The natural wetland areas in Vietnam, which are transition areas from inland and ocean, play a crucial role in minimizing coastal hazards; however, during the last two decades, about 64% of these areas have been converted from the natural wetland to the human-made wetland. It is anticipated that the conversion rate continues to increase due to economic development and urbanization. Therefore, monitoring and assessment of the wetland are essential for the coastal vulnerability assessment and geo-ecosystem management. The aim of this study is to propose and verify a new deep learning approach to interpret 9 of 19 coastal wetland types classified in the RAMSAR and MONRE systems for the Tien Yen estuary of Vietnam. Herein, a Resnet framework was integrated into the U-Net to optimize the performance of the proposed deep learning model. The Sentinel-2, ALOS-DEM, and NOAA-DEM satellite images were used as the input data, whereas the output is the predefined nine wetland types. As a result, two ResU-Net models using Adam and RMSprop optimizer functions show the accuracy higher than 85%, especially in forested intertidal wetlands, aquaculture ponds, and farm ponds. The better performance of these models was proved, compared to Random Forest and Support Vector Machine methods. After optimizing the ResU-Net models, they were also used to map the coastal wetland areas correctly in the northeastern part of Vietnam. The final model can potentially update new wetland types in the southern parts and islands in Vietnam towards wetland change monitoring in real time.

The structural systems such as road bridge beam systems (span superstructure) or beam systems used in high-rise buildings with precast pre-stressed reinforced concrete might be subjected to the load higher than the calculated load of the design process. In the process of operation the internal forces of the beams reach to a certain value, the section stiffness in elements will be changed. The change in section stiffness will lead to redistribution of internal forces in the whole system. However, that has not been included in the design process. The paper presents nonlinear deformation analysis of precast pre-stressed beam system used in road bridge span superstructure or high-rise buildings using nonlinear model of materials according to Russian construction codes to build the relationship between internal forces and section stiffness. From the calculation results, some comments will be recommended about increased load capacity in comparison with the results obtained from analysis using linear model of material.

No abstract