In this paper, first by some laboratory odor tests, it became clear that bottom mud odor of fishing ports and drainage stations that contain a lot of organic rot components is mainly due to the hydrogen sulfide odor of the bottom mud itself and due to the ammonia odor by highly alkaline ground improvement material, and their concentrations were found to be strongly correlated with pH. The following became clear that the optimum pH range for the maximum deodorizing effect of improved mud by PS ash should be within the weak alkali range and the addition of ferrous sulfate was effective in deodorizing. In addition, the results of the confirmed odor test on site improved the odor control function effect by reduction of PS ash adding the curing period and finally the utilization results were obtained.
It is important to measure and predict seepage flow behavior in river levees in order to estimate the state of seepage failure in river levees. In this paper, a quasi-real-time prediction method of the water level of the foundation of the levee is proposed using a learned artificial neural network model based on the water level changes of the river and the foundation of the levee in the event of a flood. For this purpose, the changes in the measured water levels of the river and the foundation of the levee were trained by an artificial neural network model using deep learning method during past flood events. The usefulness and validity of the proposed water level prediction method were verified by using actual water levels measured at two first-class river levees at four flood events.
This study aimed to estimate seepage analysis models combining measurement data of soil column test and data assimilation by the merging particle filter, and to discuss predicted performance of the estimated models. Concerning two types of materials with different particle sizes, the estimated models learning the measurement data of 30 days could predict soil moisture conditions after a few months later with enough accuracy. As a consequence, the data assimilation of seepage analysis model using merging particle filter was available to predict future soil moisture conditions.
It is well known that the strength of sand improved by chemical grouting is influenced by many factors including the type of base resin, silica concentration, soil grain and density. Although the strength is several to several tens of times larger than that of the silica hydrogel, the mechanism for increasing the strength has not been clarified. In this paper, a new proposal was made on the strength mechanism in which the volumetric shrinkage of silica hydrogel in sand offers confining pressure to the sand skeletons. In order to prove the validity of the mechanism, a series of unconfined compression strength tests, splitting tensile strength tests and elastic wave tests were carried out. Furthermore, the strength development of the improved soil was estimated by equations proposed on the basis of the mechanism.
The Modified GIN method which arranged the mix proportion of the grouting material, the injected pressure and the amount of the injected grouting material and considered as the safety and the quality assurance has been developed and employed at a construction site. In order to evaluate the grouting performance through Modified GIN method, in this research, CT images of borehole core samples are taken through both medical X ray CT and μ-focus X ray CT and are analyzed. Based on the CT images, it is confirmed that the grouting material are filled into the aperture of fracture. In the wide aperture, it is also confirmed that the filling grouting material consists of some layers. In the actual construction, the low density grouting material was injected in the first step, and then the high density grouting material was injected if the proposed injected pressure was not arrived in the first step. It is thought, therefore, that the CT images show the good agreement with the actual construction process.
This study aims to build a strength prediction model for dredged soils improved by basic oxygen furnace slag. The mechanism for improving the strength of dredged soils is considered that amorphous silica in dredged soils and portlandite in basic oxygen furnace slag dissolve, and H4SiO4(aq) and Ca++ form calcium silicate hydrate (C-S-H). Based on this hypothesis, equations to calculate reaction mass of C-S-H are proposed. The relationship between calculated reaction mass of C-S-H and uniaxial compressive strength of improved soils cured for 91 days shows a good correlation.
River levees suffer occasionally from damage during flooding in the form of backward erosion piping within or below the levees. It is invisible to current methods of inspection until it manifests itself at the exterior surface. Therefore, there is strong needs to develop techniques that can identify internal damage.
This report describes detailed investigation on the left bank levee of Hiji River where significant sand boiling and subsidence of the levee slope were observed during a flooding event in July 2018, with a focus on the mechanism of sand boiling and progress of piping. Dynamic cone penetration tests were conducted at small intervals, both in vertically and horizontally, and a large pit was excavated to expose soil sections to identify the fundamental mechanism of the damage to the levee. A digital elevation map with a good resolution and accuracy confirms the area where backward erosion piping affected.