4.7 Article

Life cycle energy consumption and GHG emission from pavement rehabilitation with different rolling resistance

期刊

JOURNAL OF CLEANER PRODUCTION
卷 33, 期 -, 页码 86-96

出版社

ELSEVIER SCI LTD
DOI: 10.1016/j.jclepro.2012.05.001

关键词

Pavement; Rehabilitation; Rolling resistance; Life cycle assessment; Energy; Greenhouse gas

资金

  1. California Department of Transportation, Division of Research and Innovation
  2. University of California Institute of Transportation Studies, Multi-campus Research Programs Initiatives

向作者/读者索取更多资源

This paper describes a pavement life cycle assessment (LCA) model developed to evaluate energy use and greenhouse gas (GHG) emissions from pavement rehabilitation strategies. The LCA model analyzes the energy and GHG emissions associated with material production, construction and pavement use, which includes the effects of pavement rolling resistance on vehicle operation. The model was used to evaluate a set of case studies of pavement rehabilitation for both asphalt and concrete surfaces with different rolling resistances and traffic levels. The primary goal of the case studies is to evaluate the effect of rolling resistance on the life cycle performance of pavements, not to compare asphalt and concrete pavements. Energy and GHG emission savings from pavement rehabilitation are compared with an alternative where no rehabilitation occurs, only routine maintenance of damaged pavement. The results of the case studies show that for highway sections with high traffic volumes the energy and GHG savings accrued during the use phase due to reduced rolling resistance can be significantly larger than the energy use and GHG emissions from material production and construction, with the extent of the benefit dependent on constructed smoothness. These savings can be larger than those from other strategies to reduce highway transportation energy use and emissions, such as projected improvements in vehicle fuel economy. For low traffic volume highways, the smoothness obtained by the contractor and materials used have a more significant effect on the performance of the rehabilitation, and may result in a net increase in energy use and GHG emissions if low traffic volumes and poor construction quality occur together. (C) 2012 Elsevier Ltd. All rights reserved.

作者

我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。

评论

主要评分

4.7
评分不足

次要评分

新颖性
-
重要性
-
科学严谨性
-
评价这篇论文

推荐

Article Construction & Building Technology

Life Cycle Sustainability Assessment of Fugitive Dust Control Methods

Alena J. Raymond, Alissa Kendall, Jason T. DeJong, Edward Kavazanjian, Miriam A. Woolley, Kimberly K. Martin

Summary: Fugitive dust at construction sites can affect air quality and respiratory health, prompting contractors to use dust control strategies to comply with environmental regulations. Water application is the most common mitigation strategy, but alternatives like magnesium chloride are also utilized. Despite reducing dust emissions, these strategies have other environmental impacts throughout their life cycle.

JOURNAL OF CONSTRUCTION ENGINEERING AND MANAGEMENT (2021)

Article Engineering, Environmental

Circularity of Lithium-Ion Battery Materials in Electric Vehicles

Jessica Dunn, Margaret Slattery, Alissa Kendall, Hanjiro Ambrose, Shuhan Shen

Summary: Batteries have the potential to reduce greenhouse gas emissions from on-road transportation significantly, but there are concerns over material production and criticality. Circular economy strategies can mitigate impacts and secure regional supplies, requiring the development of recycling and manufacturing infrastructure in each region.

ENVIRONMENTAL SCIENCE & TECHNOLOGY (2021)

Article Engineering, Environmental

A scalable and spatiotemporally resolved agricultural life cycle assessment of California almonds

Elias Marvinney, Alissa Kendall

Summary: This study presents a comprehensive life cycle assessment of almond production in California, revealing significant variability in environmental impacts across regions and over time, highlighting the need for spatially and temporally resolved agricultural LCAs.

INTERNATIONAL JOURNAL OF LIFE CYCLE ASSESSMENT (2021)

Article Energy & Fuels

AI and Text-Mining Applications for Analyzing Contractor's Risk in Invitation to Bid (ITB) and Contracts for Engineering Procurement and Construction (EPC) Projects

Su Jin Choi, So Won Choi, Jong Hyun Kim, Eul-Bum Lee

Summary: This study developed two core modules for a digital EPC contract risk analysis tool to help contractors identify and manage risk provisions. By automatically extracting risk-involved clauses and building a machine learning model, the accuracy rates of risk clause extraction were improved.

ENERGIES (2021)

Article Agriculture, Dairy & Animal Science

Grass-fed vs. grain-fed beef systems: performance, economic, and environmental trade-offs

Sarah C. Klopatek, Elias Marvinney, Toni Duarte, Alissa Kendall, Xiang (Crystal) Yang, James W. Oltjen

Summary: This study investigated the performance, carcass quality, financial outcomes, and environmental impacts of four grass-fed and grain-fed beef systems in California. The results showed that these systems differ in terms of animal performance, carcass quality, and environmental effects, with no system having absolute superiority.

