Autonomous Fire Resistance Evaluation

Naser, M.Z.

doi:10.1061/(asce)st.1943-541x.0002641

Cited by 27 publications

(10 citation statements)

References 54 publications

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“…In these tests, small-sized specimens are prepared and examined in a steady-state, or transient-state manner to identify how fire testing influences the examined property (say compressive strength) as a function of the testing procedure (i.e., temperature rise, loading conditions, etc.). Unfortunately, little has been conducted on this front [4][5][6][7].…”

Section: Introductionmentioning

confidence: 99%

Generalized temperature-dependent material models for compressive strength of masonry using fire tests, statistical methods and artificial intelligence

Daware

Naser

Karaki

2022

Archit. Struct. Constr.

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Masonry has superior fire resistance properties stemming from its inert characteristics, and slow degradation of mechanical properties. However, once exposed to fire conditions, masonry undergoes a series of physio-chemical changes. Such changes are often described via temperature-dependent material models. Despite calls for standardization of such models, there is a lack in such standardized models. As a result, available temperature-dependent material models vary across various fire codes and standards. In order to bridge this knowledge gap, this paper presents three methodologies, namely, regression-based, probabilistic-based, and the use of artificial neural (ANN) networks, to derive generalized temperature-dependent material models for masonry with a case study on the compressive strength property. Findings from this paper can be adopted to establish updated temperature-dependent material models of fire design and analysis of masonry structures.

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Section: Introductionmentioning

confidence: 99%

Generalized temperature-dependent material models for compressive strength of masonry using fire tests, statistical methods and artificial intelligence

Daware

Naser

Karaki

2022

Archit. Struct. Constr.

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“…It is generally the explosion at the surface layers of the concrete element exposed to an elevated temperature that could be triggered by fire. However, by leveraging the power of artificial intelligence and machine learning, Naser constructed many AI-derived models to predict the fire-induced spalling and fire resistance of concrete elements [ 73 , 74 , 75 , 76 , 77 , 78 ]. These models were built on the basis of various machine learning algorithms (e.g., logistic regression, decision tree, random forest, gradient boosted trees, and support vector machine) or the combination of neural networks with genetic algorithms.…”

Section: Application Of Ai/ml In Fire Engineeringmentioning

confidence: 99%

Review on the Use of Artificial Intelligence to Predict Fire Performance of Construction Materials and Their Flame Retardancy

Nguyên

Nguyen

et al. 2021

Molecules

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The evaluation and interpretation of the behavior of construction materials under fire conditions have been complicated. Over the last few years, artificial intelligence (AI) has emerged as a reliable method to tackle this engineering problem. This review summarizes existing studies that applied AI to predict the fire performance of different construction materials (e.g., concrete, steel, timber, and composites). The prediction of the flame retardancy of some structural components such as beams, columns, slabs, and connections by utilizing AI-based models is also discussed. The end of this review offers insights on the advantages, existing challenges, and recommendations for the development of AI techniques used to evaluate the fire performance of construction materials and their flame retardancy. This review offers a comprehensive overview to researchers in the fields of fire engineering and material science, and it encourages them to explore and consider the use of AI in future research projects.

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“…Notable and recent works have reported that traditional and modern concretes (such as those of high strength and/or ultra high-strength characteristics) can be prone to spalling [ 7 – 11 ]. Spalling amplifies in modern concretes due to the complex chemicals/additives involved in preparing such mixtures and their resulting denser microstructure [ 12 , 13 ]. Holistically, fire-induced spalling can be attributed to three temperature-dependent/temperature-triggered mechanisms: (1) rise in pore vapor pressure, (2) generated thermal stresses, and (3) combined pore pressure and thermal stresses [ 10 ].…”

Section: Introductionmentioning

confidence: 99%

Discovering Graphical Heuristics on Fire-Induced Spalling of Concrete Through Explainable Artificial Intelligence

Tapeh

Naser

2022

Fire Technol

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Fire-induced spalling of concrete continues to be an intriguing and intricate research problem. A deep dive into the open literature highlights the alarming discrepancy and inconsistency of existing theories, as well as the complexity of predicting spalling. This brings new challenges to creating fire-safe concretes and primes an opportunity to explore modern methods of investigation to tackle the spalling phenomenon. Thus, this paper leverages the latest advancements in explainable Artificial Intelligence (XAI) to vet existing theories on fire-induced spalling and to discover solutions/heuristics to predict spalling of concrete mixtures. The developed heuristics are in the form of graphs and nomograms . The proposed solutions allow interested researchers and engineers to graphically identify the propensity of a given concrete mixture to spalling directly and with ease. For example, we report that concrete mixtures with a combination of moderate water/binder ratio (of about 0.3), low heating rate (less than 2.5°C/min), moderate rise in temperature (less than 500°C), and have moisture content (less than 3%) are expected to be less prone to spalling. Further, findings from this research showcase the potential for developing simple (i.e., one-shot) and graphical (coding-free and formula-free) XAI-based solutions and web applications to address decades-long problems in the area of concrete research.

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Autonomous Fire Resistance Evaluation

Cited by 27 publications

References 54 publications

Generalized temperature-dependent material models for compressive strength of masonry using fire tests, statistical methods and artificial intelligence

Generalized temperature-dependent material models for compressive strength of masonry using fire tests, statistical methods and artificial intelligence

Review on the Use of Artificial Intelligence to Predict Fire Performance of Construction Materials and Their Flame Retardancy

Discovering Graphical Heuristics on Fire-Induced Spalling of Concrete Through Explainable Artificial Intelligence

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