Abstracts – Browse Results

Search or browse again.

Click on the titles below to expand the information about each abstract.
Viewing 12 results ...

Anastasopoulos, P C, Haddock, J E and Peeta, S (2014) Cost Overrun in Public-Private Partnerships: Toward Sustainable Highway Maintenance and Rehabilitation. Journal of Construction Engineering and Management, 140(06).

Becker, T C, Jaselskis, E J and El-Gafy, M (2014) Improving Predictability of Construction Project Outcomes through Intentional Management of Indirect Construction Costs. Journal of Construction Engineering and Management, 140(06).

Chen, Y Q, Zhang, Y B and Zhang, S J (2014) Impacts of Different Types of Owner-Contractor Conflict on Cost Performance in Construction Projects. Journal of Construction Engineering and Management, 140(06).

El-Abbasy, M S, Senouci, A, Zayed, T, Mirahadi, F and Parvizsedghy, L (2014) Condition Prediction Models for Oil and Gas Pipelines Using Regression Analysis. Journal of Construction Engineering and Management, 140(06).

  • Type: Journal Article
  • Keywords: Oil pipelines; Gas pipelines; Predictions; Regression analysis; Oil and gas pipelines; Condition prediction; Regression analysis; Quantitative methods;
  • ISBN/ISSN: 0733-9364
  • URL: https://doi.org/10.1061/(ASCE)CO.1943-7862.0000838
  • Abstract:
    Although they are the safest means of transporting oil and gas products, pipelines can sometimes fail with hazardous consequences and large business losses. The decision to replace, repair, or rehabilitate depends mainly on the condition of the pipeline. Assessing and predicting its condition is therefore a key step in the maintenance plan of a pipeline. Several models have recently been developed to predict pipeline failures and conditions. However, most of these models were limited to the use of corrosion as the sole factor to assess the condition of pipelines. The objective of this paper is to develop models that assess and predict the condition of oil and gas pipelines based on several factors including corrosion. The regression analysis technique was used to develop the condition prediction models based on historical inspection data of three existing pipelines in Qatar. In addition, a condition assessment scale for pipelines was built based on expert opinion. The models were able to satisfactorily predict pipeline condition with an average percent validity above 96% when applied to the validation data set. The models are expected to help decision makers assess and predict the condition of existing oil and gas pipelines and hence prioritize their inspection and rehabilitation planning.

Hawas, F and Cifuentes, A (2014) Valuation of Projects with Stochastic Cash Flows and Intertemporal Correlations: Practical Modeling Guidelines. Journal of Construction Engineering and Management, 140(06).

Ko, C and Chung, N (2014) Lean Design Process. Journal of Construction Engineering and Management, 140(06).

Lee, H W, Choi, K and Gambatese, J A (2014) Real Options Valuation of Phased Investments in Commercial Energy Retrofits under Building Performance Risks. Journal of Construction Engineering and Management, 140(06).

Maghrebi, M, Travis Waller, S and Sammut, C (2014) Assessing the Accuracy of Expert-Based Decisions in Dispatching Ready Mixed Concrete. Journal of Construction Engineering and Management, 140(06).

O’Connor, J T, O’Brien, W J and Choi, J O (2014) Critical Success Factors and Enablers for Optimum and Maximum Industrial Modularization. Journal of Construction Engineering and Management, 140(06).

Russell, M M, Hsiang, S M, Liu, M and Wambeke, B (2014) Causes of Time Buffer and Duration Variation in Construction Project Tasks: Comparison of Perception to Reality. Journal of Construction Engineering and Management, 140(06).

West, J (2014) Collaborative Patterns and Power Imbalance in Strategic Alliance Networks. Journal of Construction Engineering and Management, 140(06).

Zhang, S, Migliaccio, G C, Zandbergen, P A and Guindani, M (2014) Empirical Assessment of Geographically Based Surface Interpolation Methods for Adjusting Construction Cost Estimates by Project Location. Journal of Construction Engineering and Management, 140(06).