小田 和広

微生物代謝による改良土の三軸試験方法を提案している。本法では、モールドを用いて供試体を作製することにより、炭酸カルシウム析出量を適切にコントロールできる。

ﾚｰﾀﾞｰ･ｱﾒﾀﾞｽ解析雨量と高速道路に設置された雨量計の比較を通じ、解析雨量の精度を考察した。その結果、降雨量度が強いときは、解析雨量は実際の雨量を過小評価することが分かった。

ゲリラ豪雨のような集中豪雨によって引き起こされる斜面崩壊に対し、数値シミュレーションによる再現性を検証すると共に、降雨特性（降雨強度、降雨パターン等）と斜面崩壊の特性との関連について明らかにした。

土中への雨水浸透における擬似飽和時の体積含水率に関するカラム実験と斜面模型実験を通じ、斜面変状の発生と体積含水率の関係について明らかにした。

斜面変形監視を目的とした基礎研究としてオリエンテーション・コード・マッチングによる変位検出手法の開発とその精度に関して表した。

土壌水分特性ﾊﾟﾗﾒｰﾀの決定に関してﾃﾞｰﾀ同化手法の一つである粒子ﾌｨﾙﾀの有用性について、ﾃﾞｰﾀ更新の見地から考察した。

土壌水分特性ﾊﾟﾗﾒｰﾀの決定に関してﾃﾞｰﾀ同化手法、特に粒子ﾌｨﾙﾀの有用性を明らかにした。

ゲリラ豪雨時に迅速に道路点検を行うためのハザードマップの作成方法について調査し、雨量の想定、現象の予測とその評価方法について提案した。

ニューラルネットワークによって推定した地盤定数を用いた圧密沈下量に関する確率論的考察。

ﾚｰﾀﾞｰ･ｱﾒﾀﾞｽ解析雨量と高速道路に設置された雨量計の比較を通じ、解析雨量の精度を考察した。特に、解析雨量の補正方法について提案した。

土壌水分特性ﾊﾟﾗﾒｰﾀの決定に関して、目的関数を最適化するﾚｰｾﾞﾝﾊﾞｰｸﾞ･ﾏｰｶｰﾄﾞ法を適用した。そして、そうやって得られた土壌水分ﾊﾟﾗﾒｰﾀを土質特性と関連付け、空間的な分布特性を明らかにする方法を提案した。

サイフォンによる地下水位低下工法の開発を目的とした模型実験におけるサイフォンの内径と排水量ならびに地下水低下量の関係を実験的に明らかにした。

二次圧密係数のひずみ速度依存性を考慮できる弾粘塑性モデルによる大阪湾洪積粘土の長期圧密試験への適用性を検証した。

ﾚｰﾀﾞｰ･ｱﾒﾀﾞｽ解析雨量と高速道路に設置された雨量計の比較を通じ、解析雨量の精度を考察した。特に、解析雨量の補正方法について提案した。

微生物代謝によよって改良された豊浦砂の等方的な圧縮挙動と膨潤挙動を実験的に明らかにした。

人工知能を利用した潜在的な危険性を有する道路斜面の抽出。

2014年8月における豪雨時の福知山での土砂災害の斜面の分類について。

浚渫土から抽出したリサイクル砂試料によるサンドマット内の間隙水圧の評価式の提案。

This paper reports on the development of an advanced slope disaster monitoring system for practical use. In 2010, we developed a distributed wireless sensor network and installed it on a slope along an expressway to detect and monitor water behavior and deformations in the slope due to heavy rainfalls at an early stage. In this previous research, three major issues remained unresolved: poor wireless communication data collection rate, sensing performance, and monitoring system. To resolve these issues, in 2013, improved monitoring systems with wireless mesh network functions were set up on slopes along six expressways managed by the West Nippon Expressway Company in Japan. Consequently, we confirmed that data were constantly being sent to the data server every ten minutes, even at single-hop wireless communication distances up to 400 m. Improvements in the sensor nodes by adding strain gauge sensors and contact input and output devices made them useful not only for monitoring slope failure but also for maintenance of the slope by the ground anchor method and evaluation of the drainage effect of the embankment. (However, improvements in the electric field strength and addition of a retry function resulted in a decline in battery life.) The improved monitoring system allows road managers to view the data at each site at any time. Further, the data can also be downloaded from the webserver on the Internet.

There is moisture movement under the ground as a factor contributing to salt damage at the arid regions. Some parts of wall paintings of the Dunhuang Mogao Grottoes, which is a World Heritage site, have been seriously damaged by the crystallization of salts. This damage is likely to have been caused by short-term factors such as rainwater and flooding, and a longterm factor, namely, groundwater movement. In order to preserve the grottoes, it is important to know the relationship between underground moisture and salts. The purpose of this study is to understand the moisture content and salinity in conglomerate layers behind the Mogao Grottoes by the geophysical explorations and laboratory experiment. The resistivity characteristics of the field sample focused on water saturation with salinity were determined by laboratory tests. By obtaining the distribution of water saturation from the electric resistivity method in the study site, the moisture content in the ground was estimated. To confirm the reliability of the estimation, RI-density log was implemented. These results indicate there is relationship between the hydric environment and salinity concentration in each depth behind the Mogao Grottos.

In this paper, the effect of errors in relationship between void ratio and consolidation pressure on estimation of consolidation settlement was discussed stochastically. Firstly, the availability of an artificial neural network to spatial interpolation of relationship between void ratio and consolidation pressure was elucidated. Secondly, the probability density was derived directly through the frequency of relative errors. Also, the mathematical models which express the distribution of probability density were discussed. Finally, Monte Carlo simulations were carried out in order to estimate the consolidation settlement statistically. It was available to apply the relationship between probability density and relative errors derived through normal distribution. Also, the probability of occurrence of consolidation settlement could be estimated.

