scholarly journals BIM-Based Construction Progress Measurement of Non-Repetitive HVAC Installation Works

2019 ◽  
Author(s):  
Patrick Dallasega ◽  
Andrea Revolti ◽  
Camilla Follini ◽  
Christoph Paul Schimanski ◽  
Dominik Tobias Matt
Keyword(s):  
Progress Measurement

scholarly journals Applied Research on Software Progress Measurement: Taking B2B Project of A Company as an Example

2020 ◽  
Vol 2 (1) ◽  
pp. 4
Author(s):  
Shuangxi Zhang ◽  
Norriza Hussin
Keyword(s):  
Measurement Method ◽  
Applied Research ◽  
Cost Data ◽  
Value Management ◽  
Practical Case ◽  
Earned Value ◽  
Earned Value Management ◽  
A Company ◽  
Progress Measurement

<p>Because of the limitations of Earned Value Management (EVM), there are great defects in managing software progress. Although Earned Schedule (ES) improves EVM, it is not reliable to utilize cost data to measure software progress. In 2014, Earned Duration Management (EDM), which is a new measurement method, was introduced. In this paper, via a practical case, the EDM method is used to measure the software progress.</p>

Extended Earned Value Management Based on Fuzzy Multi-Criteria Decision Making

2016 ◽  
pp. 2273-2289
Author(s):  
Hêriş Golpîra
Keyword(s):  
Success Factors ◽  
Uncertain Data ◽  
Good Alternative ◽  
Value Management ◽  
Earned Value ◽  
Analytic Hierarchy ◽  
Earned Value Management ◽  
Key Success Factors ◽  
Hierarchy Process ◽  
Progress Measurement

This paper proposes an extended earned value management (EEVM) as an integrated comprehensive project progress measurement technique. The method considers all of project key success factors, simultaneously. That is the method guaranties the realistic weights achievement to implement project control and scheduling. The weights are employed to publish a correct and comprehensive progress reports which can guarantee the future decisions for the project. It is noteworthy that the weighting approach is not just an earned value management (EVM), but it covers its concept. Since, the method is comprehensive and according to its ability to take any key success factors in to account, it can be used as a good alternative for the EVM approach, and can be called as an EEVM. The method combines the fuzzy group analytic hierarchy process (FGAHP) and fuzzy technique of order performance by similarity to ideal solution (FTOPSIS) to define activity weights according to some projects uncertain data. Taking to account the advantages of FGAHP for criteria weighting besides FTOPSIS for activity weighting provides a flexible method works with human habits and projects vagueness and uncertainty. Efficiency of the proposed method has been practically verified on a stadium in Kurdistan. The results illustrate superiority of the method in case of comprehensiveness and flexibility in comparison with the other methods.

scholarly journals A Real-Time Physical Progress Measurement Method for Schedule Performance Control Using Vision, an AR Marker and Machine Learning in a Ship Block Assembly Process

Sensors ◽  
2020 ◽  
Vol 20 (18) ◽  
pp. 5386
Author(s):  
Taihun Choi ◽  
Yoonho Seo
Keyword(s):  
Machine Learning ◽  
Real Time ◽  
Measurement Method ◽  
Work Performance ◽  
Time Series Data ◽  
Production Costs ◽  
Series Data ◽  
Image Analysis System ◽  
Progress Management ◽  
Progress Measurement

Progress control is a key technology for successfully carrying out a project by predicting possible problems, particularly production delays, and establishing measures to avoid them (decision-making). However, shipyard progress management is still dependent on the empirical judgment of the manager, and this has led to delays in delivery, which raises ship production costs. Therefore, this paper proposes a methodology for shipyard ship block assembly plants that enables objective process progress measurement based on real-time work performance data, rather than the empirical judgment of a site manager. In particular, an IoT-based physical progress measurement method that can automatically measure work performance without human intervention is presented for the mounting and welding activities of ship block assembly work. Both an augmented reality (AR) marker-based image analysis system and a welding machine time-series data-based machine learning model are presented for measuring the performances of the mounting and welding activities. In addition, the physical progress measurement method proposed in this study was applied to the ship block assembly plant of shipyard H to verify its validity.

scholarly journals Sustainable Application of Hybrid Point Cloud and BIM Method for Tracking Construction Progress

2020 ◽  
Vol 12 (10) ◽  
pp. 4106 ◽  
Author(s):  
Seungho Kim ◽  
Sangyong Kim ◽  
Dong-Eun Lee
Keyword(s):  
Data Collection ◽  
Point Cloud ◽  
3D Model ◽  
Construction Project ◽  
3D Models ◽  
The Past ◽  
Sensing Technology ◽  
Automation Tools ◽  
High Degree ◽  
Progress Measurement

Compared to the past, the complexity of construction-project progress has increased as the size of structures has become larger and taller. This has resulted in many unexpected problems with an increasing frequency of occurrence, such as various uncertainties and risk factors. Recently, research was conducted to solve the problem via integration with data-collection automation tools of construction-project-progress measurement. Most of the methods used spatial sensing technology. Thus, this study performed a review of the representative technologies applied to construction-project-progress data collection and identified the unique characteristics of each technology. The basic principle of the progress proposed in this study is its execution through the point cloud and the attributes of BIM, which were studied in five stages: (1) Acquisition of construction completion data using a point cloud, (2) production of a completed 3D model, (3) interworking of an as-planned BIM model and as-built model, (4) construction progress tracking via overlap of two 3D models, and (5) verification by comparison with actual data. This has confirmed that the technical limitations of the construction progress tracking through the point cloud do not exist, and that a fairly high degree of progress data which contains efficiency and accuracy can be collected.

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