data with good quality.