Sediment disasters caused by deep-seated catastrophic landslides have become a serious problem in Japan. Thus, it is necessary to identify slopes with a higher susceptibility to these landslides in order to establish effective disaster prevention planning. In this study, a method based on an artificial neural network technique and mathematical statistics is proposed. Slopes with a higher susceptibility to deep-seated catastrophic landslides are then identified by applying the proposed method to 142 slopes in Nara Prefecture, Japan. Furthermore, the slopes are prioritized according to the sample scores of Hayashi's second method of quantification.

Recently, slope disasters caused by cloudbursts become increasing in Japan. It is necessary to comprehend infiltration of rainwater into the slope in order to elucidate the mechanism that a slope failure occur. Not all amount of rainfall infiltrate into the slope, and some surface runoff happen. In this study, amount of rainwater that infiltrate into the slope is predicted through numerical simulations using two types of heavy rainfall which have happened in the past. Furthermore, the effect of intensity of heavy rainfall on types of sediment disasters is elucidated and the property of rainwater flow in the slope is revealed.

The appropriate estimation of analytical parameters is required, in order to predict the deformation behavior of man-made islands as accurately as possible. It is, however, difficult to estimate those objectively, because of the limited number of soil investigations, errors in the soil tests and spatial variation of soil properties. An artificial neural network is applied to estimate Holocene clay properties objectively and automatically in this paper. The availability of an artificial neural network to estimating Holocene clay properties is confirmed through statistic discussion of analytical parameters and numerical simulations of the construction of the man-made island.

Accurate prediction of consolidation settlement has been one of the most important geotechnical issues in the land development with reclamation. The soil properties in reclamation ground should be estimated properly in order to predict the consolidation settlement accurately. However, they are varied spatially due to effect of their sedimentary condition. Then, the soil properties obtained through soil investigations have various errors which cannot be eliminated. Also, the number of soil investigations is not enough to estimate properly them in many cases. In this paper, the soil properties are estimated properly from the limited number of soil investigations with an artificial neural network. Also, the estimation error in soil properties and its effect on estimation of consolidation settlement is discussed stochastically. Firstly, an artificial neural network is applied to estimate spatially soil properties, including relationship between void ratio and consolidation pressure of the Holocene clay in Kobe Airport. Secondary, it is elucidated that the estimation accuracy of both void ratio and relationship between void ratio and consolidation pressure is higher than those of compression index and pre-consolidation pressure thought discussions about relative errors. Finally, the Monte Carlo simulations to consolidation settlement is carried out in order to estimate stochastically the consolidation settlement. The consolidation settlement applied practically might be estimated about 1.1 times of that estimated with an artificial neural network judging from the probability.

無線センサネットワークによる土砂災害警戒システムの改良について。

植生情報と深層崩壊の関連性について考察。

現地計測結果に基づき，室内模型実験から観測された擬似飽和の概念に基づく斜面のモデル化をした。

風化砂岩と風化泥岩の互層斜面における雨水浸透挙動の数値解析による再現。

擬似飽和時の体積含水率に着目し，小型模型実験の表層崩壊発生までの水分挙動を検討した．崩壊に至る変位の発生は擬似飽和時の体積含水率に達したときと一致することをあきらかにした。

傾斜センサーの開発。

インドネシア・Bumi Waluyaにおける鉄道駅舎付近の地すべりの実態と原因に関する考察。

ニューラルネットワークによって土性の空間分布を推定できることの堆積学の視点からの考察。

ニューラルネットワークによる神戸沖沖積粘土の土質特性の空間評価について。

深層崩壊した斜面に対する自己組織化マップを使った分類と危険度評価方法の提案。

降雨浸透の実大模型実験に対する再現解析。

比抵抗探査による斜面の含水量評価について。

先端根固め中掘り鋼管杭基礎の薄層支持の問題おける支持層の剛性が支持力に及ぼす影響の解明。

The determination of the spatial distribution of consolidation parameters, which are used to predict the consolidation settlement, was carried out through an artificial neural network. Monte Carlo simulations of consolidation settlement were carried out in order to discuss their accuracy. The ability of the artificial neural network was verified by estimating the errors of the consolidation parameters.

The sedimentation and self-weight consolidation of dredged soil is influenced by segregating sedimentation. Various problems occur depending on the sedimentation characteristics, which are difficult to predict. Accordingly, the authors calculated the volume and density ratios for a fine-grained soil layer in dredged soil after the completion of sedimentation and self-weight consolidation. Then, they calculated the resulting settlement. This method was found to be effective for evaluating the settlement caused by sedimentation and self-weight consolidation.

人工知能と数理統計学を利用した深層崩壊の危険性の高い斜面の抽出について。

豪雨時における高速道路のり面の水分状況の監視とそれに基づく規制解除に関する考察。

擬似飽和状態に対する現地計測と模型実験に基づく考察。

細粒分の流出による土の強度低下に関する実験的研究。

比抵抗の経年変化による斜面内水分挙動の解析。

降雨浸透に関する実大模型実験の浸透挙動の再現解析。

先端根固め鋼管杭の支持層の剛性が支持力に及ぼす影響について。

微生物代謝によって固化された砂の力学特性に及ぼす相対密度の影響について。

根固めコンクリートの強度が薄層支持杭の支持力に及ぼす影響について。