Significant Management Factors to Reduce Carbon Emission of Infrastructure Construction Project in Thailand DOI

S Liwthaisong,

Kittiwet Kuntiyawichai,

Supakorn Tirapat

и другие.

Опубликована: Янв. 1, 2024

This study investigates the important management factors contributing to carbon emissions of infrastructure construction project in Thailand. Specifically, research seeks identify which stages process most significantly cause emissions. At beginning, were reviewed and selected. Then, interviews with managers related persons projects conducted investigate real context. Next, discussion five experts who have relevant experience emission for then arose validate possible key factors. Based on expert comments, questionnaire was developed its validity clarity. After that, distributed 829 certified from Thai Green Building Institute (GBI), employs exploratory factor analysis (EFA) adequacy data, achieving a significant Kaiser-Meyer-Olkin (KMO) measure 0.805. The identifies three primary components responsible emissions: (1) manufacturing materials process, an eigenvalue 4.396, accounting 39.964% variance, highlights impact raw material production; (2) transportation 1.599, explains 14.534% underscoring environmental implications transporting waste; (3) 1.279, contributes 11.624% focusing directly linked on-site activities. results demonstrate strong statistical correlations among measured latent variables, indicating robust model fit. Key highest loadings include concrete production, steel manufacturing, vehicle distance used waste debris disposal, others. not only sources sector but also provides insights into potential areas effective reduction.

Язык: Английский

How to achieve better construction and demolition waste management: insights from the recycled building materials supply chain DOI
Feng Guo, Yinghui Song

Environment Development and Sustainability, Год журнала: 2025, Номер unknown

Опубликована: Фев. 7, 2025

Язык: Английский

Процитировано

1
Interval prediction model for residential daily carbon dioxide emissions based on extended long short-term memory integrating quantile regression and sparse attention DOI

Yuyi Hu,

Xiaopeng Deng, Liwei Yang

и другие.

Energy and Buildings, Год журнала: 2025, Номер unknown, С. 115481 - 115481

Опубликована: Фев. 1, 2025

Язык: Английский

Процитировано

1
BIM-based embodied carbon evaluation during building early-design stage: A systematic literature review DOI

Baolin Huang,

Hong Zhang, Habib Ullah

и другие.

Environmental Impact Assessment Review, Год журнала: 2024, Номер 112, С. 107768 - 107768

Опубликована: Дек. 12, 2024

Язык: Английский

Процитировано

6
Machine Learning for Pedestrian-Level Wind Comfort Analysis DOI Creative Commons
Miray Gür, İlker Karadağ

Buildings, Год журнала: 2024, Номер 14(6), С. 1845 - 1845

Опубликована: Июнь 18, 2024

(1) Background: Artificial intelligence (AI) and machine learning (ML) techniques are being more widely employed in the field of wind engineering. Nevertheless, there is a scarcity research on comfort pedestrians terms conditions with respect to building design, particularly historic sites. (2) Objectives: This aims evaluate ML- computational fluid dynamics (CFD)-based pedestrian (PWC) analysis outputs using novel method that relies sophisticated handling image data. The goal propose assessment enhance efficiency AI models over different urban scenarios. (3) Methodology: stages include climate data, CFD OpenFOAM, ML Autodesk Forma, comparisons results similarity based SSIM, MSE, PSNR metrics. (4) Conclusions: study effectively demonstrates considerable potential utilizing as supplementary tool for evaluating PWC. It maintains high degree accuracy precision, allowing rapid effective assessments. methodology precise comparison two visual absence numerical data allows objective pertinent comparisons, it eliminates any distortions. (5) Recommendations: Additional can explore integration case studies, thus expanding scope studies.

Язык: Английский

Процитировано

5
Management of Carbon Emissions Throughout the Building Life Cycle Based on the Analytic Hierarchy Process DOI Creative Commons
Jinyang Zheng, Zhipeng Lü, Yang Ding

и другие.

Buildings, Год журнала: 2025, Номер 15(4), С. 592 - 592

Опубликована: Фев. 14, 2025

The severe global warming driven by the large-scale emission of greenhouse gases has made reduction carbon emissions a critical priority for economic and social development. Among various sectors, construction industry stands out due to its significant consumption natural resources throughout building process, resulting in considerable environmental burden. In China, from account approximately 40% total emissions. Therefore, mitigating this sector is utmost importance. This study develops an evaluation model low-carbon production management enterprises, utilizing Analytic Hierarchy Process (AHP). Through case study, research identifies practical challenges implementing offers actionable recommendations. Theoretically, provides valuable reference future on energy conservation industry. practice, it guidance enterprises achieving transition.

