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ADAPTS: An Intelligent Sustainable Conceptual Framework for Engineering Projects.

Amalia Luque1, Ana De Las Heras1, María Jesús Ávila-Gutiérrez1

  • 1Dpto. Ingeniería del Diseño. Escuela Politécnica Superior. Universidad de Sevilla. Virgen de África, 7. 41011 Sevilla, Spain.

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PubMed
Summary
This summary is machine-generated.

This study introduces the ADAPTS framework, utilizing Machine Learning techniques to optimize environmental sustainability in engineering projects. It provides a structured approach for researchers to integrate sustainability criteria into project development.

Keywords:
LCASDG 11SDG 9applicationapproachesconceptual frameworkdataproject engineeringsensorstools

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Area of Science:

  • Engineering
  • Environmental Science
  • Computer Science

Background:

  • Optimization of environmental sustainability is crucial for engineering projects, encompassing products, facilities, and processes.
  • Existing research lacks a unified framework for integrating Machine Learning (ML) into sustainability optimization.
  • A structured approach is needed to guide researchers in applying ML for enhanced environmental performance in engineering.

Purpose of the Study:

  • To propose a novel conceptual framework, ADAPTS, for optimizing environmental sustainability in engineering projects.
  • To facilitate the application of Machine Learning techniques in sustainability-driven engineering optimization.
  • To provide a clear, hierarchical structure for understanding the key variables and their interrelationships in sustainability optimization.

Main Methods:

  • A comprehensive bibliographic search was conducted on the Web of Science database.
  • A hermeneutic procedure was employed to analyze selected literature and synthesize findings.
  • A pyramid-shaped graphic representation illustrates the five dimensions of the ADAPTS framework.

Main Results:

  • The developed conceptual framework, ADAPTS, comprises five key dimensions: Application, Data, Approach, Tool, and Sensing.
  • The framework visually represents the relationships between these dimensions, aiding in understanding the optimization process.
  • A case study is presented to demonstrate the practical applicability of the ADAPTS framework.

Conclusions:

  • The ADAPTS framework offers a systematic methodology for researchers to optimize environmental sustainability in engineering projects using ML.
  • The framework's modular structure allows for adaptation to various engineering applications and ML tools.
  • This work contributes to advancing the integration of sustainability principles and advanced computational methods in engineering design and operation.