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Body Mass Index in Human Gait for Building Risk Assessment Using Graph Theory.

Washington Velásquez1,2, Manuel S Alvarez-Alvarado1, Andres Munoz-Arcentales1,2

  • 1Escuela Superior Politécnica del Litoral, Campus Gustavo Galindo Km 30.5 Vía Perimetral, P.O. Box 09-01-5863, Guayaquil EC090112, Ecuador.

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Summary
This summary is machine-generated.

This study models how body mass index (BMI) affects human gait dynamics and predicts building evacuation times using graph theory and simulations. Findings enhance civil safety assessments by integrating physiological data with emergency response models.

Keywords:
Monte Carlo simulationbody mass indexbreadth-first searchevacuation routeshuman gait

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Area of Science:

  • Human physiology
  • Biomechanical engineering
  • Civil safety engineering

Background:

  • Understanding human physiology, specifically body mass index (BMI), is crucial for accurate biomechanical modeling.
  • Existing models for building evacuation often lack detailed physiological considerations.
  • Assessing civil safety requires integrating human movement dynamics with structural and emergency response parameters.

Purpose of the Study:

  • To investigate the influence of body mass index (BMI) on human gait parameters.
  • To develop a mathematical model for predicting human evacuation times from buildings during emergencies.
  • To quantify building risk by simulating emergency scenarios.

Main Methods:

  • Mathematical modeling of human gait based on center of mass, inertia, and speed.
  • Graph theory representation of buildings for simulating occupant movement.
  • Application of Breadth-First Search (BFS) algorithm for optimal route identification.
  • Gaussian process modeling for evacuation time prediction.
  • Non-sequential Monte Carlo simulation for building risk quantification.

Main Results:

  • Established a link between body mass index (BMI) and human gait characteristics.
  • Developed a predictive model for building evacuation times.
  • Quantified building risk through comprehensive simulation.
  • Demonstrated the utility of integrating physiological data into safety models.

Conclusions:

  • The study provides a novel, integrated approach to civil safety assessment.
  • Incorporating human physiological factors like BMI enhances the realism of evacuation models.
  • The developed methodology offers a foundation for more accurate risk assessment and emergency planning.