Building Artificial-Intelligence Digital Fire (AID-Fire) system: A real-scale demonstration

Zhang, Tianhang; Wang, Zilong; Zeng, Yanfu; Wu, Xiaoni; Huang, Xinyan; Xiao, Fu

doi:10.1016/j.jobe.2022.105363

Cited by 28 publications

(5 citation statements)

References 41 publications

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“…Expanding the training set is expected to improve the model's performance in handling such scenarios. At the same time, the position of the high-temperature fire source shown by the forecast results is not fixed, because the plume will sway and tilt under the influence of local airflow [27], so there will be obvious deviations. In practice, the model's prediction of the ignition source is usually off by no more than 3 m, usually within the acceptable range.…”

Section: Prediction Performance On Spatial Temperature Distributionsmentioning

confidence: 99%

Data-Driven Prediction Methods for Real-Time Indoor Fire Scenario Inferences

Zhang,

Mo,

Fan

et al. 2023

Fire

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High temperatures, toxic gases, and smoke resulting from indoor fires pose evident threats to the lives of both trapped individuals and firefighters. This study aims to predict indoor fire development effectively, facilitating rapid rescue decisions and minimizing casualties and property damage. A comprehensive database has been developed using Computational Fluid Dynamics (CFD) tools, primarily focused on basic fire scenarios. A total of 300 indoor fire scenarios have been simulated for different fire locations and severity levels. Using fire databases developed from simulation tools, artificial intelligence models have been developed to make spatial–temporal inferences on indoor temperature, CO concentration, and visibility. Detailed analysis has been conducted to optimize sensor system layouts while investigating the variations in prediction accuracy according to different prediction horizons. The research results show that, in combination with artificial intelligence models, the optimized sensor system can accurately predict temperature distribution, CO concentration, and visibility, achieving R2 values of 91%, 72%, and 83%, respectively, while reducing initial hardware costs. The research results confirm the potential of artificial intelligence in predicting indoor fire scenarios and providing practical guidelines for smart firefighting. However, it is important to note that this study has certain limitations, including the scope of fire scenarios, data availability, and model generalization and interpretability.

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Section: Prediction Performance On Spatial Temperature Distributionsmentioning

confidence: 99%

Data-Driven Prediction Methods for Real-Time Indoor Fire Scenario Inferences

Zhang,

Mo,

Fan

et al. 2023

Fire

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“…For fire prediction, ML techniques combined with zone models have been proposed to recover missing data in cases in which sensors are destroyed during fires [70,71]. Moreover, AI has been applied for fire source locations [24,72]. For instance, Wu et al [72] applied AI and big data to predict the location and size of a fire source in a tunnel.…”

Section: Artificial Intelligence (Ai)mentioning

confidence: 99%

“…Recently, researchers have made significant progress in the development of DT technologies and tools for fire safety [22][23][24]. For instance, Jiang et al [23] proposed a system that combines DT technology, semantic web technologies, and IoT data to enable the dynamic monitoring and predictive operation of fire protection systems.…”

Section: Introductionmentioning

confidence: 99%

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Digital-Twin-Based Fire Safety Management Framework for Smart Buildings

Almatared,

Liu,

Abudayyeh

et al. 2023

Buildings

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In recent years, the implementation of digital twin (DT) technology has gained significant attention in various industries. However, the fire safety management (FSM) sector has been relatively slow in adopting this technology compared to other major industries. Therefore, this study aims to explore the limitations, opportunities, and challenges associated with adopting DT technology in the FSM sector and further develop a DT-based FSM framework towards smart facility management (FM). To achieve this objective, this research started by reviewing several promising DTs for FSM, including building information modeling (BIM), the Internet of Things (IoT), artificial intelligence (AI), and augmented reality (AR). On this basis, a conceptual framework was synthesized in consideration of the benefits of each technology. A questionnaire was conducted for FM professionals to evaluate the proposed framework and identify the challenges of adopting DT in the FSM sector. The survey results reveal that the proposed framework can assist decision makers in obtaining comprehensive information about facilities’ communication among stakeholders. The survey results validate the potential of the adoption of DTs toward smart FM practices in FSM. The survey results provide insights into the perception of DT technology among FM practitioners and identify the current state of DT technology in the FSM sector, its expected benefits, and its potential challenges. The main barriers to adopting DTs in FSM are a lack of knowledge about DTs, their initial costs, user acceptance, difficulties in systems integration, education training costs, a lack of competence, development complexity, difficulties in data management, and a lack of trust in data security.

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“…Also related to smart cities, Zhang et al [87] developed a real-time fire identification system that uses an IoT sensor network, cloud server, AI engine, and user interface to collect, store, process, and display complex building fire information. Their designed system also leveraged Conv-LSTM neural network.…”

Section: Real-time Fire Identification Systemsmentioning

confidence: 99%

Deep Neural Networks for Spatial-Temporal Cyber-Physical Systems: A Survey

et al. 2023

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Cyber-physical systems (CPS) refer to systems that integrate communication, control, and computational elements into physical processes to facilitate the control of physical systems and effective monitoring. The systems are designed to interact with the physical world, monitor and control the physical processes while in operation, and generate data. Deep Neural Networks (DNN) comprise multiple layers of interconnected neurons that process input data to produce predictions. Spatial-temporal data represents the physical world and its evolution over time and space. The generated spatial-temporal data is used to make decisions and control the behavior of CPS. This paper systematically reviews the applications of DNNs, namely convolutional, recurrent, and graphs, in handling spatial-temporal data in CPS. An extensive literature survey is conducted to determine the areas in which DNNs have successfully captured spatial-temporal data in CPS and the emerging areas that require attention. The research proposes a three-dimensional framework that considers: CPS (transportation, manufacturing, and others), Target (spatial-temporal data processing, anomaly detection, predictive maintenance, resource allocation, real-time decisions, and multi-modal data fusion), and DNN schemes (CNNs, RNNs, and GNNs). Finally, research areas that need further investigation are identified, such as performance and security. Addressing data quality, strict performance assurance, reliability, safety, and security resilience challenges are the areas that are required for further research.

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Building Artificial-Intelligence Digital Fire (AID-Fire) system: A real-scale demonstration

Cited by 28 publications

References 41 publications

Data-Driven Prediction Methods for Real-Time Indoor Fire Scenario Inferences

Data-Driven Prediction Methods for Real-Time Indoor Fire Scenario Inferences

Digital-Twin-Based Fire Safety Management Framework for Smart Buildings

Deep Neural Networks for Spatial-Temporal Cyber-Physical Systems: A Survey

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