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This paper focuses on the application of “combined simulation modeling” to achieve more accurate and flexible modeling of random processes affecting construction progress. In particular, a model is presented where a project schedule prepared using the critical path method (CPM) is transferred into a process interaction-discrete event simulation model and then combined with a continuous change weather process in the same model. The combined approach provides certain advantages to other methods. It is more effective than deterministically adding the expected delay due to weather to the expected duration of the project estimated from CPM, for example. It is also more accurate than using straight Monte Carlo-simulation of the CPM schedule or discrete event simulation modeling. The paper also provides an overview of the various aspects of implementing the combined model including use of mathematical and statistical methods to model weather processes and trained neural networks to estimate the effects of weather on productivity.
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Papers that cite the same references — the relational fallback while embeddings are still backfilling.
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