A system boundary-based critical review on crane selection in building construction

Wang, Roy Dong; Zayed, Tarek; Pan, Wei; Zheng, Saina; Tariq, Salman

doi:10.1016/j.autcon.2020.103520

Cited by 21 publications

(15 citation statements)

References 51 publications

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“…Construction simulation can also be used for visualizing the process and helps to resolve the issues before the actual construction process (Doloi, 2008). Simulation methods and tools are very useful for challenging problems and decision-making exercises, especially for complex systems (Al-Masalha, 2004;Tantisevi and Akinci, 2008;Jiradamkerng, 2016;Willmann et al, 2016;Li and Lu, 2019;Tan et al, 2021;Wang et al, 2021). Simulation is based on modelling real world scenarios for their comprehension, interpretation, evaluation and improvement (Pena, 2021).…”

Section: Bepam 125mentioning

confidence: 99%

A review of methodologies for performance evaluation of automated construction processes

Krishnamoorthi

Raphael

2021

BEPAM

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PurposeThe aim of this paper is to synthesize knowledge related to performance evaluation of automated construction processes during the planning and execution phases through a theme-based literature classification. The primary research question that is addressed is “How to quantify the performance improvement in automated construction processes?”Design/methodology/approachA systematic literature review of papers on automated construction was conducted involving three stages-planning, conducting and reporting. In the planning stage, the purpose of the review is established through key research questions. Then, a four-step process is employed consisting of identification, screening, shortlisting and inclusion of papers. For reporting, observations were critically analysed and categorized according to themes.FindingsThe primary conclusion from this study is that the effectiveness of construction processes can only be benchmarked using realistic simulations. Simulations help to pinpoint the root causes of success or failure of projects that are either already completed or under execution. In automated construction, there are many complex interactions between humans and machines; therefore, detailed simulation models are needed for accurate predictions. One key requirement for simulation is the calibration of the models using real data from construction sites.Research limitations/implicationsThis study is based on a review of 169 papers from a database of peer-reviewed journals, within a time span of 50 years.Originality/valueGap in research in the area of performance evaluation of automated construction is brought out. The importance of simulation models calibrated with on-site data within a methodology for performance evaluation is highlighted.

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Section: Bepam 125mentioning

confidence: 99%

A review of methodologies for performance evaluation of automated construction processes

Krishnamoorthi

Raphael

2021

BEPAM

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“…As an important lifting equipment indispensable in modern engineering and construction, crane plays an extremely important role in improving work efficiency 1 . More specifically, the practical application environment of crane is becoming increasingly complex and the application intensity continues to increase, thus the potential security risks are also increasing gradually.…”

Section: Introductionmentioning

confidence: 99%

“…As an important lifting equipment indispensable in modern engineering and construction, crane plays an extremely important role in improving work efficiency. 1 More specifically, the practical application environment of crane is becoming increasingly complex and the application intensity continues to increase, thus the potential security risks are also increasing gradually. Due to the increased difficulty of identification, important parts of the crane (e.g., balance beam, A-frame, tie-rod, fly-jib, and cargo boom) are prone to localized damage, which causes downtime of the entire large hoisting system.…”

Section: Introductionmentioning

confidence: 99%

Multichannel intelligent fault diagnosis of hoisting system using differential search algorithm‐variational mode decomposition and improved deep convolutional neural network

Lee

2022

Structural Contr & Hlth

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Nowadays, the feature extraction method of multichannel acoustic emission (AE) signal provides a solid research foundation for digital and intelligent fault diagnosis of the hoisting system. More specifically, AE signal collected from the hoisting system is generally characterized by nonlinear and non-stationary, thus making the traditional intelligent fault diagnosis methods cannot accurately extract the inherent fault features. To alleviate this problem and improve

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“…large working coverage, simple installation and dismantling and on-site construction convenience). 1 In engineering construction, the crane application environment is becoming increasingly complex and the application intensity continues to increase, thus the potential security risks are also increasing gradually. [2][3][4] Specifically, due to the increased difficulty of identification, important parts of the crane (e.g.…”

Section: Introductionmentioning

confidence: 99%

Acoustic emission and moving window-improved kernel entropy component analysis for structural condition monitoring of hoisting machinery under various working conditions

Yang

2021

Structural Health Monitoring

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Acoustic emission (AE) has been widely used to the nondestructive evaluation (NDE) and structural health monitoring (SHM) of hoisting machinery recently. Kernel entropy component analysis (KECA) is generally applied to extract the AE features based on its excellent nonlinear ability. However, traditional KECA specifically requires a considerable number of components (e.g. eigenvalues and eigenvectors) to excellently describe the original data, which leads to a reduction in the effect of approximate dimensionality reduction of high-dimensional data, thus causing readily unacceptable condition monitoring result. To overcome this weakness, a novel method named moving window-improved kernel entropy component analysis (MW-IKECA) is proposed in this study for structural condition monitoring of hoisting machinery, which is aimed at extracting more AE feature information and improving the condition identification accuracy. Firstly, a twiddle factor is introduced in the KECA model for the purpose of breaking the restriction that the projection axes originate only from the feature vectors and maximizing the independence between the components. Meanwhile, the moving window local strategy is incorporated into the proposed IKECA to extract more rich and effectiveness AE feature information at different scales. Finally, the Cauchy–Schwarz (CS) statistic is utilized to calculate the similarity between probability density functions and maintain the angular structure of the MW-IKECA feature space for the task of improving the monitoring accuracy and shortening the monitoring time-delay of MW-IKECA. Results of the experimental and practical engineering application validate the effectiveness and superiority of the proposed method in AE-based crane SHM under different working conditions compared with the traditional KECA and some combinatorial methods.

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A system boundary-based critical review on crane selection in building construction

Cited by 21 publications

References 51 publications

A review of methodologies for performance evaluation of automated construction processes

A review of methodologies for performance evaluation of automated construction processes

Multichannel intelligent fault diagnosis of hoisting system using differential search algorithm‐variational mode decomposition and improved deep convolutional neural network

Acoustic emission and moving window-improved kernel entropy component analysis for structural condition monitoring of hoisting machinery under various working conditions

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