Simulation-based evaluation of fuel consumption in heavy construction projects by monitoring equipment idle times

Akhavian, Reza; Behzadan, Amir H.

doi:10.1109/wsc.2013.6721677

Cited by 13 publications

(8 citation statements)

References 13 publications

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“…The effects of idling times on fuel consumption during construction operations have been estimated by simulating discrete events and used to evaluate technical plans with respect to efficiency of resource utilization and emissions [37]. Information on the relationship between idling time and hourly fuel consumption can be applied across a fleet of construction machines in various ways, leading to a range of concepts for identifying optimal production cycles that minimize unit costs and emissions [11].…”

Section: Literature Reviewmentioning

confidence: 99%

“…The planner can also vary the swell factor to determine how modifying the load factor affects the actual haul capacity, production rate, and cycle time, and thus the haulers' queue times, which are computed during the third stage of the modeling process. Because the queue time strongly affects energy consumption and emissions (i.e., long queue times imply significant idle time; see queue time in Figures 1 and 2) [37], the selection of haulers might need to be modified. The final outputs of the modeling process include estimates of the hourly energy consumption and CO 2 emissions for individual haulers and the fleet as a whole, enabling meaningful evaluation of the environmental impact of the planned earthwork operations.…”

Section: Application Of the Proposed Model In A Case Studymentioning

confidence: 99%

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A Practical Method for Assessing the Energy Consumption and CO2 Emissions of Mass Haulers

Jassim¹,

Lu²,

Olofsson³

2016

Energies

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Mass hauling operations play central roles in construction projects. They typically use many haulers that consume large amounts of energy and emit significant quantities of CO 2. However, practical methods for estimating the energy consumption and CO 2 emissions of such operations during the project planning stage are scarce, while most of the previous methods focus on construction stage or after the construction stages which limited the practical adoption of reduction strategy in the early planning phase. This paper presents a detailed model for estimating the energy consumption and CO 2 emissions of mass haulers that integrates the mass hauling plan with a set of predictive equations. The mass hauling plan is generated using a planning program such as DynaRoad in conjunction with data on the productivity of selected haulers and the amount of material to be hauled during cutting, filling, borrowing, and disposal operations. This plan is then used as input for estimating the energy consumption and CO 2 emissions of the selected hauling fleet. The proposed model will help planners to assess the energy and environmental performance of mass hauling plans, and to select hauler and fleet configurations that will minimize these quantities. The model was applied in a case study, demonstrating that it can reliably predict energy consumption, CO 2 emissions, and hauler productivity as functions of the hauling distance for individual haulers and entire hauling fleets.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Application Of the Proposed Model In A Case Studymentioning

confidence: 99%

A Practical Method for Assessing the Energy Consumption and CO2 Emissions of Mass Haulers

Jassim¹,

Lu²,

Olofsson³

2016

Energies

View full text Add to dashboard Cite

show abstract

“…The planner can also vary the swell factor to determine how modifying the load factor affects the actual haul capacity, production rate, and cycle time, and thus the haulers' queue times, which are computed during the third stage of the modeling process. Because the queue time strongly affects energy consumption and emissions (i.e., long queue times imply significant idle time; see queue time at Figures 1 and 2) [37], the selection of haulers might need to be modified. The final outputs of the modeling process include estimates of the hourly energy consumption and CO2 emissions for individual haulers and the fleet as a whole, enabling meaningful evaluation of the environmental impact of the planned earthwork operations.…”

Section: Application Of the Proposed Model In A Case Studymentioning

confidence: 99%

A Practical Method for Assessing the Energy Consumption & CO<sub>2</sub> Emissions of Mass Haulers

Jassim

Olofsson

2016

Preprint

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Mass hauling operations play central roles in construction projects. They typically use many haulers that consume large amounts of energy and emit significant quantities of CO2. However, practical methods for estimating the energy consumption and CO2 emissions of such operations during project planning are lacking. This paper presents a detailed model for estimating the energy consumption and CO2 emissions of mass haulers that integrates the mass hauling plan with a set of predictive equations. The mass hauling plan is generated using a planning program such as DynaRoad in conjunction with data on the productivity of selected haulers and the amount of material to be hauled during cutting, filling, borrowing, and disposal operations. This plan is then used as input for estimating the energy consumption and CO2 emissions of the selected hauling fleet. The proposed model will help planners to assess the energy and environmental performance of mass hauling plans, and to select hauler and fleet configurations that will minimize these quantities. The model was applied in a case study, demonstrating that it can reliably predict energy consumption, CO2 emissions, and hauler productivity as functions of the hauling distance for individual haulers and entire hauling fleets.

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“…The advancement of technology in miniaturized electronics and powerful cloudbased software has enabled successful applications of sensor-based Internet-of-Thing (IoT) platforms in construction jobsites [3,4]. Some of the recent applications of such IoT systems enabled quantifying workers body motions using wearable sensors.…”

Section: Introductionmentioning

confidence: 99%

Automated Workers’ Ergonomic Risk Assessment in Manual Material Handling Using sEMG Wearable Sensors and Machine Learning

et al. 2021

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Manual material handling tasks have the potential to be highly unsafe from an ergonomic viewpoint. Safety inspections to monitor body postures can help mitigate ergonomic risks of material handling. However, the real effect of awkward muscle movements, strains, and excessive forces that may result in an injury may not be identified by external cues. This paper evaluates the ability of surface electromyogram (EMG)-based systems together with machine learning algorithms to automatically detect body movements that may harm muscles in material handling. The analysis utilized a lifting equation developed by the U.S. National Institute for Occupational Safety and Health (NIOSH). This equation determines a Recommended Weight Limit, which suggests the maximum acceptable weight that a healthy worker can lift and carry, as well as a Lifting Index value to assess the risk extent. Four different machine learning models, namely Decision Tree, Support Vector Machine, K-Nearest Neighbor, and Random Forest are developed to classify the risk assessments calculated based on the NIOSH lifting equation. The sensitivity of the models to various parameters is also evaluated to find the best performance using each algorithm. Results indicate that Decision Tree models have the potential to predict the risk level with close to 99.35% accuracy.

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Simulation-based evaluation of fuel consumption in heavy construction projects by monitoring equipment idle times

Cited by 13 publications

References 13 publications

A Practical Method for Assessing the Energy Consumption and CO2 Emissions of Mass Haulers

A Practical Method for Assessing the Energy Consumption and CO2 Emissions of Mass Haulers

A Practical Method for Assessing the Energy Consumption & CO<sub>2</sub> Emissions of Mass Haulers

Automated Workers’ Ergonomic Risk Assessment in Manual Material Handling Using sEMG Wearable Sensors and Machine Learning

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Simulation-based evaluation of fuel consumption in heavy construction projects by monitoring equipment idle times

Cited by 13 publications

References 13 publications

A Practical Method for Assessing the Energy Consumption and CO2 Emissions of Mass Haulers

A Practical Method for Assessing the Energy Consumption and CO2 Emissions of Mass Haulers

A Practical Method for Assessing the Energy Consumption &amp; CO<sub>2</sub> Emissions of Mass Haulers

Automated Workers’ Ergonomic Risk Assessment in Manual Material Handling Using sEMG Wearable Sensors and Machine Learning

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Product

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A Practical Method for Assessing the Energy Consumption & CO<sub>2</sub> Emissions of Mass Haulers