Management of constraints (e.g. materials and labour) is a major challenge in construction projects. Advanced working packaging (AWP) is an effective constraint-management method. However, one prerequisite for AWP, i.e. constraint modelling, is generally performed manually. Information extraction methods in the industry cannot meet the demands for AWP, because they focus on entity extraction but ignore extraction of semantically rich relations. To address this problem, this study proposes a hybrid deep learning model. A bidirectional long short-term memory and conditional random field (Bi-LSTM-CRF) model and knowledge representation learning (KRL) model are developed to extract entities and relations among entities from text documents, respectively. To better apply the KRL model, the study maps domain classes of entities and then stacks class information in the model structure, while employing synonym mapping to handle entities unseen during training. The overall accuracies for extracting entities and relations can reach 0.936 and 0.884, respectively, and adding class information increases relation extraction performance metrics by 6.63%. In a scenario implementation, it is shown that the model can automate constraint modelling for ongoing projects. Therefore, the model is useful for AWP and can reduce delays and reworks by saving a significant amount of time for constraint monitoring and removal.

约束条件（例如材料和人工）的管理是建筑项目中的主要挑战。高级工作包装（AWP）是一种有效的约束管理方法。但是，AWP的一个先决条件，即约束建模，通常是手动执行的。行业中的信息提取方法不能满足对AWP的需求，因为它们专注于实体提取，却忽略了语义丰富的关系的提取。为了解决这个问题，本研究提出了一种混合深度学习模型。开发了双向长短期记忆和条件随机字段（Bi-LSTM-CRF）模型和知识表示学习（KRL）模型，以分别从文本文档中提取实体和实体之间的关系。为了更好地应用KRL模型，该研究会映射实体的领域类别，然后将类别信息堆叠在模型结构中，同时使用同义词映射来处理训练期间看不到的实体。提取实体和关系的总体准确度分别可以达到0.936和0.884，添加类信息可以将关系提取性能指标提高6.63％。在场景实现中，显示了该模型可以针对正在进行的项目自动执行约束建模。因此，该模型对于AWP很有用，并且可以通过节省大量时间来进行约束监视和删除来减少延迟和返工。添加类信息可使关系提取性能指标提高6.63％。在场景实现中，显示了该模型可以针对正在进行的项目自动执行约束建模。因此，该模型对于AWP很有用，并且可以通过节省大量时间来进行约束监视和删除来减少延迟和返工。添加类信息可使关系提取性能指标提高6.63％。在场景实现中，显示了该模型可以针对正在进行的项目自动执行约束建模。因此，该模型对于AWP很有用，并且可以通过节省大量时间来进行约束监视和删除来减少延迟和返工。