# Applications of Artificial Intelligence in Fire Safety of Agricultural Structures
## 2 Fire Safety in Agricultural Structures/Farms
### Artificial-Intelligence-Based Networks for Fire Prevention/Monitoring/Simulation  
The development of artificial-intelligence-based human-based observation systems for fire prevention/monitoring is supported by the complex nature of fires from a natural perspective. Fire is a destructive force, but whose impact on building materials is not fully conceptualized despite advances in fluid dynamics, materials science, civil engineering, and computer science [13]. The findings documented by [PERSON] et al. [15] concerning the usefulness of AI-based genetic algorithms (GAs), adaptive neurofuzzy inference systems (ANFISs), and artificial neural networks (ANNs) underscores the suitability of the latter in the design of materials and fire retardants that achieve satisfactory fire performance. However, it is of note that different networks offer distinct capabilities. This worldview is validated by [PERSON] and [PERSON] [47], who argued that fuzzy-based network systems were superior to ANN. The unique benefits associated with fuzzy logic were linked to the unique inferences, including the ability to \"infer the fuzzy output variables from the fuzzy input variables\" [47] (p. 269). This makes it practical to convert the real-world knowledge into mathematical language that can be utilized by the models. The practical utility of the fuzzy system was also demonstrated by [PERSON] [21], whose simulations utilized fuzzy and selection queries to identify partial location information and consequently establish the location of an individual in buildings at risk of fire. The attainment of satisfactory fire performance could be realized through the customization of the load level, thermal stability and other material features, and geometric parameters. It is impractical to tailor each of these parameters without the integration of AI systems [2, 13, 14, 15, 31]. Even though there is a strong case for AI systems in fire prevention, past computer models and simulations have yielded distinct outcomes, a phenomenon that is attributed to the unique response of different structures and materials, different stimulation parameters and environments, and technical expertise. The key parameters that predict the outcomes of the simulation models include wall thickness, diameter ratio, outer diameter, load ratios and temperature, and diameter-thickness ratio (see Table 1). For example, simulation experiments conducted by [PERSON] et al. [45] confirmed that K-CRISP prediction provides an accurate representation, but the accuracy was impacted by time. Other fundamental concerns relate to the specificity of the technology. The K-CRISP system works best using the Monte Carlo fire simulation model and FireGrid systems [45, 48]. There is no guarantee that it would function as intended using alternative mechanisms due  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Trusses** & **F\({}_{\text{u}}\) (kN)** & \multicolumn{6}{c}{**Critical Temperature (IC)**} \\ \hline  & & 0.2F\({}_{\text{u}}\) & 0.3F\({}_{\text{u}}\) & 0.4F\({}_{\text{u}}\) & 0.5F\({}_{\text{u}}\) & 0.6F\({}_{\  
to concerns about the accuracy of AI-based networks in modeling the potential sources of heat that can cause fires in farms. [PERSON] [23] noted that fires in agricultural settings could be triggered by heat generated by stored grains. Such heat is generated via anaerobic fermentation and aerobic respiration, which are natural postharvest biological processes that occur as grains lose excess moisture to attain equilibrium in the moisture content. If the storage structures lack appropriate vents for heat to escape through, there is an imminent risk of pyrolysis even in the absence of oxygen [23]. Similar fire risks were documented during the storage of grains stored in biomass fuels in large storage units [49]. In the latter case, the risk of self-ignition was attributed to self-heating.