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Weather-wise: A weather-aware planning tool for improving construction productivity and dealing with claims

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journal contribution
posted on 2018-04-26, 14:45 authored by Pablo Ballesteros-Perez, Yonatan Alexis Rojas-Cespedes, Will Hughes, Shabnam Kabiri, Eugenio Pellicer, Daniel Mora-Melia, Maria Luisa del Campo-Hitschfeld
© 2017 Elsevier B.V. The influence of unforeseen, extreme weather in construction works usually impacts productivity, causes significant project delays and constitutes a frequent source of contractor's claims. However, construction practitioners cannot count on sound methods for mediating when weather-related claims arise, nor harnessing the influence of weather variability in construction projects. Building on the few most recent quantitative studies identifying those key weather agents and levels of intensity that affect some standard building construction activities, a new stochastic model that processes and replicates the spatio-temporal variability of combined weather variables is proposed. This model can help anticipate weather-related project duration variability; improving construction productivity by selecting the best project start date; and objectively evaluating weather-related claims. A two-building construction case study using different Spanish locations is used to demonstrate the model. The results showed that ignoring the influence of weather can lead to an extension of 5–20% longer project duration compared to planned.


This research was supported by the CIOB Bowen Jenkins Legacy Research Fund at the University of Reading (H5405400).



  • Architecture, Building and Civil Engineering

Published in

Automation in Construction




81 - 95


BALLESTEROS-PEREZ, P. ...et al., 2017. Weather-wise: A weather-aware planning tool for improving construction productivity and dealing with claims. Automation in Construction, 84, pp. 81-95.


© Elsevier


  • AM (Accepted Manuscript)

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This work is made available according to the conditions of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0) licence. Full details of this licence are available at: https://creativecommons.org/licenses/by-nc-nd/4.0/

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This paper was accepted for publication in the journal Automation in Construction and the definitive published version is available at https://doi.org/10.1016/j.autcon.2017.08.022




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