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

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...

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Published inApplied mathematics and nonlinear sciences Vol. 9; no. 1
Main Author Zhang, Junqiang
Format Journal Article
LanguageEnglish
Published Beirut Sciendo 01.01.2024
De Gruyter Brill Sp. z o.o., Paradigm Publishing Services
Subjects
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ISSN2444-8656
2444-8656
DOI10.2478/amns-2024-2104

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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.
AbstractList 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.
Author Zhang, Junqiang
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SubjectTerms 62-07
Building construction
Carbon
Carbon emission
Construction industry
Data analysis
Multi-dimensional data analysis
Multi-objective optimization
Optimization
Particle swarm algorithm
Predictive analysis
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