JOURNAL OF ANIMAL SCIENCE (2022)

Review Engineering, Environmental

Literature review on policies to mitigate GHG emissions for cement and concrete

Pablo Busch, Alissa Kendall, Colin W. Murphy, Sabbie A. Miller

Summary: This study reviews previous research on cement and concrete decarbonization and analyzes the most common proposed measures along with their level of action, stakeholders involved, barriers to implementation, and policy actions. The primary technical measures identified for decarbonization include improved energy efficiency, fuel switching, carbon capture utilization and storage, and reduction of the clinker-to-cement ratio for cement production, and alternative binders, material and construction efficiency, and CO2 uptake by concrete for concrete production and end-uses. However, there is less clarity about preferred policy solutions and key barriers, indicating a need for further research.

RESOURCES CONSERVATION AND RECYCLING (2022)

Article Green & Sustainable Science & Technology

An AI-Based Automatic Risks Detection Solution for Plant Owner's Technical Requirements in Equipment Purchase Order

Chae-Yeon Kim, Jong-Gwan Jeong, So-Won Choi, Eul-Bum Lee

Summary: This study proposed a purchase order recognition and analysis system (PORAS) that utilizes artificial intelligence (AI) to automatically detect and compare risk clauses in purchase orders (POs) between plant owners and suppliers. The system significantly reduces the owner engineer's review time of risk clauses, improving work efficiency in the plant industry.

SUSTAINABILITY (2022)

Article Green & Sustainable Science & Technology

Contractor's Risk Analysis of Engineering Procurement and Construction (EPC) Contracts Using Ontological Semantic Model and Bi-Long Short-Term Memory (LSTM) Technology

So-Won Choi, Eul-Bum Lee

Summary: This study aims to analyze critical risk clauses in ITB documents to enhance the competitiveness of EPC contractors. Two models, rule-based and train-based, were developed and suggested to be used together for ITB analysis.

SUSTAINABILITY (2022)

Article Engineering, Environmental

Electric vehicle lithium-ion battery recycled content standards for the US - targets, costs, and environmental impacts

Jessica Dunn, Alissa Kendall, Margaret Slattery

Summary: Lithium-ion battery recycling is crucial for reducing the environmental impact of electric vehicles and securing domestic supply chains. However, the US lacks policies for battery recycling. The European Union has proposed recycled content standards to promote a circular battery ecosystem. This analysis suggests that a significant portion of cobalt, lithium, and nickel demand in the future could be met through retired supply, but domestic recycling is more expensive than exporting to China. Policy measures may be necessary to ensure the domestic recycling of critical materials.

RESOURCES CONSERVATION AND RECYCLING (2022)

Article Energy & Fuels

A Prediction Model for Spot LNG Prices Based on Machine Learning Algorithms to Reduce Fluctuation Risks in Purchasing Prices

Sun-Feel Yang, So-Won Choi, Eul-Bum Lee

Summary: This study used machine learning to predict the spot LNG price index (JKM) and reduce price fluctuation risks for LNG importers. The LSTM model performed the best, but its performance decreased during the COVID-19 period. The ML models' performance can be improved through additional studies.

ENERGIES (2023)

Article Green & Sustainable Science & Technology

Knowledge Retrieval Model Based on a Graph Database for Semantic Search in Equipment Purchase Order Specifications for Steel Plants

Ho-Jin Cha, So-Won Choi, Eul-Bum Lee, Duk-Man Lee

Summary: The complexity and age of industrial plants have led to an increased need for equipment maintenance and replacement. To address the challenge of reducing the process and review time of equipment purchase order (PO) documents, a purchase order knowledge retrieval model (POKREM) was developed. POKREM utilizes knowledge graph (KG) technology and a hierarchical structure to create a graph database for accurate and efficient document search. The implementation of POKREM resulted in a significant reduction in PO document review time and improved work efficiency for engineers.

SUSTAINABILITY (2023)

Article Green & Sustainable Science & Technology

Machine Learning-Based Tap Temperature Prediction and Control for Optimized Power Consumption in Stainless Electric Arc Furnaces (EAF) of Steel Plants

So-Won Choi, Bo-Guk Seo, Eul-Bum Lee

Summary: This study aims to improve the efficiency of the electric arc furnace (EAF) process by predicting tap temperature in real time and automatically setting the input power. A tap temperature prediction model (TTPM) was developed using a machine learning algorithm, resulting in reduced temperature deviation and energy consumption. Economic evaluation showed good feasibility, and the reliability of the system was verified through ten months of successful operation.

SUSTAINABILITY (2023)

Article Computer Science, Information Systems

A Question-Answering Model Based on Knowledge Graphs for the General Provisions of Equipment Purchase Orders for Steel Plants Maintenance

Sang-Hyuk Lee, So-Won Choi, Eul-Bum Lee

Summary: Recently, there has been an increase in equipment replacement and maintenance repair and operation (MRO) optimization in Korean industrial plants, particularly steel-making factories, due to aging and deterioration. To address this, plant owners need to review equipment supply contracts (purchase order documents) with suppliers and vendors promptly. However, the efficiency and quality of the review process vary among engineers due to differences in manual skills and experience. This study developed a general provisions question-answering model (GPQAM) that combines knowledge graph (KG) and question-answering (QA) techniques to facilitate the search for semantically connected contract clauses during equipment purchase contract reviews.

