Chung Yuan Christian University

Tunnel engineering plays a critical role in modern infrastructure development. During its service life, tunnels are inevitably subjected to dynamic loads, such as earthquakes, Tunnel Boring Machine (TBM) excavation, and rockburst events. These forces can lead to mechanical property degradation of jointed rock mass, thereby affecting the safety of tunnel engineering. Grouting technology, a crucial method for enhancing the mechanical properties of jointed rock mass, has been widely utilized in tunnel engineering. Therefore, investigating the impact mechanisms of filled jointed rocks is of significant importance. This study examines the impact behavior of filled jointed rock specimens using a split Hopkinson pressure bar apparatus. The results indicate that the dynamic mechanical properties of filled jointed rocks exhibit significant strain-rate dependence. With an increase in the joint surface roughness coefficient of composite-filled specimens, higher dynamic compressive strength and lower failure rates are observed compared to specimens filled with portland cement materials. Specimens with higher joint surface roughness coefficients showed lower damage factors and greater stability under dynamic loading. The failure process and mechanical properties of filled jointed rock under dynamic loading were shown and analyzed specifically based on the numerical model built in this paper. At high strain rates, the relationship between the dynamic increase factor and strain rate for filled jointed rocks follows the Grady equation, which can be used to predict and analyze their dynamic compressive strength. This study provides valuable insights into the impact mechanisms of grouted jointed rocks.

Artificial intelligence (AI) has reshaped various aspects of human lives, particularly through its capabilities to address complex sustainability challenges. Despite the rapid expansion of AI applications, their contribution to cleaner production and sustainable development remains underexplored, especially in developing nations. In Southeast Asia (SEA), where AI adoption has grown substantially across environmental, economic, and social dimensions, research that examines its role in cleaner production outcomes remains fragmented. In view of this gap, this study conducts a systematic literature review (SLR) of AI applications related to cleaner production and sustainable development by examining relevant themes, application areas, and sustainability dimensions addressed by AI, while evaluating the maturity of AI methodologies, alignment with cleaner production outcomes, and integration with circular economy and resource efficiency goals. Moreover, it investigates the barriers and challenges that constrain AI application and offers future research directions to advance AI deployment for cleaner production and sustainable development across SEA countries.

We enhance the operational performance of a textile factory by optimizing its production layout. The optimized layout improves operational time and increases overall productivity through strategic layout planning and workflow adjustments. The systematic layout planning method, combined with the analytical hierarchy process and technique for order of preference by similarity to an ideal solution from the multi-criteria decision-making process, is used to develop and select the optimal layout solution. The results show that the implementation of the improved layout design in alternative 1 led to a 45% reduction in average travel time and a 34% reduction in travel distance. This proposed solution optimizes production space while improving flexibility, safety, and competitiveness.

Summary: On April 3, 2024, an earthquake measuring 7.2 on the Richter scale occurred off the coast of Hualien County, Taiwan. This earthquake was the largest earthquake in Taiwan after the September 21 earthquake in 1999. The Taroko National Park, located in the northern part of Hualien City, had serious rockfalls and collapses; many tourists and vehicles were trapped on the trails and tunnels waiting to be rescued. The non-governmental organization, the Taiwan Development Association for Disaster Medical Teams, the disaster preparedness and rescue group, signed a cooperation agreement with the Special Search and Rescue (SSAR) Team and immediately formed an advance team to carry out the rescue mission. The SSAR team of the National Fire Agency and from neighboring counties and cities were responsible for coordinating rescue missions at the disaster site. Three of these teams have doctors, nurses, and emergency paramedics. Formation of an SSAR medical team to accompany the disaster relief mission. It was anticipated that more people or injuries would be extricated from the Taroko National Park on the second day of the disaster, and initial assessment, medical assistance, and medical evacuation assistance. The disaster medical advance team was dispatched by the on-site command center to perform medical operations at the scene of Taroko National Park with the SSAR medical team, sharing human resources and medical supplies, and at the same time being responsible for the evacuation of patients from the medical station. 181 people were monitored by the medical station and treated by the doctors for the evacuation. This was the first time that the disaster medical team and the SSAR medical teams cooperated in the rescue of a large-scale earthquake disaster in Taiwan in the past 25 years.