## 1 Introduction  
Fire-related disasters remain a critical threat to human life, property assets, and operational continuity--particularly in high-density, high-value facilities such as high-rise buildings, data centers, and underground infrastructures. Traditional sprinkler-based fire suppression systems are widely adopted to mitigate fire hazards and are proven to be effective in containing fire spread and reducing property damage during the initial stages of combustion [1, 2]. However, these systems exhibit inherent limitations when deployedin rapidly evolving fire scenarios or environments where collateral water damage is of critical concern. Sprinklers typically rely on thermal activation mechanisms, which delay the onset of suppression until significant heat buildup is detected. As a result, they may be too slow to react in scenarios requiring immediate intervention. Additionally, they consume large volumes of water--up to four times more than water mist systems--leading to excessive water pooling, increased risk to sensitive electronic assets, and extended recovery times [3; 4; 5]. Moreover, their suppression mechanism--primarily based on large water droplets--offers limited evaporation efficiency compared to fine mist systems, reducing their effectiveness in toxic gas suppression and localized flame knockdown [6; 7]. In terms of spatial adaptability, sprinkler systems offer broad area coverage but often fail to effectively suppress fires that originate in shielded, irregular, or vertically partitioned zones, especially in complex architectural layouts [8; 9]. These limitations underscore the need for next-generation fire response systems that can deliver early, precise, and integrated suppression while minimizing collateral damage and downtime. Recent advancements in artificial intelligence (AI), Internet of Things (IoT), and sensor fusion technologies have enabled the development of intelligent fire response platforms that overcome many of the limitations of conventional sprinkler systems. These AI-enhanced systems integrate rapid fire detection, intelligent decision-making, and automated suppression into a unified architecture that significantly improves fire response speed, operational precision, and overall safety. Key enabling technologies include AI-powered object detection models (e.g., YOLOv4), multi-modal sensor networks combining flame, smoke, infrared, and ultraviolet inputs, and edge computing frameworks that allow for real-time hazard classification and decision support [10; 11; 12]. Unlike traditional systems that operate reactively based on thermal thresholds, these platforms detect fire ignition at earlier stages and dynamically adapt suppression strategies to fire type, intensity, and spatial constraints using AI-driven inference and reinforcement learning algorithms [13; 14; 15]. In parallel, traditional structural fire assessments often rely on nominal temperature--time curves--such as ISO 834 [16] and ASTM E119 [17]--to simulate fire exposure and evaluate material behavior. [PERSON]. [18] applied analytical equations to study steel profile behavior under various standardized fire curves, providing insight into fire duration impacts and cross-section effects. Similarly, [PERSON]. [19] proposed a simplified thermal model for estimating temperatures in steel members exposed to fire, contributing to more efficient structural safety assessments. However, these approaches remain limited to static thermal scenarios and lack integration with real-time sensing or adaptive suppression capabilities, highlighting the gap that intelligent fire platforms aim to fill. Moreover, these AI-based platforms enable localized and automated fire suppression using clean-agent delivery systems and targeted water or mist sprays, thereby minimizing water damage and improving response accuracy [20; 21]. They are also scalable and cost-effective, leveraging modular AI architectures and commercial off-the-shelf (COTS) hardware for flexible deployment in buildings, vehicles, and even autonomous vessels [12; 22].