ELECTRONICS (2023)

Article Green & Sustainable Science & Technology

Modeling of Predictive Maintenance Systems for Laser-Welders in Continuous Galvanizing Lines Based on Machine Learning with Welder Control Data

Jin-Seong Choi, So-Won Choi, Eul-Bum Lee

Summary: This study aimed to develop a predictive maintenance model for detecting equipment failures in laser welders in a steel plant. The model combined an auto-encoder (AE) and a long short-term memory (LSTM) model to achieve high accuracy. The LW-PMM achieved an accuracy rate of 97.3% and a precision rate of 79.8%.

SUSTAINABILITY (2023)

Article Green & Sustainable Science & Technology

Prioritizing greenhouse gas mitigation strategies for local governments using marginal abatement cost

Mark T. Lozano, Alissa Kendall, Ali A. Butt, Arash Saboori, John Harvey, Changmo Kim

Summary: Cities and subnational jurisdictions play a crucial role in mitigating global climate change through climate action plans (CAPs). A study in California found that many CAPs lack quantitative information on the cost and emissions reductions of proposed strategies, leading to a framework developed for comparing strategies based on life cycle emissions and costs. The framework was piloted with two California counties, revealing strategies with cost savings and some that increased emissions. Future work should explore integrating life cycle and annual accounting methods for more effective decision-making.

ENVIRONMENTAL RESEARCH: INFRASTRUCTURE AND SUSTAINABILITY (2021)

Article Green & Sustainable Science & Technology

Relative evaluation of probabilistic methods for spatio-temporal wind forecasting

Lars odegaard Bentsen, Narada Dilp Warakagoda, Roy Stenbro, Paal Engelstad

Summary: This study investigates uncertainty modeling in wind power forecasting using different parametric and non-parametric methods. Johnson's SU distribution is found to outperform Gaussian distributions in predicting wind power. This research contributes to the literature by introducing Johnson's SU distribution as a candidate for probabilistic wind forecasting.

JOURNAL OF CLEANER PRODUCTION (2024)

Article Green & Sustainable Science & Technology

Comparison of ethane recovery processes for lean gas based on a coupled model

Xing Liu, Qiuchen Wang, Yunhao Wen, Long Li, Xinfang Zhang, Yi Wang

Summary: This study analyzes the characteristics of process parameters in three lean gas ethane recovery processes and establishes a prediction and multiobjective optimization model for ethane recovery and system energy consumption. A new method for comparing ethane recovery processes for lean gas is proposed, and the addition of extra coolers improves the ethane recovery. The support vector regression model based on grey wolf optimization demonstrates the highest prediction accuracy, and the multiobjective multiverse optimization algorithm shows the best optimization performance and diversity in the solutions.

JOURNAL OF CLEANER PRODUCTION (2024)

Article Green & Sustainable Science & Technology

A novel deep-learning framework for short-term prediction of cooling load in public buildings

Cairong Song, Haidong Yang, Xian-Bing Meng, Pan Yang, Jianyang Cai, Hao Bao, Kangkang Xu

Summary: The paper proposes a novel deep learning-based prediction framework, aTCN-LSTM, for accurate cooling load predictions. The framework utilizes a gate-controlled multi-head temporal convolutional network and a sparse probabilistic self-attention mechanism with a bidirectional long short-term memory network to capture both temporal and long-term dependencies in the cooling load sequences. Experimental results demonstrate the effectiveness and superiority of the proposed method, which can serve as an effective guide for HVAC chiller scheduling and demand management initiatives.

JOURNAL OF CLEANER PRODUCTION (2024)

Article Green & Sustainable Science & Technology

The impact of social interaction and information acquisition on the adoption of soil and water conservation technology by farmers: Evidence from the Loess Plateau, China

Zhe Chen, Xiaojing Li, Xianli Xia, Jizhou Zhang

Summary: This study uses survey data from the Loess Plateau in China to evaluate the impact of social interaction on the adoption of soil and water conservation (SWC) technology by farmers. The study finds that social interaction increases the likelihood of farmers adopting SWC, and internet use moderates this effect. The positive impact of social interaction on SWC adoption is more pronounced for farmers in larger villages and those who join cooperative societies.

JOURNAL OF CLEANER PRODUCTION (2024)

Article Green & Sustainable Science & Technology

Study on synergistic heat transfer enhancement and adaptive control behavior of baffle under sudden change of inlet velocity in a micro combustor

Chenghua Zhang, Yunfei Yan, Kaiming Shen, Zongguo Xue, Jingxiang You, Yonghong Wu, Ziqiang He

Summary: This paper reports a novel method that significantly improves combustion performance, including heat transfer enhancement under steady-state conditions and adaptive stable flame regulation under velocity sudden increase.

JOURNAL OF CLEANER PRODUCTION (2024)