With the groom in construction industry with rapid urbanization, need for high-performance concrete is increasing exponentially because of its high mechanical and durability properties. CO2 emissions related to production of cement is proving to be a big threat for environment because of which sustainability is a big challenge for concrete industry. High-Performance concrete with minimization of cement is must. So, for address this particle packing approach with partial replacement of cement with SCMs is major objective of this work. Mix designs have been developed using Design of Experiment (DOE) for Modified Andreason & Andersen Particle packing model and results were compared to ACI control mixes. Five-factor two level central composite design DOE was with the maximum and minimum Silica Fume and Fly Ash replacements being 15 kg/m3 & 66 kg/m3 and 0 kg/m3 & 83 kg/m3 respectively. The comparison for strength and durability was done by five tests i.e., Slump, compressive strength, rapid chloride penetration, abrasion resistance and absorption. The concrete mixes were also analyzed for sustainability in terms of cement consumption and CO2 emissions per MPa of concrete. Testing for the 28-day and 56-day properties the mix with 73% Portland cement, 13% silica fume and 14% fly ash was most efficient one. For the mixes, the average reduction in CO2 emissions per MPa was about 30-40%. In addition to this the concrete developed had good workability with average slump of 189.56mm and very high durability (average RCPT & abrasion resistance value being 898.596 Columb & 0.285%). The conclusion drawn can be used to develop efficient and ecological high-performance concrete. This will contribute to significant reduction of CO2 emissions.

LANDSLIDE SUSCEPTIBILITY MAPPING USING TRIGRS FOR ARE-DI VILLAGE, CHIANG RAI PROVINCE, THAILAND

Changes in extreme rainfall tend to be magnified into unpredictable fluctuations in runoff, leading to flooding and drought in the Pasak River Basin of Thailand. Moreover, it also affects the operation of the existing infrastructure. Therefore, it is important to monitor changes in the extreme rainfall events and integrate them into planning and operations with the additional challenges posed by climate change. In this study, rainfall data at the ten observed stations across the basin was used to assess the extreme rainfall indices over the baseline period 1985–2014. The five new CMIP6 global climate model datasets and two Shared Socioeconomic Pathways of SSP2-4.5 and SSP5-8.5 were selected to project the future climate scenarios from 2023 to 2100. The extreme rainfall indices trends are analysed using the Mann-Kendall test and Sen's slope, while the IDW technique is adopted to visualise the spatial trends. The results show that most of the rainfall indices in low-altitude areas are higher than in high-altitude areas, except for the duration-based indices CWD and CDD. The observed extreme rainfall shows a larger variation than that predicted by climate models. The very high greenhouse gas emissions exhibited by the SSP5-8.5 scenario contribute to greater uncertainty in future extreme rainfall for plain areas than in high-altitude areas. The Pasak River Basin is expected to experience wet rather than dry climates in the future. The spatial trends from past and future periods highlight the significant increasing trends in the area where the Pasak Jolasid reservoir is located. The results of this study will benefit policymakers in a position to reduce future climate vulnerabilities and can be used for building local adaptation strategies in response to long-term climate change.

In the field of structural health monitoring, there are different approaches for damage identification. Most of the traditional approaches use the acceleration measurement of the building to identify the changes in its dynamic properties. Similarly, some use these dynamic properties to update the numerical model and then estimate the location and magnitude of damage. This is a different approach of estimating the responses in the building to detect the damage by using the acceleration measured from a few floors of the building. An experimental study is carried out to test and validate this novel approach.

This paper proposes a smart queue management system for delivering real-time service request updates to clients' smartphones in the form of E-Ticket by using the Bluetooth Low Energy in broadcasting advertising packet mode. The proposed system aims at reducing the dissatisfaction with services with medium to long waiting times. To this end, the system allows carriers of digital tickets to leave the waiting areas and return in time for their turn to receive service.

1. BACKGROUND

A complete and accurate distribution of artificial canal is fundamental for advancing studies of human-induced carbon emission in tropical peatlands. The most recent publicly available drainage canals map in Southeast Asian Peatlands is a collection from Stanford Digital Repository that generated from 5-meter Planet Basemaps satellite imagery using a convolutional neural network [1]. This dataset displayed intersection over union (IoU) score of 0.85, however, the determination of a true positive was loosened within 25-meter distance. Given extremely low load bearing capacity in peat soil, roads are also labeled as canals. This assumption and relaxing the true positive criteria could lead to the uncertainty in canal detection. We proposed to address that uncertainty by incorporating higher resolution of satellite images where canal and non-canal can be visually better distinguished. Thus, this study aims to deliver classification models for automatically extracting artificial canal map from high-resolution (HR) satellite images.

