Nicholas E. Malasavage

Stabilized Dredged Material. I: Parametric Study

This study presents the results of a treatability study for dredged material (DM) stabilization using 20 combinations of pozzolanic agents (lime, cement kiln dust, high alkali and slag cements, and fly ash). The DM consisted of CH/OH soil excavated from the U.S. Army Corps of Engineers Craney Island confined disposal facility in Hampton Roads, Virginia, having an in situ moisture content of approximately 130% and void ratio of 3.35. Mix designs were prepared for each stabilized DM (SDM) blend using a 3-day mellowing period for the SDM blends to become compactable. Typical maximum dry unit weights were on the order of 11.9–12.9 kN/ m3 (76–82 lb/ ft3 ) , for total dry pozzolan doses to wet DM between 5 and 95%, the upper dosing limit being unconstrained for potential use of the SDM blends as fill. Unconfined compression strength (UCS) testing of the SDM blends using DM with an initial MC of 132.5% was completed in accordance with ASTM D1632 and ASTM D1633 for curing times of 7, 28, and 180 days. The 28-day ...

Stabilized Dredged Material. III: Mineralogical Perspective

The prior two papers in this series reported on the geoenvironmental and geomechanical properties of 20 stabilized dredged material (SDM) blends using dredged material (DM) from the U.S. Army Corps of Engineers Craney Island confined disposal facility. The pozzolans included lime, cement kiln dust (CKD), class F fly ash, and two cements (portland and slag cement). This paper reports on the mineralogical evolution of the SDM blends over a 6-month curing period using techniques new to mainstream geotechnical engineering: X-ray diffraction (XRD) with Rietveld quantification analysis which allows direct quantitative mineralogical comparisons between soil samples. Despite being classified as a high plasticity clay-organic clay (CH/OH soil), XRD showed that the DM contained no montmorillonite, illite or kaolinite, and was thus mineralogically unreactive. The quartz, feldspar, and mica contents were numerically tracked and were shown to remain stable 6 months after blending. The chlorite (in DM) content decrease...

Stabilized Dredged Material. II: Geomechanical Behavior

This study presents the results of a detailed geotechnical evaluation of six stabilized dredged material (SDM) blends incorporating various combinations of lime, cement kiln dust, high alkali and slag cements, and Class F fly ash. The dredged material classified as CH/OH soil with an in situ moisture content (MC) of approximately 130% and void ratio of 3.35. Mix designs and unconfined compression strength tests were completed for each SDM blend based on 3-day mellowing characteristics. Compacted dry densities were on the order of 7.8–11.2 kN/ m3 ( 49–71 lb/ ft3 ) , with MCs on the order of 34–73%. Peak effective friction angles ranged from 20–50° with cohesion intercepts on the order of 30–235 kPa ( 4–34 lb/ in.2 ) using a maximum stress obiliquity criterion. Postpeak effective friction angles (15% axial strain) were routinely in excess of 40° with low cohesion ( <40 kPa ; 6 lb/ in.2 ). One sample exhibited very strong soil-fabric effects (cohesion) having an effective friction angle of only approximately...

Predicting Time-to-Failure in Slopes from Precursory Displacements: A Centrifuge Experiment

Predicting the time-to-failure of slow-moving landslides based on precursory displacements is an appealing concept that could significantly improve our ability to evaluate and manage landslide risk. In this paper, we explore this concept by examining data from a well-documented failure of a submerged, saturated clay slope that occurred during a controlled geotechnical centrifuge experiment. We found that application of a simple linear inverse velocity relationship to the initial portion of the data set produced conservative but nevertheless inaccurate predictions of time-to-failure. Repeating this exercise with data collected closer to failure provided significantly improved the prediction, but at the cost of reduced warning time. A related non-linear model produced an excellent fit over the full time range of observed displacements, but required curve fitting parameters drawn from empirical observations, thus limiting its practical application for predicting the time-to-failure. Still, both models provided a very good basis for understanding and interpreting displacement-time monitoring data, and as landslides progress towards failure, we found that these models are capable of accurately predicting time-to-failure.

Spatial Analysis for Identifying Concentrations of Urban Damage

Disasters resulting from earthquakes, hurricanes, fires, floods, and terrorist attacks can result in significant and highly concentrated damage to buildings and infrastructure within urban regions. Following such events, it is common to dispatch investigation teams to catalog and inventory damage locations. In recent years, these data gathering efforts have been aided by developments in high resolution satellite remote sensing technologies (e.g. Matsuoka & Yamazaki, 2005) and by advances in ground-based field data collection (e.g. Deaton & Frost, 2002). Damage inventories are typically presented as maps showing the location and damage state of structures in part or all of an effected region. In some cases information on the post-event condition of major infrastructure systems such as transportation, power, communications, and water networks is also included. Depending on the means used to acquire data, damage inventories may be developed in days (satellitebased data acquisition) or weeks-to-months (ground-based damage surveys) after an event. Once available, these inventories can be used for a range of purposes including guiding emergency rescues (short-term use), identification of neighborhoods requiring post-disaster financial assistance (intermediate-term use), and support of zoning, planning or urban policy studies (long-term use). An important task when analyzing these inventories is to identify and quantify damage concentrations or clusters, as this information is useful for prioritizing post-disaster recovery activities. Additionally, an understanding of damage concentrations can provide insight to the multiscale processes that govern an urban regions performance during an extreme event. In some cases spatial patterns and clusters can be inferred from damage inventories using simple, qualitative visual assessment techniques. While this may be a satisfactory approach in situations where there is a marked contrast in building performance, its effectiveness is limited when damage contrasts are subtle, and spatial patterns are less obvious. In these instances, more advanced spatial analysis tools such as point pattern analysis can be of benefit. Point pattern analysis (PPA) techniques are a group of quantitative methods that describe the pattern of point (or event) locations and determine if point locations are concentrated (or clustered) within a defined region of study. An early and often-cited example of a semi7