TY - JOUR
T1 - Towards self-recovering construction schedules
T2 - a new method for periodically updating project plans and optimizing recovery actions
AU - AlJassmi, Hamad
AU - Abduljalil, Yusef
AU - Philip, Babitha
N1 - Funding Information:
The authors are grateful to the UAE University for the facilities provided for research. This research was financially supported by the Research Affairs Office at UAE University under grant number 12R099
Publisher Copyright:
© 2022 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group on behalf of the Architectural Institute of Japan, Architectural Institute of Korea and Architectural Society of China.
PY - 2023
Y1 - 2023
N2 - It is common for a construction schedule to deviate from its original-planned baseline, as uncertainty is inherent in all construction activities. Accordingly, planners are required to perform periodic schedule updates that learn from retrospective progress to more accurately schedule remaining activities and draw optimum recovery plans. This research proposes a method that utilizes a neural network regression model to forecast upcoming productivity rates based on retrospective progress and accordingly updates the schedule on a regular time interval with the required resource adjustments to meet the planned end date of the project with optimal cost. The method was tested on brickwork activities at a residential complex construction project in the UAE, using retrospective progress data of 1487 working days for 132 masons, and was found to be 98% accurate in predicting labor productivity, which was thus used as a basis to draw schedule recovery plans according to the proposed framework. In essence, this research provides a platform toward an automated self-recovering scheduling system, which serves construction managers in proactively preventing potential schedule deficiencies.
AB - It is common for a construction schedule to deviate from its original-planned baseline, as uncertainty is inherent in all construction activities. Accordingly, planners are required to perform periodic schedule updates that learn from retrospective progress to more accurately schedule remaining activities and draw optimum recovery plans. This research proposes a method that utilizes a neural network regression model to forecast upcoming productivity rates based on retrospective progress and accordingly updates the schedule on a regular time interval with the required resource adjustments to meet the planned end date of the project with optimal cost. The method was tested on brickwork activities at a residential complex construction project in the UAE, using retrospective progress data of 1487 working days for 132 masons, and was found to be 98% accurate in predicting labor productivity, which was thus used as a basis to draw schedule recovery plans according to the proposed framework. In essence, this research provides a platform toward an automated self-recovering scheduling system, which serves construction managers in proactively preventing potential schedule deficiencies.
KW - Productivity
KW - activity crashing
KW - delay recovery
KW - fast tracking
KW - neural network
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U2 - 10.1080/13467581.2022.2153055
DO - 10.1080/13467581.2022.2153055
M3 - Article
AN - SCOPUS:85144852504
SN - 1346-7581
VL - 22
SP - 2335
EP - 2347
JO - Journal of Asian Architecture and Building Engineering
JF - Journal of Asian Architecture and Building Engineering
IS - 4
ER -