Tidal flats and their associated sandbanks are dynamic environments crucial for ecological balance and biodiversity. Monitoring their evolutionary history and topographic changes is important to better understand their dynamic mechanisms and predict their future status. Accurately mapping their evolution, however, remains challenging due to highly dynamic currents, suspended sediment variability, and unclear boundaries between land, tidal flats, and water. Traditional waterline methods struggle under these conditions. In this study, we propose an Object-Based Image Segmentation (OBIS) method, specifically designed for SAR images, to extract waterlines and distinguish tidal flats and shorelines from water bodies. This method integrates SAR polarimetric feature analysis to select high-quality images and employs partition processing to preserve local feature statistics. Using 199 Sentinel-1 GRD, 132 Radarsat-2 SLC, and 157 Landsat images, we analyzed coastal dynamics in the Dutch Wadden Sea from 1986 to 2020. Our DEMs, validated against LiDAR data (2016–2019) and 58 ground anchor measuring stations (2011–2020), achieved an accuracy of 10–30 cm. Results show that coastal tidal flats and sandbanks expanded at rates of 0.107–0.324 km2 yr−1 and 0.010–0.073 km2 yr−1, respectively, with a net intertidal volume increase of approximately 8.6×107m3. The generated DEMs provide valuable insights for sediment budget evaluation and hydrodynamic modeling, supporting scientific research and coastal management. The proposed OBIS-based framework demonstrates its effectiveness for mapping national-scale tidal flats and sandbanks dynamics.

Domino effects are high-impact low-probability events that can have catastrophic consequences. To prevent and to reduce risks related to such events, safety barriers (SBs) are crucial. However, the initiation, propagation, and stopping processes of domino effects are characterized with complexity and uncertainties and hence they are unpredictable. This makes it challenging to allocate SBs based on predicted probabilities. In this study, a multi-objective optimization model which integrates graph theory with Non-dominated Sorting Genetic Algorithm II (NSGA-II) was proposed to allocate add-on SBs effectively. Graph metrics were used to quantify the escalation risks related to storage tanks and to optimize the allocation of add-on SBs, thereby minimizing the consequences of a domino effect under a budget constraint. The results of the case study demonstrate great efficiency in finding globally optimal strategies with a largest reduction of 94.3% in the out-closeness score due to the implementation of add-on SBs, allowing decision-makers to choose the most preferable investment strategy in face of domino effect risk. Our study therefore provides a novel approach to achieve an optimal allocation of add-on SBs globally and can be useful in preventing domino effects in large-scale chemical clusters equipped with a large number of storage tanks.

Microwave heating is an effective method to achieve autonomic crack healing in asphalt mixtures, and the use of microwave-absorbing materials can largely improve this healing efficiency. As a solid waste, coal gangue contains metal oxides, which shows the possibility of microwave heating. In order to further promote the application of coal gangue in the microwave healing of asphalt mixtures, this study looks into the synergistic effect of basalt and coal gangue powder (CGP) on the microwave heating self-healing of an asphalt mixture. The mechanical performance, water stability, low-temperature crack resistance and microwave healing efficiency of the asphalt mixture were investigated using the immersion Marshall test, standard Marshall test, Cantabro test and semi-circular bending (SCB), and healing tests, respectively. The results indicated that the addition of CGP in asphalt mixture can improve the microwave heating speed, which also showed a significant advantage in water stability and fracture energy recovery. The research results will further promote the utilization rate of coal gangue.