The development of a rebar-counting model for reinforced concrete columns: Using an unmanned aerial vehicle and deep-learning approach

Inspecting the number of rebars in each column of a reinforced concrete (RC) structure is a significant task that must be undertaken during the rebar inspection process. Conventionally, counting the rebars has relied on a manual inspection carried out by visiting inspectors. However, this approach is very time-consuming, labor-intensive, and poses a potential safety risk. Previous studies have focused on the applications of counting the rebars for a production line and/or warehouse, using vision-based methods. Therefore, this study aims to propose an innovative approach incorporating the use of an unmanned aerial vehicle (UAV) on real construction sites to count the rebars automatically. For analyzing the images, robust object detection methods based on deep learning (Faster R-CNN, R-FCN, SSD 300, SSD500, YOLOv5, and YOLOv6) were developed. A total of 384 models generated from six different methods were trained and implemented using data sets based on the original and augmented images with adjustments made for the hyperparameters. In a test, the best optimized model based on Faster R-CNN produced an accuracy of 94.61% at AP50. In addition, video testing demonstrated a coverage of up to 32 frames per second in the experimental environment, suggesting that this method has potential for real-time application

Opportunities of industry 4.0 for SMEs in the area of rebar steel distribution within the construction industry –a PPC potential analysis

Industry 4.0coins a global trend towards applying digital technologies to manufacturing. However, the openness towards related innovations varies among different industries. Whilst for instance many manufacturers within automotive or logistics industries have optimized their factories already, the German construction sector falls back regarding adaptation. Reinforcement steel distributors reflect a fundamental part of this sector and are broadly hesitant to initiate their factory transformation. This research provides an overview of the opportunities of Industry 4.0 in the area of reinforcement steel trade and processing. It analyzes how to derive an innovative factory design leveraging on state-of-the-art production planning methods, by aggregating market information and technology

Steel bar counting from images with machine learning

Counting has become a fundamental task for data processing in areas such as micro-biology, medicine, agriculture and astrophysics. The proposed SA-CNN-DC (Scale Adaptive— Convolutional Neural Network—Distance Clustering) methodology in this paper is designed for automated counting of steel bars from images. Its design consists of two Machine Learning techniques: Neural Networks and Clustering. The system has been trained to count round and squared steel bars, obtaining an average detection accuracy of 98.81% and 98.57%, respectively. In the steel industry, counting steel bars is a time consuming task which highly relies on human labour and is prone to errors. Reduction of counting time and resources, safety and productivity of employees and high confidence of the inventory are some of the advantages of the proposed methodology in a steel warehouse

Sustainable Construction

Construction is one of the main sectors that generates greenhouse gases. This industry consumes large amounts of raw materials, such as stone, timber, water, etc. Additionally, infrastructure should provide service over many years without safety problems. Therefore, their correct design, construction, maintenance, and dismantling are essential to reducing economic, environmental, and societal consequences. That is why promoting sustainable construction has recently become extremely important. To help address and resolve these types of questions, this book explores new ways of reducing the environmental impacts caused by the construction sector, as well promotes social progress and economic growth. The chapters collect the papers included in the “Sustainable Construction” Special Issue of the Sustainability journal. The papers cover a wide spectrum of issues related to the use of sustainable materials in construction, the optimization of designs based con sustainable indicators, the life-cycle assessment, the decision-making processes that integrate economic, social, and environmental aspects, and the promotion of durable materials that reduce future maintenance

Case Study Analysis of Improving Productivity Rates for Self-Perform Concrete

Productivity is one of the most important aspects of a successful construction project, especially if the work is self-performed and not sub-contracted out to another contractor. Usually the success of a project is directly correlated to how well the production has been throughout the course of the job. Often when a project manager notices their job is not doing well and not making their target profits or man-hours, productivity is usually the answer to that problem. This paper will examine the different ways and methods of tracking production that was used by Overaa construction. The focus of this study is on the production rates of self-performed concrete that was done by Overaa construction on three different jobs: Jamieson Canyon Water treatment plant improvements, Sacramento Water treatment facilities, and Napa’s new pump station. This paper describes the different methods used for each job and compares each method with the success of the job. After comparing the three different tracking methods used by Overaa and after analyzing each of their success, there should be a definitive answer on which method to use when starting out in the construction industry

Automatic Scaffolding Productivity Measurement through Deep Learning

This study developed a method to automatically measure scaffolding productivity by extracting and analysing semantic information from onsite vision data