The urban drainage system is an important part of the urban water cycle. However, with the aging of drainage pipelines and other external reasons, damages such as cracks, corrosion, and deformation of underground pipelines can cause serious consequences such as urban waterlogging and road collapse. At present, the detection of underground drainage pipelines mostly focuses on the qualitative identification of pipeline damage, and it is impossible to quantitatively analyze pipeline damage. Therefore, a method to quantify the damage volume of concrete pipes that combines surface segmentation and reconstruction is proposed. An RGB-D sensor is used to collect the damage information of the drainage pipeline, and the collected depth frame is registered to generate the pipeline’s surface point cloud. Voxel sampling and Gaussian filtering are used to improve data processing efficiency and reduce noise, respectively, and the RANSAC algorithm is used to remove the pipeline’s surface information. The ball-pivoting algorithm is used to reconstruct the surface of the segmented damage data and pipe’s surface information, and finally to obtain the damage volume. In order to evaluate, we conducted our research on real-world materials. The measurement results show that the method proposed in this paper measures an average relative error of 7.17% for the external damage volume of concrete pipes and an average relative error of 5.22% for the internal damage measurements of concrete pipes.

Under the coupling effect of the defects inside and around pipes, the shear displacement of the joints of old concrete pipes fallen into disrepair usually shows an accelerated deterioration trend, and its excessive value can lead to structural damage or fluid leakage. Therefore, developing a prediction model for the maximum shear displacement of concrete drainage pipe joints with preexisting defects is of crucial significance. To this end, this work selects internal corrosion and erosion voids as the typical defects inside and around old pipes, establishes 300 sets of three‐dimensional fine finite element (FE) models of the concrete drainage pipes with corrosion and void, and verifies the reliability of the developed FE model by the full‐scale tests. Based on the verified FE data, a prediction model is successfully proposed for the assessment of the maximum shear displacement of the concrete drainage pipe joints with corrosion and void defects. Moreover, parameter sensitivity analysis is carried out to reveal their relative contribution to the maximum shear displacement of pipe joints. The results show that the model proposed herein has the ability to accurately predict the maximum shear displacement of the pipe joints. The maximum and minimum relative error between the value predicted by the proposed model and the one computed by the FE model is also 12.4% and 4.8% respectively. Moreover, the traffic load with a contribution of 28.7% has the greatest impact on the maximum shear displacement of pipe joints.

With the age of pipeline and increase in the volume of urban sewage, the pipeline has different degrees of defects, which can cause safety problems such as road collapse and urban flooding. The service life of drainage pipes is closely related to daily maintenance and inspection, so it is very important to inspect the defects and monitor the operation of drainage pipes regularly. However, the existing research lacks quantitative detection and intelligent management of pipeline defect information. Therefore, the depth camera is used as the sensor to quantitatively detect the volume and area of the pit on the concrete pipe, and a defect information management platform is constructed in this paper. Firstly, combined BIM model with 3D point cloud, this paper proposes a 3D defect information management platform of drainage pipeline. Then, the depth camera is used to collect the damage data and preprocess the data, and a method for calculating the damage volume and surface area of drainage pipeline based on 3D mesh reconstruction of the defect point cloud is proposed. The verification experiment results show that the error between the quantized volume and the real volume is mostly within 10%, and the maximum error is 17.54%, indicating high accuracy. The drainage pipeline information model is created. Finally, the data is uploaded to the information management platform to realize the visualization and informatization of pipeline defects and the later operation and maintenance requirements of the pipeline.