2. METHOD

Here we exploit a deep learning method to automatically segment surface water features from 1.5-meter resolution pan-sharpened SPOT-6/SPOT-7 orthomosaic. Two models are developed to distinguished between (1) water and non-water; and (2) canal and non-canal. True color SPOT images are provided by Indonesia Space Agency (LAPAN) that acquired between 2014 and 2017 as part of national distribution of nearly cloud-free HR satellite images.

Data labelling for supervised learning is created by visually interpreting the input images from SPOT-6/SPOT-7. Label images covers 660 km² in Indragiri Hilir Regency, Riau Province, Indonesia and split to 70%, 20%, and 10% for training, validation and testing respectively. Given the high resolution of input images, we were able to precisely identify surface water features. Canal networks is not necessarily connected due to sedimentation, poor maintenance, or canal blocking activity.

The neural network designs we use in this study follow the U-Net architecture [2] combined with Resnet-34 backbone, a fully convolutional encoder-decoder network with skip-connections. The input to the network is a patch x ∈ ℝ^512x512x3, and the output is a segmentation map Φ (x) ∈ [0.2] ^512x512x1. The model was compiled by using Adam optimizer, loss function using Jaccard binary cross entropy, and evaluate the performance by using IoU score.

3. RESULT

Both models are applied to test dataset to assess the reliability for unseen data.

4. CONCLUSIONS

Validation dataset yields IoU score of 0.75 and 0.68 for Model 1 and Model 2 respectively. However, when evaluated on the test dataset the IoU score decrease to 0.67 for Model 1 and 0.59 for Model 2. Employing higher resolution of satellite images without any conflicting assumption could lead to more objective and realistic results.

The 2011 Flood in Thailand triggered an acceleration of business continuity planning in industrial complexes. In these circumstances, the development of an objective indicator to measure the effectiveness of business continuity planning is necessary for future resilient business. There are studies conducted on understanding the heat emissions of industrial complexes [1], but there is limited scientific knowledge to understand the activities of industrial complexes from the perspective of business continuity. Therefore, this study set the research question of whether it is possible to objectively assess the status of activities in industrial complexes from indirect data and conducted a preliminary study of the effective feasibility of this approach was conducted.

The number of aging sewage pipes is on the increase, and Japan is facing three shortages in financial resources, human resources, and technology for maintenance and management. Under these circumstances, it is difficult to conduct equal surveys of the vast number of sewage pipes. Therefore, the development of more efficient and effective inspection methods is required. In this study, as an approach to the realization of efficient management, an urgency classification model for sewage pipes was constructed and evaluated by utilizing the inspection results of sewage pipes.

In the event of a large-scale earthquake disaster, it is necessary to respond to diverse medical needs and effectively utilize limited medical resources. Among these, if the medical response to chronic drug users is delayed, the condition may become more severe and lives may be at risk. It is considered possible to provide prompt and accurate pharmaceutical support at the time of a disaster by understanding the local demand for pharmaceuticals during normal times. In this study, targeting the city of Hakui in Ishikawa Prefecture, using the National Health Insurance database, which enables the identification of prescribed medicines for each individual, we calculated the number of users of chronically needed medicines for each town and city. As a result, it was possible to identify the pharmaceutical needs in Hakui at the time of a disaster on a per-town character basis.

In recent years, due to the declining birthrate, aging population, and declining population, vacant houses in Japan have been increasing everywhere. As a measure against the problem of vacant houses, utilization of vacant houses is recommended by the national and local governments in Japan. On the other hand, there is a risk that non-sustainable utilization without anticipated demand or balance will be carried out. Therefore, it is necessary to thoroughly examine demand and sustainability after utilization.

Based on this social background in Japan, in this study, we aim to examine appropriate site location of vacant houses considering regional characteristics.

We focused on Hatoyama Town, where the problem of vacant houses is occurring. Among them, we will be analyzed in Hatoyama New Town, where there are many vacant houses and elderly people.

The distance between the houses and the facility and the area of the facilities were considered to calculate the customer attraction rate Wing Huff Model .

The cases considered were as follows. “Case1” is considered that only existing facilities in Hatoyama Town. “Case2” is considered that in which a vacant house is utilized in Hatoyama New Town with a low customer attraction rate by result of “Case1”. Comparing the result of “Case1” and “Case2”, it can be suggested that a certain number of customers are desired by utilizing the vacant house that had a low customer attraction rate in the above result.

In addition, a questionnaire survey on the situation of vacant house measures and utilization projects were conducted to Japan local governments . While utilizing vacant houses has been increasing rapidly in Japan, there are a lot of problems such as site selection, lack of financial resources. We aim to improve the efficiency of vacant house utilization in Japan by conducting a questionnaire survey and improving Huff Model.