<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>Carbon emission from building construction is an important issue in the construction industry, and the continuous application of management and assessment techniques forms a new trend of carbon emission reduction. This paper establishes a multi-objective optimization model for building construction carbon emission based on multidimensional data analysis of four objectives: schedule, cost, quality, and carbon emission. The particle swarm algorithm is improved by dynamically adjusting the inertia weight factor and penalty function to deal with constraints and is used to solve the multi-objective optimization model. Building construction cases are selected to predict and analyze the combinations of optimal execution modes under different teams, to compare the optimization results of configuration schemes and the efficiency of algorithms, and to further propose the practical path of carbon emission reduction in building construction. The study addresses the construction combination mode that meets the needs of various decision-makers, and the team 3 combination mode has the lowest carbon emission (228.41kg). The multi-objective optimization scheme under carbon emission constraints optimizes between 3.93% and 21.78% in the four objective dimensions. This paper further expands the scope of the multi-objective optimization model for building construction focusing on the “low carbon” objective, improves the feasibility of the configuration scheme, and then promotes the green and stable development of the construction industry.</p>
</abstract>

Carbon emission from building construction is an important issue in the construction industry, and the continuous application of management and assessment techniques forms a new trend of carbon emission reduction. This paper establishes a multi-objective optimization model for building construction carbon emission based on multidimensional data analysis of four objectives: schedule, cost, quality, and carbon emission. The particle swarm algorithm is improved by dynamically adjusting the inertia weight factor and penalty function to deal with constraints and is used to solve the multi-objective optimization model. Building construction cases are selected to predict and analyze the combinations of optimal execution modes under different teams, to compare the optimization results of configuration schemes and the efficiency of algorithms, and to further propose the practical path of carbon emission reduction in building construction. The study addresses the construction combination mode that meets the needs of various decision-makers, and the team 3 combination mode has the lowest carbon emission (228.41kg). The multi-objective optimization scheme under carbon emission constraints optimizes between 3.93% and 21.78% in the four objective dimensions. This paper further expands the scope of the multi-objective optimization model for building construction focusing on the “low carbon” objective, improves the feasibility of the configuration scheme, and then promotes the green and stable development of the construction industry.

Research on multi-objective optimization and prediction of building construction carbon emission based on multi-dimensional data analysis

Applied Mathematics and Nonlinear Sciences

This work is licensed under the Creative Commons Attribution 4.0 International License.

Carbon emission from building construction is an important issue in the construction industry, and the continuous application of management and assessment...

Research on multi-objective optimization and prediction of building construction carbon emission based on multi-dimensional data analysis

Publicado en línea: 05 ago 2024

Páginas: -

Recibido: 18 abr 2024

Aceptado: 03 jul 2024

DOI: https://doi.org/10.2478/amns-2024-2104

Palabras clave
Multi-dimensional data analysis, Multi-objective optimization, Particle swarm algorithm, Predictive analysis, Carbon emission, Building construction

© 2024 Junqiang Zhang, published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Research on multi-objective optimization and prediction of building construction carbon emission based on multi-dimensional data analysis

Publicado en línea: 05 ago 2024

Páginas: -

Recibido: 18 abr 2024

Aceptado: 03 jul 2024

DOI: https://doi.org/10.2478/amns-2024-2104

Palabras claveMulti-dimensional data analysis, Multi-objective optimization, Particle swarm algorithm, Predictive analysis, Carbon emission, Building construction

© 2024 Junqiang Zhang, published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Palabras clave
Multi-dimensional data analysis, Multi-objective optimization, Particle swarm algorithm, Predictive analysis, Carbon emission, Building construction