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Construction schedule augmentation with implicit dependency constraints and automated generation of lookahead plan revisions
Graphical abstract Display Omitted
Highlights A NLP transformer method for predicting implicit construction dependencies with 91% F1-Score. A process for augmenting schedules with the role and flexibility of activity relationships. A method for revising lookahead plans based on the flexibility of activity dependencies. A Constrained Condition model for creating optimal lookahead plan revisions.
Abstract Revising lookahead plans is practiced in all construction projects to meet contractual deadlines, mitigate activity delays, and disentangle process bottlenecks. We present a new machine learning-based method –which we call Implicit Logic Checker (ILC)– to learn the implicit dependency constraints and the flexibility of construction schedule relationships using a Transformer language model architecture. We then leverage relationships’ flexibilities to provide a basis for creating optimal lookahead plan revisions to help mitigate the effects of unavoidable activity delays. We then deploy a Constrained Conditional (CC) model that applies learned construction planning knowledge to build revised lookahead plans and optimizes them for consistency and duration. Our ILC model is trained and tested on 35,332 manually labeled predecessor-successor relationships from eight real construction projects achieving an F1-Score of 91%. Similarly, the revised lookahead plans generated by the CC model on two case studies show reduced overall plan durations.
Construction schedule augmentation with implicit dependency constraints and automated generation of lookahead plan revisions
Graphical abstract Display Omitted
Highlights A NLP transformer method for predicting implicit construction dependencies with 91% F1-Score. A process for augmenting schedules with the role and flexibility of activity relationships. A method for revising lookahead plans based on the flexibility of activity dependencies. A Constrained Condition model for creating optimal lookahead plan revisions.
Abstract Revising lookahead plans is practiced in all construction projects to meet contractual deadlines, mitigate activity delays, and disentangle process bottlenecks. We present a new machine learning-based method –which we call Implicit Logic Checker (ILC)– to learn the implicit dependency constraints and the flexibility of construction schedule relationships using a Transformer language model architecture. We then leverage relationships’ flexibilities to provide a basis for creating optimal lookahead plan revisions to help mitigate the effects of unavoidable activity delays. We then deploy a Constrained Conditional (CC) model that applies learned construction planning knowledge to build revised lookahead plans and optimizes them for consistency and duration. Our ILC model is trained and tested on 35,332 manually labeled predecessor-successor relationships from eight real construction projects achieving an F1-Score of 91%. Similarly, the revised lookahead plans generated by the CC model on two case studies show reduced overall plan durations.
Construction schedule augmentation with implicit dependency constraints and automated generation of lookahead plan revisions
Amer, Fouad (author) / Jung, Yoonhwa (author) / Golparvar-Fard, Mani (author)
2023-04-20
Article (Journal)
Electronic Resource
English
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