Technology adoption is pivotal for the productivity growth in construction industry. This research paper attempts to fill this gap by addressing the following research objectives. First, the predictor factors stimulating project managers' adoption of construction automation innovations are rigorously analyzed using a mixed approach combining a systematic literature review, a knowledgeable panel, and a survey questionnaire. Secondly, the study implements a light deep learning model to track the progress of reinforcing bar placements by verifying completed rebar ties. By linking the progress of the bar to a single binary condition, the number of classes needed to train the neural network drops to only two resulting in a light CNN with a recall rate of 89.2% and precision rate of 95.7%. This model can be implemented on a low power GPU, making it more cost efficient and simpler to adopt on site. A similar approach can be used on other critical activities in construction. This approach can aid inspections, quality control, in combination with drones or robotic systems. The proposed system integrates the most important factors of a successful adoption by providing a proof of concept with potential use cases in construction sites.

技术采用对于建筑行业的生产力增长至关重要。本研究论文试图通过解决以下研究目标来填补这一空白。首先，使用系统文献回顾、知识专家小组和调查问卷相结合的混合方法，对刺激项目经理采用施工自动化创新的预测因素进行了严格分析。其次，该研究实施了一个轻型深度学习模型，通过验证已完成的钢筋连接来跟踪钢筋放置的进度。通过将条形图的进度与单个二元条件联系起来，训练神经网络所需的类数下降到只有两个，从而产生一个轻量级的 CNN，召回率为 89.2%，准确率为 95.7%。该模型可以在低功耗 GPU 上实现，使其更具成本效益且更易于在现场采用。类似的方法可用于施工中的其他关键活动。这种方法可以结合无人机或机器人系统帮助检查、质量控制。拟议的系统通过提供概念证明和建筑工地的潜在用例，整合了成功采用的最重要因素。