The construction industry has traditionally been characterised by the high diversity of its agents and processes, high resistance to change and low incorporation of technology compared to manufacturing industries However, the construction sector is experiencing now a strong renovation process in methodology and tools due to the incorporation of the Building Information Modelling, Lean Construction and Integrated Project Delivery Meanwhile, in production systems, “Industry 40” is a new paradigm that proposes automation, monitoring, sensorisation, robotisation, and digitalisation to improve production and distribution processes In this context, some authors have proposed the concept of “Construction 40” as the counterpart of Industry 40 for the construction sector, although the methodological-technological implications are not clear This research shows a methodological-technological framework adapted to the Architecture, Engineering, Construction, and Operations industry This papers includes a detailed proposal for a reference frameworks and related technologies that could impact on this sector, responding to its complexities and specific challenges, such as the unique spaces for each work, which are difficult to standardise, arbitrary cost overruns and a productivity far below the average for other industries, increasing competitiveness and globalisation, as opposed to its traditionally local deployment, and an increasing demand to reduce the carbon footprint for all its activities

Methodological-Technological Framework for Construction 4.0

Classification and analysis of deep learning applications in construction: A systematic literature review

Deep learning is a new area of machine learning research. Deep learning technology applies the nonlinear and advanced transformation of model abstraction into a large database. The latest development shows that deep learning in various fields and greatly contributed to artificial intelligence so far. This article reviews the contributions and new applications of deep learning. The main target of this review is to give the summarize points for scholars to have the analysis about applications and algorithms. Then review tries to investigate the main applications and uses algorithms. In addition, the advantages of using the method of deep learning and its hierarchical and nonlinear functioning are introduced and compared to traditional algorithms in common applications. The following three criteria should be taken into consideration when choosing the area of application. (1) expertise or knowledge of the author; (2) the successful application of deep learning technology has changed the field of application, such as voice recognition, chat robots, search technology and vision; and (3) deep learning can have a significant impact on the application domain and benefit from recent research with natural language and text processing, information recovery and multimodal information processing resulting from multitasking deep learning. This review provides a general overview of a new concept and the growing benefits and popularity of deep learning, which can help researchers and students interested in deep learning methods.

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Deep Learning Application Pros And Cons Over Algorithm

Intelligent robotic systems for structural health monitoring: Applications and future trends

Towards automated detection and quantification of concrete cracks using integrated images and lidar data from unmanned aerial vehicles

This work presents a process for automated end-to-end inspection of area defects—specifically spalls and delaminations—in RC bridges. The process uses a mobile robotic platform to collect t...

Automated Defect Quantification in Concrete Bridges Using Robotics and Deep Learning

The use of mobile ground and aerial robotics presents a powerful means to augment current visual inspection practice by overcoming some common weaknesses related to accessibility, repeatabi...

Automated Bridge Inspection Using Mobile Ground Robotics

Computer vision and robotics present tremendous opportunities for automating routine inspections of reinforced concrete bridges. One of the most critical aspects of these inspections is delamination assessment, as delaminations present immediate safety concerns due to falling concrete. Current methods of delamination assessment include hammer sounding and chain dragging, which are time consuming and difficult when accessibility is limited. Infrared technology presents an alternative method of assessing delaminations. In this work, a novel inspection method is proposed that uses an infrared camera combined with a convolutional neural network to automatically assess delaminations in infrared images. MobileNetV2 is implemented as an encoder with Deeplab V3 to perform pixel-wise segmentation in infrared images

Evan McLaughlin

Papers

Automated Defect Quantification in Concrete Bridges Using Robotics and Deep Learning

Automated Bridge Inspection Using Mobile Ground Robotics

Combining Deep Learning and Robotics for Automated Concrete Delamination Assessment