Implementation of a Life Cycle Cost Deep Learning Prediction Model Based on Building Structure Alternatives for Industrial Buildings

Meshref, Ahmed Nouh; El–Dash, Karim; Basiouny, Mohamed A.; El-Hadidi, Omia

doi:10.3390/buildings12050502

Cited by 10 publications

(3 citation statements)

References 51 publications

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“… Predictive modelling: Under various loads and environmental circumstances, RC frame performance and behaviour may be predicted using ML algorithms. The viability and dependability of design choices are assessed with the aid of these prediction models [39].…”

Section: Data Visualization Was Conducted By Utilizingmentioning

confidence: 99%

Transformative role of machine learning in design optimization of reinforced concrete frames

2024

IJATEE

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Design optimization in reinforced concrete (RC) frames is crucial for achieving more efficient, cost-effective, and safe constructions. This involves determining optimal section sizes and reinforcing schemes to enhance structural performance, reduce construction expenses, and conserve natural resources. An optimized design contributes to streamlined construction procedures, shorter construction times, and improved overall sustainability. This paper critically evaluates the current literature on machine learning (ML) applications in the design optimization of RC structures, focusing on the period from 1997 to 2023. The study employs a comprehensive methodology, including bibliometric analysis, to analyze trends, research collaborations, and publication patterns. The results highlight the increasing interest in ML for RC frame optimization. Key applications of ML in design optimization include material characterization, design exploration, optimization algorithms, sensitivity analysis, predictive modeling, structural health monitoring, design code compliance, uncertainty quantification, data-driven design decisions, and design collaboration. The study identifies significant ML algorithms used in optimization, such as the trial-and-error method, linear programming (LP), and nonlinear programming (NLP). Overall, the paper provides insights into the evolving landscape of ML applications in RC frame design, emphasizing the potential for interdisciplinary collaboration and future research directions.

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Section: Data Visualization Was Conducted By Utilizingmentioning

confidence: 99%

Transformative role of machine learning in design optimization of reinforced concrete frames

2024

IJATEE

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“…The main provisions of [38] stand for this decision. Of some interest is the lifecycle of a structure, taking into account the impact perception to be evaluated using the methodology provided in [39], based on (a), relaxed-tensioned cables (b) [30], prestressed joint with energy dissipating elements (c) [31], and supplementary frame structure (d) [32].…”

Section: The Effect Of Initial and Impact-triggered Imperfectionsmentioning

confidence: 99%

“…The main provisions of [38] stand for this decision. Of some interest is the lifecycle of a structure, taking into account the impact perception to be evaluated using the methodology provided in [39], based on the variant design of a steel structure and the restorability of its bearing capacity.…”

Section: Other Featuresmentioning

confidence: 99%

Ensuring the Safety of Steel Moment Frames Subjected to Uncertain Impacts

Alekseytsev

2023

Buildings

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The article addresses the problem of safety evaluation of steel moment frames of civil buildings, e.g., warehouses, shops, garages, and multistory industrial buildings on deformable soil in the relevant case of an emergency impact. The case of accidental emergency impacts is considered when such parameters as the point, direction, and intensity of an impact cannot be predetermined. Such impacts are not expected to trigger the progressive collapse of currently implemented design solutions and the whole structure must maintain the property of survivability. To evaluate this property, several calculations are to be made in the quasi-static statement to identify the stress–strain state under the most dangerous accidental impacts. Further, final calculations are to be made in the dynamic statement. In this case, the problem of search is solved using the criterion of minimizing the integral safety margin of structural elements in a steel moment frame design. Calculations prevent the frame stability loss. The calculation is performed in the quasi-static statement using models made in compliance with the deformation theory of plasticity, while the calculation in the dynamic statement takes into account the associated plastic flow rule. The proposed procedures allow for designing steel moment frames that are resistant to accidental emergency impacts. Impact loading is analysed as pulse loading, which is statically equivalent to the dynamic effect of an inelastic impact of a stiff body on a structural system. The design and the efficiency evaluation of a steel moment frame of a two-story building are considered.

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Reducing construction waste in the construction life cycle of industrial projects during design phase by using system dynamics

Meshref

Elkasaby

Farid

2023

Journal of Building Engineering

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Implementation of a Life Cycle Cost Deep Learning Prediction Model Based on Building Structure Alternatives for Industrial Buildings

Cited by 10 publications

References 51 publications

Transformative role of machine learning in design optimization of reinforced concrete frames

Transformative role of machine learning in design optimization of reinforced concrete frames

Ensuring the Safety of Steel Moment Frames Subjected to Uncertain Impacts

Reducing construction waste in the construction life cycle of industrial projects during design phase by using system dynamics

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