# Applications of Artificial Intelligence in Fire Safety of Agricultural Structures
## 2 Fire Safety in Agricultural Structures/Farms
### Standards, Material-Specific Limits, and Scenario Constraints to Accurate Simulations  
The long-term utility of AI simulations in preventing fires in agricultural structures depends on material properties, standards, and accurate simulations. For example, the fire-retarding ability of reinforced concrete could be impaired by corrosion, which is common in farms [55]. Existing models are unable to accurately predict the temperature-dependent material properties of structural steel. This is a pertinent concern given that most modern agricultural structures are constructed using steel [56]. Moreover, the international building code recommends that pressure tanks, piping systems, valves, and fittings should be made of steel [38]. The broad application of steel predicts its influence on fire safety in various farm and nonfarm environments. The development of advanced systems is capable of reconciling the discrepancies between ASCE and Eurocode 3 model estimates [56] and seamless interaction and interaction with the existing agricultural sensors for agricultural structures, including electrochemical, ultrasonic, fiber-optic, piezoelectric, wireless, fiber Bragg grating sensors, and self-sensing concrete [57]. From a materials perspective, both Eurocode 3 and ASCE are grounded on erroneous assumptions about steel, including the fact that the fabrication, composition of steel is independent of its origins [56]. Published data on steel vary significantly across different models, a factor that limits the reliability and the utilization of the simulations in fire safety decision making. The criticism of existing Eurocode 3 and ASCE data relating to temperature-dependent material properties of structural steel is informed by the following facts. First, according to Eurocode 3, the temperature reduction factor of steel at 450 \({}^{\circ}\)C is 0.88 [56]. In contrast, the ASCE estimates the value to be 0.63 [56]. Even though such differences are perceived to be marginal, they have significant implications on accurate fire modeling. Recent studieshave attempted to address the problem through the standardization of material properties using AI and machine learning techniques in an ANSYS environment. The accuracy of the model was further predicted by the incorporation of data drawn from various building codes and standards and experimental data in the public domain. The observations made by [PERSON] [56] about the limits of existing building codes and systems relating to steel are in agreement with [PERSON] [23], who noted that existing codes were less relevant for agricultural structures and environments. The observations made by [PERSON] do not apply to BS5502: Part 23: 2004 [58] and CFPA-E Guideline No. 17:2015 F [59], which have specific provisions for agricultural structures. In contrast to steel, the use of concrete in agricultural construction diminishes the need for fire safety and adherence to fire codes. This is because concrete is a natural fireproofing material that can withstand fire exposure for at least 2 h. The fireproofing abilities of concrete have been acknowledged by the European Committee for Standardization and BSI. A major challenge is that even though concrete structures satisfy most of the codal provisions related to fire safety and have consistently proven useful over time, there is a probability that the fire resistance potential of concrete is overestimated [1]. This has been confirmed in postfire analysis investigations. The resolution of concrete fireproofing challenges using AI networks is underexplored and does not reflect the existing realities in the agricultural construction materials. Old concrete structures in farms were made using concrete materials that bear little semblance to modern concrete. Advances in materials science and engineering have resulted in the creation of concrete materials with advanced properties [1], which, in turn, impact their fireproofing abilities.