Finding Best Model to Forecast Construction Duration of Road Tunnels with New Austrian Tunneling Method Using Bayesian Inference

Hosseinian, Seyedmohammadhossein; Reinschmidt, Kenneth F.

doi:10.3141/2522-11

Cited by 5 publications

(3 citation statements)

References 14 publications

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“…Long and Ohsato (2009) introduced a new method for scheduling repetitive construction projects with several objectives including project duration, project cost or both of them and based on genetic algorithms. Hosseinian and Reinschmidt (2015) research was aimed at finding a best progress model for tunneling projects with the new Austrian tunneling method (NATM) by conducting Bayesian analysis on available data of a massive project. The analysis revealed that the dual Gompertz function was the most reliable model for this purpose.…”

Section: Project Duration Prediction Modelsmentioning

confidence: 99%

Neural network models for actual duration of Greek highway projects

Titirla

Aretoulis

2019

JEDT

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Purpose This paper aims to examine selected similar Greek highway projects to create artificial neural network-based models to predict their actual construction duration based on data available at the bidding stage. Design/methodology/approach Relevant literature review is presented that highlights similar research approaches. Thirty-seven highway projects, constructed in Greece, with similar type of available data, were examined. Considering each project’s characteristics and the actual construction duration, correlation analysis is implemented, with the aid of SPSS. Correlation analysis identified the most significant project variables toward predicting actual duration. Furthermore, the WEKA application, through its attribute selection function, highlighted the most important subset of variables. The selected variables through correlation analysis and/or WEKA and appropriate combinations of these are used as input neurons for a neural network. Fast Artificial Neural Network (FANN) Tool is used to construct neural network models in an effort to predict projects’ actual duration. Findings Variables that significantly correlate with actual time at completion include initial cost, initial duration, length, lanes, technical projects, bridges, tunnels, geotechnical projects, embankment, landfill, land requirement (expropriation) and tender offer. Neural networks’ models succeeded in predicting actual completion time with significant accuracy. The optimum neural network model produced a mean squared error with a value of 6.96E-06 and was based on initial cost, initial duration, length, lanes, technical projects, tender offer, embankment, existence of bridges, geotechnical projects and landfills. Research limitations/implications The sample size is limited to 37 projects. These are extensive highway projects with similar work packages, constructed in Greece. Practical implications The proposed models could early in the planning stage predict the actual project duration. Originality/value The originality of the current study focuses both on the methodology applied (combination of Correlation Analysis, WEKA, FannTool) and on the resulting models and their potential application for future projects.

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Section: Project Duration Prediction Modelsmentioning

confidence: 99%

Neural network models for actual duration of Greek highway projects

Titirla

Aretoulis

2019

JEDT

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“…Related studies in project progress concentrated on its development and for predicting final duration of project progress have been conducted. Hosseinian and Reinschmidt (2015) predicted project final duration, which is the time span used to finish the progress of the project by the means of Bayesian inference. Bayesian inference is referred to as a strong probability-based approach for providing the final outcome of the progress considering the previous performance of the project.…”

Section: Physics' Basic Principlesmentioning

confidence: 99%

Project scheduling and forecasting by laws of physical movement

Bagherpour¹,

Mardimamaqani²,

Fallahdizcheh³

et al. 2019

10.5267/j.jpm

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Measuring actual project progress and comparing it with the planned time schedule is essential in controlling of a project. The outcome of the latter follows the s-curve diagram, which compares actual progress with the planned amount and finally analyzing any gap between them to draw directions for project scheduling. However, final duration of the project cannot be either estimated or predicted through conventional methods. The new method, using Rules of kinematics, can calculate the final duration of the project. This novel approach is successfully implemented on a construction project. The first objective in new method is forecasting final duration of the project, finding equation path and draw a new s-curve diagram for actual status during the execution process. The second objective is to evaluate schedule performance of the project and report the status of the project at each period which is a useful tool for managers.

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“…As shown in Table 1, numerous articles were found in regard to the prediction of actual road construction duration, four that address risk analysis and/or allocation and two that aim to predict expected extensions of time (EoT). Other studies related to construction time for road projects were found, but these dealt with scheduling, control or forensic evaluation techniques and their application to the complexities of specific aspects of road construction such as excavations [11], tunnels [12] or bridges [13].…”

Section: Introductionmentioning

confidence: 99%

Delay Risk Assessment Models for Road Projects

Antoniou

2021

Systems

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Significant universal research regarding causes of delay in road projects has been carried out based on expert opinion. This study classifies and standardizes all road construction delay factors found in the literature by developing a common risk breakdown structure (RBS) to allow for comparison between real project delay factors, and the study also proposes two delay risk assessment models (DRAMs) based on delay information from 120 real projects constructed in a similar environment with akin procurement and contract policies. The first calculates the risk priority number (RPN) and the second applies the Technique for Order Preference by Similarity to Ideal Situation (TOPSIS) multi-criteria decision-making method (MCDM). The results showed that four specific delay factors dominate in Greek road construction for which relevant mitigation proposals are made. The proposed DRAMs, while calibrated for application in Greece, can be adapted to any construction environment for which real project data is available to provide a tool for transferring experience from past projects to future projects and from accomplished to novice public client decision makers.

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Finding Best Model to Forecast Construction Duration of Road Tunnels with New Austrian Tunneling Method Using Bayesian Inference

Cited by 5 publications

References 14 publications

Neural network models for actual duration of Greek highway projects

Neural network models for actual duration of Greek highway projects

Project scheduling and forecasting by laws of physical movement

Delay Risk Assessment Models for Road Projects

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