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This structure failure prediction system has the following components: sensor network, structural model, damage detection component, and AI. Depending on the type of input sensor data and desired output data, different AI algorithms are employed. Graph: Figure 2 Evacuation navigation and routing system and its components AI Emergency Management and Decision Support At the core of the outlined framework is an AI system that is trained using previously simulated fire and evacuation scenarios. Data gathered are the main input to the structure health AI component which predicts information on the structural integrity and conveys it to first responders. [Extracted from the article]

One-way glass fibre reinforced polymer (GFRP) reinforced concrete slabs at elevated temperatures are investigated through numerical modeling. Serviceability and strength requirements of ACI-440.1R are considered for the design of the slabs. Diagrams to determine fire endurance of slabs by employing "strength domain" failure criterion are presented. Comparisons between the existing "temperature domain" method with the more representative "strength domain" method show that the "temperature domain" method is conservative. Additionally, a method to increase the fire endurance of slabs by placing FRP reinforcement in two layers is investigated numerically. The amount of fire endurance gained by placing FRP in two layers increases as the thickness of slab increases. [ABSTRACT FROM AUTHOR]

This paper presents experimental and numerical results of intermediate-scale and full-scale fire tests conducted on flexurally strengthened reinforced concrete members. Two full-scale reinforced concrete T-beams and two intermediate-scale slabs were strengthened in flexure with carbon fibre reinforced polymer sheets or plates and insulated with a layer of spray-on material. T-beams and slabs were then exposed to a standard fire. T-beams were loaded to their service load. Deflections and temperatures were measured during the tests. A numerical finite-difference/volume heat transfer model was used to simulate the temperature field inside the section. The validity of the numerical model was verified by comparing the predicted temperatures with those recorded during the fire test. Fire test results show that fire endurances of more than 4 h can be achieved using an appropriate insulation system. [ABSTRACT FROM AUTHOR]

• Implementation of Artificial Neural Network (ANN) to study the critical factors of bridge fire incidents. • Defining I-girder steel bridges as the most susceptible structural system. • Defining hydrocarbon tankers and trucks with solid combustible cargo as the most hazardous sources. • Potential hazard prevention and protection measures to improve structural and human life safety, bridge durability, and reduce maintenance costs. Fire poses a major risk to the structural integrity of a bridge or progressively to the bridge's failure. This study uses an Artificial Neural Network (ANN) to investigate the determining factors in bridge fire incidents. Implementation of this powerful model produces an estimation of the damage levels. The basis of the proposed model is determined by critical factors, including bridge location, materials, structural systems, annual average daily traffic (AADT), ignition source, combustible type, and bridge face exposed to fire. Results show that steel I-girder bridges are the most susceptible structural system in a fire. Moreover, a fire involving tankers containing hydrocarbon fuel and trucks with solid combustible cargo is the most dangerous to a bridge. The accuracy of the proposed model is verified, and its outcome can be utilized to determine fire risk based on discrete characteristics. Based on the proposed model, a combination of hazard prevention and protection measures may be utilized to improve structural integrity, human life safety, and durability, and to reduce maintenance costs. [ABSTRACT FROM AUTHOR]