Embodied Carbon Minimization for Single-Story Steel Gable Frames

Salama, Abdallah; Atif, Assem; Eraky, Atef; El-Sisi, Alaa; Samir, Rania

doi:10.3390/buildings13030739

Cited by 6 publications

(3 citation statements)

References 38 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Limiting global warming to 1.5 degrees Celsius is one way to achieve this [2][3][4]. The Intergovernmental Panel on Climate Change (IPCC) estimated in its 2018 report that global greenhouse gas emissions will need to be net zero by 2050 to maintain a "high confidence" level that temperatures will remain below sustainable levels [5].…”

Section: Introductionmentioning

confidence: 99%

Prediction of an Efficient Energy-Consumption Model for Existing Residential Buildings in Lebanon Using an Artificial Neural Network as a Digital Twin in the Era of Climate Change

El-Gohary,

El-Abed,

Omar

2023

Buildings

View full text Add to dashboard Cite

Environmental factors, such as climate change, have serious consequences for existing buildings, including increased resource consumption and footprint, adverse health effects, and reduced comfort for the occupants. To promote sustainability and address climate change, architecture must embrace digitalization. Buildings can be built digitally, analyzed in real time, optimized for energy consumption, and utilized to reduce carbon emissions and achieve zero energy consumption using digital twin technology. Currently, Lebanon’s residents are turning to solar power to generate renewable energy as a result of a lack of energy supplied by the government. In this study, a digital twin model was designed using an artificial neural network (ANN) to investigate the energy consumption of residential buildings. The main idea was to assist architects and engineers in forecasting energy consumption for different design materials by selecting the most effective alternate design for materials with building envelope characteristics, such as exterior walls, roof insulation, and windows, to minimize the consumption of energy in a residential building, hence resulting in a green building. The data simulations used in the digital twin model were carried out using Quick Energy Simulation Tool (eQuest) software; 1540 simulation results were used for different thicknesses of insulation material, values of conductivity, and window types. The digital twins were designed using an artificial neural network model. The results of the investigation and the accompanying eQuest output results were found to be precise and very similar.

show abstract

Section: Introductionmentioning

confidence: 99%

Prediction of an Efficient Energy-Consumption Model for Existing Residential Buildings in Lebanon Using an Artificial Neural Network as a Digital Twin in the Era of Climate Change

El-Gohary,

El-Abed,

Omar

2023

Buildings

View full text Add to dashboard Cite

show abstract

“…The proposed decarbonization strategies fall into three major fields: selecting construction materials with a low carbon footprint, optimizing building designs that adhere to a "less is more" philosophy [5,6], and developing resilient long-life composite building components. As the "skeleton" of buildings, building structures exert considerable impact over the cumulative embodied carbon of a construction project, thereby prompting a global shift towards more sustainable structural configurations in recent years [7,8]. Decisions regarding frame materials, hybrid solutions, contextual considerations, and lifecycle assessments collectively contribute to the overarching embodied carbon outcomes.…”

Section: Introductionmentioning

confidence: 99%

Exploring Embodied Carbon Comparison in Lightweight Building Structure Frames: A Case Study

Huang,

Xing,

Rameezdeen

2023

Sustainability

View full text Add to dashboard Cite

Structural components represent major contributors to embodied carbon emissions of buildings. While there have been numerous research efforts dedicated to modelling and assessing the embodied carbon impact of buildings, there is a conspicuous gap in research that concurrently examines various material options in building structural designs, accounting for technical, economic, and carbon implications. In this study, an integrated approach is applied to assess the embodied carbon and life cycle cost impacts of three different building structures, i.e., timber-framed (TF), steel-framed (SF), and the timber–steel composite (TSCF) framed, scaffolded with Finite Element Analysis (FEA) simulations for a strength and stability analysis of different design options. A lightweight frame-structured residential building type is examined as the data source for the modelling and simulations. The results of a comparative scenario analysis highlight that both TF structures and TSCF structures have notable advantages over their SF counterparts for embodied carbon saving and building load reduction. Assessment results indicate that the TF design offers 35.56% embodied carbon reduction, followed by the TSCF design with 8.12% decarbonization, compared to the SF design. The lifecycle cost assessments also reveal the promising cost saving potential of TF and TSCF structures for the application, with cost savings of up to 7.93% and 4%, respectively. Meanwhile, the simulations further demonstrate that TSCF materials in particular can have significant benefits for lightweight building structures in overcoming the deflection problem of long TF components and the buckling of thin-walled SF members. The results help to identify the potential of TSCF structures to minimize the material use for a “Build with Less” through design optimization, which can lead to further embodied carbon and lifecycle cost reductions.

show abstract

“…With the constant consumption of energy and the emission of greenhouse gases, global climate change has become one of the most severe environmental challenges. The carbon dioxide (CO 2 ) emissions related to the construction industry have been a global concern [6][7][8][9][10]. Chau et al state that the building sector contributes 40% to the total energy consumption and 36% to the global CO 2 emissions [11].…”

Section: Introductionmentioning

confidence: 99%

Carbon Emission Evaluation Method and Comparison Study of Transformer Substations Using Different Data Sources

Liu

Zhang

et al. 2023

Buildings

View full text Add to dashboard Cite

The construction of transformer substations in transmission lines is a systematic, technical, and complex project with the need for numerous materials and resources. Under the development of the green economy, the requirements for energy conservation and carbon reduction have improved; hence, an assessment of carbon emissions in transformer substations is urgently needed. A calculation method was proposed in the present study to analyze the carbon emissions of transformer substations with different kinds of data sources, which were collected from several practical projects in the west-to-east power transmission project. In this study, a detailed comparison and discussion regarding the differences in carbon emissions of 750 kV transformer substations caused by hydrology, geology, engineering quantity, and other factors were conducted. The mean value, standard deviation, and 90% confidence interval of carbon emissions were obtained by Monte Carlo simulation through MATLAB. Results show that the total carbon emissions of the selected 750 kV transformer substations are between [56,000, 68,000] t CO2 eq. Construction engineering accounts for more than 50% of carbon emissions, followed by installation engineering and additional services. In terms of input items, electricity distribution buildings contribute more than 39% of total carbon emissions, followed by cable/earthing systems, which account for 14% of total carbon emissions. Gas insulated switchgear (GIS) and air insulated switchgear (AIS) could adopt different types of equipment foundations, and GIS equipment foundations would generate fewer carbon emissions due to the smaller land area and input materials. This study can provide experience and reference for similar projects and further guide the substation carbon emission reduction work.

show abstract

Embodied Carbon Minimization for Single-Story Steel Gable Frames

Cited by 6 publications

References 38 publications

Prediction of an Efficient Energy-Consumption Model for Existing Residential Buildings in Lebanon Using an Artificial Neural Network as a Digital Twin in the Era of Climate Change

Prediction of an Efficient Energy-Consumption Model for Existing Residential Buildings in Lebanon Using an Artificial Neural Network as a Digital Twin in the Era of Climate Change

Exploring Embodied Carbon Comparison in Lightweight Building Structure Frames: A Case Study

Carbon Emission Evaluation Method and Comparison Study of Transformer Substations Using Different Data Sources

Contact Info

Product

Resources

About