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Predicting the Response of RC Beam from a Drop-Weight Using Gene Expression Programming.

Moiz Tariq1, Azam Khan1, Asad Ullah1

  • 1NUST Institute of Civil Engineering (NICE), School of Civil and Environmental Engineering, National University of Science and Technology (NUST), Sector H-12, Islamabad 44000, Pakistan.

Materials (Basel, Switzerland)
|October 14, 2022
PubMed
Summary

A new model accurately predicts peak impact force in reinforced concrete beams under extreme loading. It considers factors like velocity, weight, and material properties, improving upon existing methods for structural safety.

Keywords:
gene expression programming (GEP)impact loadingnumerical simulationreinforced concrete beamstatistical analysis

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Area of Science:

  • Structural Engineering
  • Materials Science
  • Computational Mechanics

Background:

  • Predicting peak impact force in reinforced concrete (RC) beams under extreme loading is critical for structural safety.
  • Existing numerical and soft computing models often lack accuracy in peak impact force prediction.

Purpose of the Study:

  • To develop a simple, user-friendly, and accurate predictive model for peak impact force in RC beams.
  • To incorporate previously overlooked influencing factors into the predictive model.

Main Methods:

  • Utilized gene expression programming (GEP) with a database of 126 impact force experiments on simply supported RC beams.
  • Included impact velocity, impactor weight, concrete compressive strength, shear span to depth ratio, and tensile reinforcement quantity and strength.
  • Validated the model through statistical analysis and 3D finite element simulations in ABAQUS.

Main Results:

  • The developed GEP model accurately predicts peak impact force, overcoming limitations of existing methods.
  • Identified and integrated key parameters influencing impact force, enhancing model robustness.
  • The model demonstrated strong agreement with experimental data and finite element simulations.

Conclusions:

  • The proposed GEP model offers a significant improvement for predicting peak impact force in RC beams.
  • The model's ability to predict dynamic shear force and bending moment diagrams makes it ideal for practical engineering applications.
  • This research provides a valuable tool for enhancing the resilience of structures subjected to impact loading.