Pipe Spool Fabrication Sequencing by Automated Planning

Hu, Di; Mohamed, Yasser

doi:10.1061/9780784412329.050

Cited by 8 publications

(4 citation statements)

References 9 publications

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“…Due to the complicated shapes of industrial assemblies and the limited capacity of transportation vehicles, successful delivery of materials on construction sites becomes a very challenging task. The incurred inaccuracies and defects are sometimes unpreventable due the challenging fabrication and transportation phases (Hu and Mohamed 2012).…”

Section: Problem Statementmentioning

confidence: 99%

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Automated Deviation Analysis for As-Built Status Assessment of Steel Assemblies and Pipe Spools

Nahangi¹,

Yeung²,

Amaral³

et al. 2014

Computing in Civil and Building Engineering (2014)

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Steel assemblies and pipe spools play an essential role in the industrial construction sector. Fabrication of steel assemblies has been a challenging task due to the limited fabrication precision of the tools used in the process and inadequate inspection during fabrication. Moreover, unfavorable deformations may occur during the transportation phase which makes the erection and installation phase more complicated. These deviations require further considerations for realignment and repair that are associated with rework on construction sites. Hence, a systematic and automatic framework is required to continuously monitor the fabrication and installation processes of steel assemblies. Current approaches lack a sufficient level of control and are prone to error. This paper presents an automated framework to detect defective parts in steel assemblies and pipe spools in particular. A laser-based point cloud, which represents the as-built status, is compared to the original state from the CAD drawings that exist in the Building Information Model (BIM). Therefore, the defective parts are detected in a timely manner. The comparison is distance based and the procedure is fully automated. The experiments conducted to validate the proposed approach show that the model has high precision and a high rate of recall and has the potential to be employed for automated damage detection in order to improve productivity on construction sites.

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Section: Problem Statementmentioning

confidence: 99%

“…Recently, research studies have been attempted to improve the quality of fabricated pipe spool assemblies using simulation (Hu and Mohamed 2012;. Having simulated the fabrication process, the users are able to identify the conflicting processes using different scenarios.…”

Section: Introductionmentioning

confidence: 99%

Automated Deviation Analysis for As-Built Status Assessment of Steel Assemblies and Pipe Spools

Nahangi¹,

Yeung²,

Amaral³

et al. 2014

Computing in Civil and Building Engineering (2014)

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“…pipe spool fabrication system). 19 . It is indicated that none of the AI planners are fully adequate to solve real-life pipe spool fabrication sequencing problems.…”

Section: Application Of Pddl To Pipe Spool Fabrication Sequencing Promentioning

confidence: 99%

“…Example domain and problem definition files for pipe spool fabrication (Di and Mohamed 2012)19 A number of experiments are conducted to test the capability of AI planners to solve the pipe spool fabrication sequencing problem. These experiments are designed to be of incremental complexity.…”

mentioning

confidence: 99%

Automating fabrication sequencing for industrial construction

Hu¹,

Mohamed²

2012

Gerontechnology

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Purpose Industrial construction projects are heavily dependent on pre-fabrication of piping components. Unlike traditional manufacturing, many pipe spools have a unique design and need to be custom fabricated due to the one-of-a-kind characteristic of each industrial project. This is reflected in the fact that fabrication sequences vary greatly among pipe spools. Planning these sequences has considerable impact on the fabrication performance. However, it is currently mostly done in the form of human manual input. Personal experience and judgment are the major grounds on which sequencing decisions are based. Given the enormous number of pipe spools and the fasttracking nature of industrial projects, the efficiency and quality of such decisions cannot be guaranteed. Automating this decision-making process has the potential for overall performance enhancement, but has not yet been sufficiently investigated. Method We explore two different problem solving techniques, mainly artificial intelligence (AI) planning and dynamic programming (DP). A number of experiments have been conducted to evaluate their effectiveness. Results & Discussion The results show that AI-planning-a sophisticate planning techniquehas difficulty parsing fabrication logic that is prerequisite for AI-planners to result in a solution. DP, on the other hand, shows greater flexibility in incorporating this logic and a higher efficiency of discovering the optimal sequence. Future research will be aimed at incorporating the DP-algorithm with a discrete event simulation model so that fabrication sequences can be dynamically generated and adjusted to address changing project conditions.

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