Building life-span prediction for life cycle assessment and life cycle cost using machine learning: A big data approach

2021 ◽  
pp. 108267
Author(s):  
Sukwon Ji ◽  
Bumho Lee ◽  
Mun Yong Yi
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Life Cycle Assessment ◽  
Life Cycle ◽  
Life Span ◽  
Life Cycle Cost

Balancing of life cycle carbon and cost appraisal on alternative wall and roof design verification for residential building

2018 ◽  
Vol 18 (3) ◽  
pp. 274-300 ◽  
Author(s):  
Ali Tighnavard Balasbaneh ◽  
Abdul Kadir Bin Marsono ◽  
Emad Kasra Kermanshahi
Keyword(s):  
Life Cycle Assessment ◽  
Life Cycle ◽  
Life Span ◽  
Life Cycle Cost ◽  
Ghg Emissions ◽  
Residential Building ◽  
Life Cycle Management ◽  
Present Value ◽  
Sustainable Building ◽  
Content Type

Purpose The purpose of this study is to describe life cycle cost (LCC) and life cycle assessment (LCA) evaluation for single story building house in Malaysia. Two objective functions, namely, LCA and LCC, were evaluated for each design and a total of 20 alternatives were analyzed. Two wall schemes that have been adopted from two different recent studies toward mitigation of climate change require clarification in both life cycle objectives. Design/methodology/approach For this strategic life cycle assessment, Simapro 8.3 tool has been chosen over a 50-year life span. LCC analysis was also used to determine not only the most energy-efficient strategy, but also the most economically feasible one. A present value (PV)-based economic analysis takes LCC into account. Findings The results will appear in present value and LC carbon footprint saving, both individually and in combination with each other. Result of life cycle management shows that timber wall−wooden post and beam covered by steel stud (W5) and wood truss with concrete roof tiles (R1) released less carbon emission to atmosphere and have lower life cycle cost over their life span. W5R1 releases 35 per cent less CO2 emission than the second best choice and costs 25 per cent less. Originality/value The indicator assessed was global warming, and as the focus was on GHG emissions, the focus of this study was mainly in the context of Malaysian construction, although the principles apply universally. The result would support the adoption of sustainable building for building sector.

scholarly journals Life Cycle Assessment and Life Cycle Cost Analysis of Magnesia Spinel Brick Production

2016 ◽  
Vol 8 (7) ◽  
pp. 662 ◽  
Author(s):  
Aysun Özkan ◽  
Zerrin Günkaya ◽  
Gülden Tok ◽  
Levent Karacasulu ◽  
Melike Metesoy ◽  
...  
Keyword(s):  
Life Cycle Assessment ◽  
Life Cycle ◽  
Cost Analysis ◽  
Life Cycle Cost ◽  
Life Cycle Cost Analysis ◽  
Magnesia Spinel

Machine learning in life cycle assessment

2021 ◽  
pp. 167-190
Author(s):  
Mikaela Algren ◽  
Wendy Fisher ◽  
Amy E. Landis
Keyword(s):  
Machine Learning ◽  
Life Cycle Assessment ◽  
Life Cycle

Environmental Life-Cycle Assessment and Life-Cycle Cost Analysis of a High-Rise Mass Timber Building: A Case Study in Pacific Northwestern United States

2021 ◽  
Vol 13 (14) ◽  
pp. 7831
Author(s):  
Shaobo Liang ◽  
Hongmei Gu ◽  
Richard Bergman
Keyword(s):  
Life Cycle Assessment ◽  
Life Cycle ◽  
Cost Analysis ◽  
Life Cycle Cost ◽  
Impact Category ◽  
Life Cycle Cost Analysis ◽  
Floor Area ◽  
High Rise ◽  
Concrete Building ◽  
Mass Timber