Язык: Английский

Процитировано

0
Carbon emission assessment and interpretability improvement empowered by machine learning: A case study in four cities, China DOI
Zhan Jin, Wenjing He, Eugenia Gasparri

и другие.

Energy and Buildings, Год журнала: 2025, Номер unknown, С. 115530 - 115530

Опубликована: Фев. 1, 2025

Язык: Английский

Процитировано

0
Predicting CO2 Emissions with Advanced Deep Learning Models and a Hybrid Greylag Goose Optimization Algorithm DOI Creative Commons
Amel Ali Alhussan,

Mohamed A. S. Metwally,

S. K. Towfek

и другие.

Mathematics, Год журнала: 2025, Номер 13(9), С. 1481 - 1481

Опубликована: Апрель 30, 2025

Global carbon dioxide (CO2) emissions are increasing and present substantial environmental sustainability challenges, requiring the development of accurate predictive models. Due to non-linear temporal nature data, traditional machine learning methods—which work well when data structured—struggle provide effective predictions. In this paper, we propose a general framework that combines advanced deep models (such as GRU, Bidirectional GRU (BIGRU), Stacked Attention-based BIGRU) with novel hybridized optimization algorithm, GGBERO, which is combination Greylag Goose Optimization (GGO) Al-Biruni Earth Radius (BER). First, experiments showed ensemble such CatBoost Gradient Boosting addressed static features effectively, while time-dependent patterns proved more challenging predict. Transitioning recurrent neural network architectures, mainly BIGRU, enabled modeling sequential dependence on data. The empirical results show GGBERO-optimized BIGRU model produced Mean Squared Error (MSE) 1.0 × 10−5, best tested approach. Statistical methods like Wilcoxon Signed Rank Test ANOVA were employed validate framework’s effectiveness in improving evaluation, confirming significance robustness improvements due framework. addition accuracy CO2 forecasting, integrated approach delivers interpretable explanations significant factors emissions, aiding policymakers researchers focused climate change mitigation data-driven decision-making.

Язык: Английский

Процитировано

0
Machine Learning for Wind Speed Estimation DOI Creative Commons
İlker Karadağ, Miray Gür

Buildings, Год журнала: 2025, Номер 15(9), С. 1541 - 1541

Опубликована: Май 2, 2025

For more than two decades, computational analysis has been pivotal in expanding architectural capabilities, enabling sustainable design through detailed environmental analysis. Central to creating environments is the profound understanding of wind dynamics, which significantly influence comfort levels around buildings. Traditionally, tunnel experiments, situ measurements, and fluid dynamics (CFD) simulations have employed assess speeds urban settings. However, advent machine learning (ML) introduced innovative methodologies that extend beyond these conventional approaches, offering new insights applications design. This study focuses on evaluating pedestrian-level using ML techniques, with a comparative against traditional measurements CFD simulations. Our findings reveal can predict sufficient accuracy for preliminary phases. One primary challenges addressed integration visual outputs from models quantitative data, necessary step enhance model reliability applicability. By developing novel techniques this integration, our research marks significant contribution field, benchmarking effectiveness established methods. The results validate model’s capability accurately estimate speeds, thereby supporting comfortable environments.

Язык: Английский

Процитировано

0
Progressive Prediction of Embodied Carbon Emissions Across Stages of Schematic Design with Machine Learning DOI

Lu Luo,

Yang Chen, Wei Feng

и другие.

Journal of Cleaner Production, Год журнала: 2025, Номер unknown, С. 145799 - 145799

Опубликована: Май 1, 2025

Язык: Английский

Процитировано

0
Deconstructing synergistic carbon reduction mechanisms in large-scale construction governance: A Monte Carlo simulation based on a cross-scale model DOI

Zhizhe Zheng,

Yikun Su,

Junhao Liu

и другие.

Ain Shams Engineering Journal, Год журнала: 2025, Номер 16(9), С. 103513 - 103513

Опубликована: Июнь 5, 2025

Язык: Английский

Процитировано

0