Analysis of interrelationships among excavator productivity modifying factors

Holt, Gary David; Edwards, David John

doi:10.1108/ijppm-02-2014-0026

Cited by 26 publications

(4 citation statements)

References 29 publications

(34 reference statements)

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“…Because of the lack of real data, a computer simulation is utilized to generate data. In [13], the operator competence is presented as a modifying factor in the productivity estimation. The authors model the operator competence and then analyze the effects of this variable on the productivity estimation.…”

Section: Literature Reviewmentioning

confidence: 99%

An Approach for Estimation of Swing Angle and Digging Depth During Excavation Operation

Molaei¹,

Geimer²,

Kolum³

2022

Proceedings of the International Symposium on Automation and Robotics in Construction (IAARC)

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This paper discusses the estimation of the swing angle and digging depth during the excavation operation. The ability to calculate the excavator's productivity is an essential step toward autonomous excavators. The swing angle and digging depth have significant effects on the excavator's productivity and must be taken into account for the productivity estimation. Two approaches are proposed to estimate these variables. The first method estimates the swing angle using cabin encoder measurements. The local minimum and maximum points are found, and then Otsu's method is exploited to detect the points that are representative of scooping and dumping positions. The second method utilizes the bucket position to estimate the digging depth. The bucket position is calculated using Inertial Measurement Units (IMUs) measurements and the forward kinematics of the excavator. Otsu's method is used to distinguish the local minimum points that are representative of the digging depth during the operation. Moreover, the algorithms are computationally efficient. Finally, the performance of the proposed methods is studied using real measurements. The results show that the methods can effectively estimate the swing angle and digging depth under different working c onditions s uch a s various materials, swing angles, and digging depths.

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Section: Literature Reviewmentioning

confidence: 99%

An Approach for Estimation of Swing Angle and Digging Depth During Excavation Operation

Molaei¹,

Geimer²,

Kolum³

2022

Proceedings of the International Symposium on Automation and Robotics in Construction (IAARC)

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“…Operator's competence embraces not only aspects of productivity but also H&S aspects. It is acknowledged that operator competence and operator motivation are two entirely different concepts since a very competent operator can also be demotivated or simply idle; consequently, Holt and Edwards (2015) identified the superlative role of operator competence in relation to other productivity variables. Concerning H&S aspects, training is widely considered to be one of the best approaches to accident prevention.…”

Section: Operator's Competence / Health and Safety (Hands)mentioning

confidence: 99%

A Thematic Review of the Main Research on Construction Equipment over Recent Years

Naskoudakis

Petroutsatou

2017

Period. Polytech. Arch.

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A considerable body of literature has been dedicated to research studies on construction equipment. Many topics were discussed and analysed, and various conclusions have been reported. However, research papers published regarding construction equipment, are highly diversified, and there is a lack of systematic analysis and classification. Hence, a complete understanding of the topic is not possible, nor is the assessment of any future research direction. A meta-analysis of the latest journal papers dedicated to construction machinery would not only delineate the fields the academic research was concentrated on but would additionally reveal potential gaps for future research. In the current study, through a systematic review of the academic literature published over the last decade, primarily identified via online databases, main research themes such as optimisation, maintenance/downtime, productivity, robotics and automation, operator competence, innovation, and environment are determined and discussed, with future research directions suggested. The outcome of this paper will facilitate future researchers to develop a body of knowledge of progress on construction equipment and its potential functions and provide future research directions on this issue. Furthermore, some pointers will be provided regarding the optimum selection of fleet equipment as a key factor for the success of any construction project. These will be given as part of the necessary holistic and strategic approach required to deliver a construction project successfully.

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“…Automatic construction vehicles need to make operational adjustments based on real‐time information feedback when operating unmanned. Productivity is an important indicator of earthwork (Hola & Schabowicz, 2010; Holt & Edwards, 2015). Many factors affect productivity, and much work has been done to estimate and increase productivity (Bae et al., 2019; Montaser et al., 2012).…”

Section: Introductionmentioning

confidence: 99%

A deep learning approach for construction vehicles fill factor estimation and bucket detection in extreme environments

Guan

Chen

Wang

et al. 2022

Computer aided Civil Eng

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The development of autonomous detection technology is imperative in the field of construction. The bucket fill factor is one of the main indicators for evaluating the productivity of construction vehicles. Bucket detection is a prerequisite for bucket trajectory planning. However, previous studies have been conducted under ideal environments, a specific single environment, and several normal environments without considering the actual harsh environments at construction sites. Therefore, seven extreme environments are set in this paper to fill this gap, and an effective method is proposed. First, a novel framework for image restoration under extreme environments is proposed. It applies to all tasks conducted by vision on construction sites. Second, a combination of segmentation and classification networks is used for the first time in this area. Multitask learning is used to discover a positive correlation between fill factor estimation and bucket detection. Furthermore, probabilistic methods and transfer learning were introduced, and excellent results were achieved (97.40% accuracy in fill factor estimation and 99.76% accuracy in bucket detection for seven extreme environments).

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Analysis of interrelationships among excavator productivity modifying factors

Cited by 26 publications

References 29 publications

An Approach for Estimation of Swing Angle and Digging Depth During Excavation Operation

An Approach for Estimation of Swing Angle and Digging Depth During Excavation Operation

A Thematic Review of the Main Research on Construction Equipment over Recent Years

A deep learning approach for construction vehicles fill factor estimation and bucket detection in extreme environments

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