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Advances in the design of image processing software and in the development of cameras with on‐board processing are changing the face of machine vision. A group of suppliers is now producing vision systems targeted directly at end‐users in the assembly plant.

Examines a recently launched integration of smart cameras into industrial robots to make them responsive to a changing environment.

Reviews the capabilities of the vision‐enabled robot, citing installations in Sweden and the UK, then describes the robot and vision programming procedure.

Vision integration opens up a range of new possibilities such as simultaneous product handling and inspection, as well as providing real‐time robot guidance. Standardisation plays an extremely valuable role in building integrated systems from disparate technological elements. Here ActiveX web standards, ethernet connectivity, a standard interchangeable family of cameras and a common controller for a whole range of robots are the keys to the synthesis of a powerful new combination of robot and machine vision.

Draws to the attention of industrial engineers the availability of a family of robots with integrated machine vision.

Unlocking the potential of Big Data Analytics (BDA) has proven to be a transformative factor for the Architecture, Engineering and Construction (AEC) industry. This has prompted researchers to focus attention on BDA in the AEC industry (BDA-in-AECI) in recent years, leading to a proliferation of relevant research. However, an in-depth exploration of the literature on BDA-in-AECI remains scarce. As a result, this study seeks to systematically explore the state-of-the-art review on BDA-in-AECI and identify research trends and gaps in knowledge to guide future research.

This state-of-the-art review was conducted using a mixed-method systematic review. Relevant publications were retrieved from Scopus and then subjected to inclusion and exclusion criteria. A quantitative bibliometric analysis was conducted using VOSviewer software and Gephi to reveal the status quo of research in the domain. A further qualitative analysis was performed on carefully screened articles. Based on this mixed-method systematic review, knowledge gaps were identified and future research agendas of BDA-in-AECI were proposed.

The results show that BDA has been adopted to support AEC decision-making, safety and risk assessment, structural health monitoring, damage detection, waste management, project management and facilities management. BDA also plays a major role in achieving construction 4.0 and Industry 4.0. The study further revealed that data mining, cloud computing, predictive analytics, machine learning and artificial intelligence methods, such as deep learning, natural language processing and computer vision, are the key methods used for BDA-in-AECI. Moreover, several data acquisition platforms and technologies were identified, including building information modeling, Internet of Things (IoT), social networking and blockchain. Further studies are needed to examine the synergies between BDA and AI, BDA and Digital twin and BDA and blockchain in the AEC industry.

The study contributes to the BDA-in-AECI body of knowledge by providing a comprehensive scope of understanding and revealing areas for future research directions beneficial to the stakeholders in the AEC industry.

This work aims to develop a web platform for inspecting roof structures for technical assistance supported by drones and artificial intelligence. The tools used were HTML, CSS and JavaScript languages; Firebase software for infrastructure; and Custom Vision for image processing.

This study adopted the design science research approach, and the main stages for the development of the web platform include (1) creation and validation of the roof inspection checklist, (2) validation of the use of Custom Vision as an image recognition tool, and (3) development of the web platform.

The results of automatic recognition showed a percentage of 77.08% accuracy in identifying pathologies in roof images obtained by drones for technical assistance.

This study contributed to developing a drone-integrated roof platform for visual data collection and artificial intelligence for automatic recognition of pathologies, enabling greater efficiency and agility in the collection, processing and analysis of results to guarantee the durability of the building.

The digital construction transformation requires using emerging digital technology such as deep learning to automate implementing tasks. Therefore, this paper aims to evaluate the current state of using deep learning in the construction management tasks to enable researchers to determine the capabilities of current solutions, as well as finding research gaps to carry out more research to bridge revealed knowledge and practice gaps.

The scientometric analysis is conducted for 181 articles to assess the density of publications in different topics of deep learning-based construction management applications. After that, a thematic and gap analysis are conducted to analyze contributions and limitations of key published articles in each area of application.

The scientometric analysis indicates that there are four main applications of deep learning in construction management, namely, automating progress monitoring, automating safety warning for workers, managing construction equipment, integrating Internet of things with deep learning to automatically collect data from the site. The thematic and gap analysis refers to many successful cases of using deep learning in automating site management tasks; however, more validations are recommended to test developed solutions, as well as additional research is required to consider practitioners and workers perspectives to implement existing applications in their daily tasks.

This paper enables researchers to directly find the research gaps in the existing solutions and develop more workable applications to bridge revealed gaps. Accordingly, this will be reflected on speeding the digital construction transformation, which is a strategy over the world.

To the best of the authors’ knowledge, this paper is the first of its kind to adopt a structured technique to assess deep learning-based construction site management applications to enable researcher/practitioners to either adopting these applications in their projects or conducting further research to extend existing solutions and bridging revealed knowledge gaps.

The purpose of this paper is to develop a novel and systematic framework for bridge inspection and management to improve the efficiency in current practice.

A new framework that implements camera-based unmanned aerial systems (UASs) with computer vision algorithms to collect and process inspection data, and Bridge Information Modeling (BrIM) to store and manage all related inspection information is proposed. An illustrative case study was performed using the proposed framework to test its feasibility and efficiency.

The test results of the proposed framework on an existing bridge verified that: high-resolution images captured by an UAS enable to visually identify different types of defects, and detect cracks automatically using computer vision algorithms, the use of BrIM enable assigning defect information on individual model elements, manage all bridge data in a single model across the bridge life cycle. The evaluation by bridge inspectors from 12 states across the USA demonstrated that all of the identified problems, except for being subjective, can be improved using the proposed framework.

The proposed framework enables to: collect and document accurate bridge inspection data, reduce the number of site visits and avoid data overload and facilitate a more efficient, cost-effective and safer bridge inspection process.

This paper contributes a novel and systematic framework for the collection and integration of inspection data for bridge inspection and management. The findings from the case study suggest that the proposed framework should help improve current bridge inspection and management practice. Furthermore, the difficulties experienced during the implementation are evaluated, which should be helpful for improving the efficiency and the degree of automation of the proposed framework further.