Deep Learning-Based Automatic Detection and Evaluation on Concrete Surface Bugholes

Concrete exterior quality is one of the important metrics in evaluating construction project quality.Among the defects affecting concrete exterior quality,bughole is one of the most common imperfections,thus detecting concrete bughole accurately is significant for improving concrete exterior quality and consequently the quality of the whole project.This paper presents a deep learning-based method for detecting concrete surface bugholes in a more objective and automatic way.The bugholes are identified in concrete surface images by Mask R-CNN.An evaluation metric is developed to indicate the scale of concrete bughole.The proposed approach can detect bugholes in an instance level automatically and output the mask of each bughole,based on which the bughole area ratio is automatically calculated and the quality grade of the concrete surfaces is assessed.For demonstration,a total of 273 raw concrete surface images taken by mobile phone cameras are collected as a dataset.The test results show that the average precision(AP)of bughole masks is 90.8%.

Automated monitoring and warning solution for concrete placement and vibration workmanship quality issues

Placing and vibrating concrete are vital activities that affect its quality.The current monitoring method relies on visual and time-consuming feedbacks by project managers,which can be subjective.With this method,poor workmanship cannot be detected well on the spot;rather,the concrete is inspected and repaired after it becomes hardened.To address the problems of retroactive quality control measures and to achieve real-time quality assurance of concrete operations,this paper presents a monitoring and warning solution for concrete placement and vibration workman-ship quality.Specifically,the solution allows for collecting and compiling real-time sensor data related to the work-manship quality and can send alerts to project managers when related parameters are out of the required ranges.This study consists of four steps:(1)identifying key operational factors(KOFs)which determine acceptable workmanship of concrete work;(2)reviewing and selecting an appropriate positioning technology for collecting the data of KOFs;(3)designing and programming modules for a solution that can interpret the positioning data and send alerts to project managers when poor workmanship is suspected;and(4)testing the solution at a certain construction site for validation by comparing the positioning and warning data with a video record.The test results show that the monitoring performance of concrete placement is accurate and reliable.Follow-up studies will focus on developing a communication channel between the proposed solution and concrete workers,so that feedbacks can be directly delivered to them.