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Canales, A R, Arbelaez, M, Vasquez, E, Aveiga, F, Strong, K, Walters, R, Jaselskis, E J and Jahren, C T (2009) Exploring Training Needs and Development of Construction Language Courses for American Supervisors and Hispanic Craft Workers. Journal of Construction Engineering and Management, 135(05), 387–96.

Dai, J, Goodrum, P M, Maloney, W F and Srinivasan, C (2009) Latent Structures of the Factors Affecting Construction Labor Productivity. Journal of Construction Engineering and Management, 135(05), 397–406.

Hegab, M and Smith, G R (2009) Labor Performance Analysis for Microtunneling Projects. Journal of Construction Engineering and Management, 135(05), 432–5.

Hinze, J and Olbina, S (2009) Empirical Analysis of the Learning Curve Principle in Prestressed Concrete Piles. Journal of Construction Engineering and Management, 135(05), 425–31.

  • Type: Journal Article
  • Keywords: Piles; Prefabrication; Empirical equations; Prestressed concrete;
  • ISBN/ISSN: 0733-9364
  • URL: https://doi.org/10.1061/(ASCE)CO.1943-7862.0000004
  • Abstract:
    The principle of learning curves can be applied in construction for the prediction of the time/cycle of future work, work performance levels, and other performance measures. Learning curve principles can be effectively utilized in litigation cases where production is compromised by delays. The objective of this study was to determine if learning curves could be used to accurately predict the production efforts of future units by applying the principles to the prefabrication and driving of prestressed concrete piles. The individual time to cast each of the concrete piles was recorded and used to compute the cumulative average time ( CATN ) to fabricate the concrete piles. The data were used to compute the learning rate (Ï•) and the theoretical time to complete the first unit ( Kc ) . From this information, predictions were made as to the amount of effort to fabricate future piles. The results showed that the pile fabrication crew improved its learning throughout the pile fabrication effort, but this improvement was quite small. The learning curve theory was found to apply well to large numbers of repeated items, and that the predictions made with learning curves are reasonably accurate.

Hwang, S (2009) Dynamic Regression Models for Prediction of Construction Costs. Journal of Construction Engineering and Management, 135(05), 360–7.

Jang, W and Skibniewski, M J (2009) Cost-Benefit Analysis of Embedded Sensor System for Construction Materials Tracking. Journal of Construction Engineering and Management, 135(05), 378–86.

Lewis, P, Rasdorf, W, Frey, H C, Pang, S and Kim, K (2009) Requirements and Incentives for Reducing Construction Vehicle Emissions and Comparison of Nonroad Diesel Engine Emissions Data Sources. Journal of Construction Engineering and Management, 135(05), 341–51.

Lucko, G and Peña Orozco, A A (2009) Float Types in Linear Schedule Analysis with Singularity Functions. Journal of Construction Engineering and Management, 135(05), 368–77.

Mao, X, Zhang, X and AbouRizk, S M (2009) Enhancing Value Engineering Process by Incorporating Inventive Problem-Solving Techniques. Journal of Construction Engineering and Management, 135(05), 416–24.

Mitropoulos, P and Cupido, G (2009) Safety as an Emergent Property: Investigation into the Work Practices of High-Reliability Framing Crews. Journal of Construction Engineering and Management, 135(05), 407–15.

Moynihan, G, Zhou, H and Cui, Q (2009) Stochastic Modeling for Pavement Warranty Cost Estimation. Journal of Construction Engineering and Management, 135(05), 352–9.