The applicability of short fiber reinforcement technology to embankment materials such as sandy and gravelly soils was investigated. Discrete and flexible linear vinylon fibers with a length of 30 mm and a diameter of 0.5 mm were used and selected based on their potential dispersibility. Optimum fiber contents could be determined from the results of mixing tests. Series of plane strain compression tests were performed, and results confirmed that the strength and deformation characteristics of the tested sandy and gravelly soils could be steadily improved due to vinylon fibers mixing. The underlying reinforcing mechanisms include the reduction of shear displacement of soil particles by the flexural rigidity of the fibers, resulting into an increase in soil particles interlocking.

 With the rise of fiscal efficiency and social finance, the combination of PFS (Pay for Success) and investment model SIB (Social Impact Bond), where payments fluctuate depending on performance indicators for solving social issues, is gaining momentum in the healthcare field. In 2021, in the area of urban development, Japan's first PFS/SIB urban hollowing-out measure was launched in Maebashi City. The purpose of this research is to examine its effectiveness through practice in Maebashi City and to identify issues when applying it to future projects and other cities. In conclusion, in PFS, we confirmed that the incentive system in which the trustee aims to achieve the performance indicators while devising the content of implementation is effective in urban development. In addition, it was confirmed that it is difficult for private urban development corporations with weak financial foundations to secure cash flow while waiting for performance payments, and that while securing funds through SIBs is effective, there are challenges in structuring them due to the new structure.

 One way to control excess demands for urban transport services is to match demand with the service capacity in advance through quantity regulations, such as introducing reservation systems. However, this system does not work for non-reservation users, who cannot make reservations owing to irregular activities (such as sudden purchases, hospital visits and business trip behaviour), while it is socially undesirable to exclude this demand by fully introducing a reservation system. This study proposes a new reservation system that contributes to the efficiency of transport systems where reservation and non-reservation users share capacity-constrained transport services. We first describe a mathematical model of a transport system with the proposed reservation scheme referred to as a bundled commuter scheme. We next propose an auction mechanism for implementing the proposed scheme by extending the Vickrey-Clarke-Groves mechanism. We then show that the proposed scheme with this mechanism satisfies the strategy-proof and the efficiency properties. Moreover, this scheme ensures that users who participate in the proposed reservation scheme do not lose compared to when they do not participate. Finally, numerical experiments are conducted to investigate the properties of the proposed scheme in detail with comparing to other control schemes.

 In rural areas, many public transportation services are secured through public subsidies. However, since 2019, there has been considerable criticism from various institutions and commentators that these subsidies merely cover deficits, thereby reducing incentives for operational improvements. This study focuses on Japan's regional public transportation subsidy system, identifying methods such as "deficit compensation subsidy" and "tendering of services (gross cost contracts, net cost contracts)" from academic theory and administrative practice. It highlights that recent criticisms of "deficit coverage" do not explicitly consider the tendering of services, leading to a failure to distinguish between deficit compensation subsidies and gross cost contracts and consequently underestimating the value of gross cost contracts. Based on this analysis, the study conducts a policy evaluation, demonstrating that after deregulation, the significance of deficit compensation subsidies has diminished, the national subsidy system's improvement has been delayed, and the transition to a tendering of services system is urgently needed.

 Tatsugaike and Sunayamaike pump stations, both located in Toyosato Town, Inukami County, Shiga Prefecture, are groundwater pumping stations that have been in operation since the end of Meiji period. The purpose of this study is to examine the technical value of these pumping stations as civil engineering heritage based on the description of historical documents. The result of this study is as follows; 1) The design process and the characteristics of the well structure of Tatsugaike pump station were clarified for the first time. 2) Tatsugaike and Sunayamaike pump stations are the first agricultural groundwater pump stations constructed by overcoming uncertainty of groundwater availability. 3) The facilities of the both pump stations, especially the well structure, were considered to be the original of “masonry wells (ponds)” that were later widely constructed in Shiga Prefecture. In conclusion, both pump stations were evaluated as having high technical value as civil engineering heritage.

 Jimbo Kotora investigated on the Chianan Irrigation in 1919, the year before the construction started. He was a famous geologist in Japan. At the request of the Taiwan Governor-General’ office, he carried out a survey of land where the project was planned. As a result of his investigation, he concluded that building the dam was safe. Furthermore, he may have provided useful advice for the business. According to Jimbo's investigation, the project made significant progress toward its realization, leading to the approval of the budget in Congress. In this paper, we examined the significance of Jimbo's investigation in detail.

 We propose an image recognition method that automatically measures the deterioration size from photographed images by a digital camera to improve the efficiency of inspections of communication tunnels used to accommodate communication cables. The proposed method detects equipment and deterioration areas from photographed images using a deep learning, and estimates pixel resolution (mm/pixel) from the detection results of metal rack areas and size information. The actual deterioration size is measured from the pixel resolution and the detection result of the deterioration area. We evaluated the detection results of the proposed method using 1, 700 images, and confirmed that the proposed method can detect with an F-measure of 0.92 in the metal rack areas and 0.84 in the exposed rebar area. We evaluated the measured values by the proposed method and the measured values by the measuring tape using 91 exposed rebars, and found that the correlation coefficient was high at 0.9773. The measured performance of the proposed method are sufficient to determine the scale of deterioration and can be applied to the inspection of communication tunnels.

 An efficient way to identify rockfall hazard areas on wide-area slopes along railway lines is to use the results of quantitative assessment of slope features based on numerical elevation models. In this study, the slope gradient and ground point ratio were calculated based on the spatial numerical information after automatic ground point filtering, and a schematic extraction method of the fall type rockfall source was proposed using these results. A new point cloud filtering process was then performed to reproduce the fine ground surface including boulders on the extracted slopes, and an evaluation method was proposed to objectively compare the slope profile based on the difference in specific height between the point clouds after offset filtering. Furthermore, a method for identifying rockfall hazard areas based on a numerical elevation model, which takes into account the empirical methods practiced by railway field engineers, is presented by means of a case study.

 This paper presents an accurate and efficient method for object classification of underwater structures by applying a point cloud deep learning method to underwater point cloud data obtained from a narrow multibeam echo-sounder. Specifically, the paper presents an automatic method for generating point cloud training data from a 3D CAD model of an underwater structure by utilizing the CG lighting technology. Additionally, we added a cut model, which is a partial model of the CAD model, in order to improve the accuracy of object classification. The validity and effectiveness of the method are verified by applying the training data generated by the proposed method to PointNet++, a point cloud deep learning framework, and evaluating the classification accuracy of actual underwater objects.

 The aging of construction workers and the decrease in the number of young people entering the workforce have raised concerns about the decline in technical skills in the construction sector. This study aims to reveal chronological changes in the skill level of local construction engineers in terms of age, period, and cohort effects by conducting a cohort analysis of time-series changes in their skill level. A questionnaire survey was conducted on engineers belonging to construction companies (engineers of prime contractors) that undertook earthwork projects in Ehime Prefecture, and Age-Period-Cohort (APC) analysis was used to decompose the time-series changes in their skill levels into the three effects of age, period, and cohort. The results showed that the skill level of the generation that entered the workforce 20-25 years ago (1997-2002), when social criticism of the construction industry increased, tended to be the lowest. The skill level of the younger generation who entered the construction industry within 10 years was also generally lower than that of the veteran generation. Finally, based on the above results, we discuss the actual situation of the decline in technical skills in the local construction sector and summarize the issues to be examined in the future.