摘要
为提升工程连续箱梁移位工作质量,保障工程有序顺利进行,设计龙门吊轨道交叉下的跨海通道工程连续箱梁移位工序AI识别模型。分析连续箱梁移位工序流程,将连续箱梁移位工作划分为钢筋加工、模板安装、混凝土蒸养等五个工序。利用人工智能技术中的门限玻尔兹曼机与支持向量机构建连续箱梁移位工序AI识别模型,采集连续箱梁移位工作图像,将所采集图像作为模型输入,利用卷积神经网络中的卷积层与池化层优化门限玻尔兹曼机模型,利用优化后的模型采集连续箱梁移位工作图像特征;基于不同图像特征,利用支持向量机划分图像类别,完成工序识别。实验结果显示,该模型能够准确识别连续箱梁移位工序,有效提升工程施工性能与经济效益。
In order to improve the quality of continuous box girder displacement and ensure the orderly and smooth progress of the project,an AI identification model for the continuous box girder displacement process of cross-sea channel project under the crossing of a gantry crane track is designed.The process flow of continuous box girder displacement is analyzed,and it is divided into five processes:reinforcement processing,formwork installation,and concrete steam curing.The AI recognition model of the continuous box girder shift process is built using threshold Boltzmann machine and support vector machine in artificial intelligence technology.Continuous box girder shift working images are collected,and these collected images are taken as the model input.The convolution layer and pooling layer in the convolutional neural network are utilized to optimize the threshold Boltzmann machine model.The optimized model is then used to collect the characteristics of the continuous box girder shift working images.Based on different image features,support vector machine is used to classify image categories and complete process recognition.The experimental results show that the model can accurately identify the displacement process of continuous box girder and effectively improve the construction performance and economic benefits of the project.
作者
黄华
HUANG Hua(Guangdong Shengxiang Traffic Engineering Testing Co.,LTD.,Guangzhou 511400,China)
出处
《结构工程师》
2024年第2期184-191,共8页
Structural Engineers
关键词
龙门吊轨道
跨海通道工程
连续箱梁移位
工序识别
深度学习
支持向量机
gantry crane track
cross-sea channel project
displacement of continuous box girder
process identification
deep learning
support vector machine