Global construction industry has a huge influence on world primary energy consumption, spending, and greenhouse gas (GHGs) emissions. To better understand these factors for mass timber construction, this work quantified the life cycle environmental and economic performances of a high-rise mass timber building in U.S. Pacific Northwest region through the use of life-cycle assessment (LCA) and life-cycle cost analysis (LCCA). Using the TRACI impact category method, the cradle-to-grave LCA results showed better environmental performances for the mass timber building relative to conventional concrete building, with 3153 kg CO2-eq per m2 floor area compared to 3203 CO2-eq per m2 floor area, respectively. Over 90% of GHGs emissions occur at the operational stage with a 60-year study period. The end-of-life recycling of mass timber could provide carbon offset of 364 kg CO2-eq per m2 floor that lowers the GHG emissions of the mass timber building to a total 12% lower GHGs emissions than concrete building. The LCCA results showed that mass timber building had total life cycle cost of $3976 per m2 floor area that was 9.6% higher than concrete building, driven mainly by upfront construction costs related to the mass timber material. Uncertainty analysis of mass timber product pricing provided a pathway for builders to make mass timber buildings cost competitive. The integration of LCA and LCCA on mass timber building study can contribute more information to the decision makers such as building developers and policymakers.

scholarly journals Decision to paving solutions in road infrastructures based on life-cycle assessment

2016 ◽  
Vol 11 (1) ◽  
pp. 43-52 ◽  
Author(s):  
Maria de Lurdes Antunes ◽  
Vânia Marecos ◽  
José Neves ◽  
João Morgado
Keyword(s):  
Life Cycle Assessment ◽  
Life Cycle ◽  
Decision Process ◽  
Life Cycle Cost ◽  
Road Network ◽  
Construction Costs ◽  
Road Pavement ◽  
Pavement Structures ◽  
Using Data ◽  
Term Performance

The construction and maintenance of a road network involve the expenditure of large budgets. In order to optimize the investments in road infrastructures, designers and decision makers should have the instruments to make the most suitable decision of paving solutions for each particular situation. The life-cycle assessment is an important tool of different road pavement solutions with this purpose. This paper presents a study concerning the life-cycle cost analysis of different flexible and semi-rigid paving alternatives, with the objective to contribute for a better support in the decision process when designing new pavement structures. The analysis was carried out using data on construction costs of certain typical pavement structures and taking into consideration appropriate performance models for each type of structure being selected. The models were calibrated using results from long term performance studies across Europe and the maintenance strategies considered have taken into account the current practice also found in the European context. Besides the life-cycle administration costs, the proposed methodology also deals with user and environmental costs through its inclusion in the decision process using multi-criteria analysis. It was demonstrated that this methodology could be a simple and useful tool in order to achieve the most adequate paving solutions of a road network, in terms of construction and maintenance activities, based simultaneously on technical, economic and environmental criteria.

scholarly journals Application of Life Cycle Assessment and Machine Learning for High-Throughput Screening of Green Chemical Substitutes

2020 ◽  
Author(s):  
Xinzhe Zhu ◽  
Chi-Hung Ho ◽  
Xiaonan Wang
Keyword(s):  
Machine Learning ◽  
Life Cycle Assessment ◽  
Life Cycle ◽  
Production Process ◽  
Environmental Impacts ◽  
High Throughput ◽  
High Throughput Screening ◽  
Molecular Descriptors ◽  
Trifluoroacetic Anhydride ◽  
Chemical Materials

<p><a></a><a>The production process of many active pharmaceutical ingredients such as sitagliptin could cause severe environmental problems due to the use of toxic chemical materials and production infrastructure, energy consumption and wastes treatment. The environmental impacts of sitagliptin production process were estimated with life cycle assessment (LCA) method, which suggested that the use of chemical materials provided the major environmental impacts. Both methods of Eco-indicator 99 and ReCiPe endpoints confirmed that chemical feedstock accounted 83% and 70% of life-cycle impact, respectively. Among all the chemical materials used in the sitagliptin production process, </a><a>trifluoroacetic anhydride </a>was identified as the largest influential factor in most impact categories according to the results of ReCiPe midpoints method. Therefore, high-throughput screening was performed to seek for green chemical substitutes to replace the target chemical (i.e. trifluoroacetic anhydride) by the following three steps. Firstly, thirty most similar chemicals were obtained from two million candidate alternatives in PubChem database based on their molecular descriptors. Thereafter, deep learning neural network models were developed to predict life-cycle impact according to the chemicals in Ecoinvent v3.5 database with known LCA values and corresponding molecular descriptors. Finally, 1,2-ethanediyl ester was proved to be one of the potential greener substitutes after the LCA data of these similar chemicals were predicted using the well-trained machine learning models. The case study demonstrated the applicability of the novel framework to screen green chemical substitutes and optimize the pharmaceutical manufacturing process.</p>

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