Predicting an energy use intensity and cost of residential energy-efficient buildings using various parameters: ANN analysis

Jayakeerti, M.; Nakkeeran, G.; Aravindh, M. Durai; Krishnaraj, L.

doi:10.1007/s42107-023-00717-y

Cited by 10 publications

(1 citation statement)

References 35 publications

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“…The transient state analysis of rehabilitated RC beams using finite element modelling and response surface methodology represents a sophisticated approach to addressing the complexities of structural engineering [11,12]. By leveraging the capabilities of FEM to simulate dynamic behaviors and ANN to predict responses under different scenarios, engineers can optimize the rehabilitation process, ensuring that the RC beams meet or exceed performance expectations.…”

Section: Introductionmentioning

confidence: 99%

Transient state analysis of rehabilitated RC beams using finite element modelling and prediction using an artificial neural network

2024

Eng. Res. Express

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The present research develops and verifies a simpler numerical approach for analyzing the thermal transient state of rehabilitated concrete beams reinforced with various types of FRP (fiber-reinforced polymer) subjected to high temperatures and specifically built as under-reinforced concrete beams. This approach offers a straightforward, efficient, and exact instrument for numerical analysis. The proposed analytical technique has been validated by load-displacement curves and cross-section temperature data, indicating its dependability and practicality. Subsequently, the validated approach was used to examine the impact of significant variables on the outcome and restoration of FRP-reinforced concrete beams at high temperatures. The methodology gives the Comparing conventional and CFRP, GFRP, AFRP reinforced beams using beam, truss, and shell elements. Thermal and UDL loads were applied, mesh at 25mm x 25mm. Transient analysis contrasts performance via displacement and temperature. The temperature vs. displacement curve shows the FRP comparisons. Identifying the critical temperature before failure is crucial, emphasizing the curve's significance in assessing structural performance and potential failure points. Nodal temperatures ranged 939-963°C (rehabilitated) vs 958°C (conventional). 200°C difference affects thermal boundary conditions for structural analysis and Conventional peaks at 320°C, while AFRP, GFRP, and CFRP reach 358°C, 385°C, and 390°C respectively. CFRP lasts 2400 minutes. Neural network models demonstrate effective generalizability, enabling satisfactory predictions of RC beam rehabilitation with CFRP laminates within the study's parameter range.

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Section: Introductionmentioning

confidence: 99%

Transient state analysis of rehabilitated RC beams using finite element modelling and prediction using an artificial neural network

2024

Eng. Res. Express

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Scientometric Analysis of Building Energy Analysis in the Construction During 2005–2022

Nakkeeran,

Krishnaraj

2023

Lecture Notes in Civil Engineering

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Soft computing techniques for predicting the compressive strength properties of fly ash geopolymer concrete using regression-based machine learning approaches

Philip,

Nakkeeran

2024

J Build Rehabil

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Predicting an energy use intensity and cost of residential energy-efficient buildings using various parameters: ANN analysis

Cited by 10 publications

References 35 publications

Transient state analysis of rehabilitated RC beams using finite element modelling and prediction using an artificial neural network

Transient state analysis of rehabilitated RC beams using finite element modelling and prediction using an artificial neural network

Scientometric Analysis of Building Energy Analysis in the Construction During 2005–2022

Soft computing techniques for predicting the compressive strength properties of fly ash geopolymer concrete using regression-based machine learning approaches

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