The application of “deep learning” in construction site management: scientometric, thematic and critical analysis

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Faris Elghaish ◽  
Sandra T. Matarneh ◽  
Mohammad Alhusban
Keyword(s):  
Deep Learning ◽  
Construction Management ◽  
Progress Monitoring ◽  
Gap Analysis ◽  
Scientometric Analysis ◽  
Construction Site ◽  
Site Management ◽  
Research Gaps ◽  
Content Type ◽  
Safety Warning

Purpose 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. Design/methodology/approach 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. Findings 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. Practical implications 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. Originality/value 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 Development of Construction Management System on Architect View in Our Country

2011 ◽  
Vol 368-373 ◽  
pp. 3069-3073
Author(s):  
Sheng Hui Chen ◽  
Hui Min Li ◽  
Xin Ma
Keyword(s):  
Construction Management ◽  
Management System ◽  
Project Manager ◽  
Building Construction ◽  
Construction Site ◽  
Site Management ◽  
Credit System ◽  
Starting Point ◽  
Similarities And Differences ◽  
Centralized System

In order to improve construction site management, we make the architect’ position as the starting point for our research ,analyze the similarities and differences between the project manager and the architect and transform the traditional building construction management system from centralized system into flat -like system. Furthermore, we propose that the implementation of the system must be assisted with the construction of credit system and the establishment and implementation of personal practice insurance system.

scholarly journals Deep learning for detecting distresses in buildings and pavements: a critical gap analysis

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Faris Elghaish ◽  
Sandra T. Matarneh ◽  
Saeed Talebi ◽  
Soliman Abu-Samra ◽  
Ghazal Salimi ◽  
...  
Keyword(s):  
Deep Learning ◽  
Crack Detection ◽  
Gap Analysis ◽  
Network Models ◽  
Structure Type ◽  
Neural Network Models ◽  
Content Type ◽  
Critical Gap ◽  
Inspection Process ◽  
Structured Analysis

Purpose The massive number of pavements and buildings coupled with the limited inspection resources, both monetary and human, to detect distresses and recommend maintenance actions lead to rapid deterioration, decreased service life, lower level of service and increased community disruption. Therefore, this paper aims at providing a state-of-the-art review of the literature with respect to deep learning techniques for detecting distress in both pavements and buildings; research advancements per asset/structure type; and future recommendations in deep learning applications for distress detection. Design/methodology/approach A critical analysis was conducted on 181 papers of deep learning-based cracks detection. A structured analysis was adopted so that major articles were analyzed according to their focus of study, used methods, findings and limitations. Findings The utilization of deep learning to detect pavement cracks is advanced compared to assess and evaluate the structural health of buildings. There is a need for studies that compare different convolutional neural network models to foster the development of an integrated solution that considers the data collection method. Further research is required to examine the setup, implementation and running costs, frequency of capturing data and deep learning tool. In conclusion, the future of applying deep learning algorithms in lieu of manual inspection for detecting distresses has shown promising results. Practical implications The availability of previous research and the required improvements in the proposed computational tools and models (e.g. artificial intelligence, deep learning, etc.) are triggering researchers and practitioners to enhance the distresses’ inspection process and make better use of their limited resources. Originality/value A critical and structured analysis of deep learning-based crack detection for pavement and buildings is conducted for the first time to enable novice researchers to highlight the knowledge gap in each article, as well as building a knowledge base from the findings of other research to support developing future workable solutions.

Exploring challenges to ICT utilisation in construction site management

2018 ◽  
Vol 18 (3) ◽  
pp. 321-349 ◽  
Author(s):  
Aghaegbuna Obinna U. Ozumba ◽  
Winston Shakantu
Keyword(s):  
South Africa ◽  
Information And Communication Technologies ◽  
Qualitative Data ◽  
National Level ◽  
Management Process ◽  
Construction Site ◽  
Site Management ◽  
Content Type ◽  
Technology Knowledge ◽  
Ict Adoption

Purpose The purpose of this paper is to explore the nature and occurrence, and peculiarities and dynamics, if any, of perceived challenges to the adoption of information and communication technologies (ICT) in construction site management; using South Africa as context for empirical study. Design/methodology/approach Literature on the constraints to technology transfer and ICT adoption in construction is used as basis for the study. A national survey of registered persons in South Africa was used to collect qualitative data. A robust multi-stepped analytical approach was used to derive results. Findings Findings suggest appreciable similarity between literature and primary data, in types of individual challenges and their categories. Lack of technology and management support, and knowledge and information related issues, are relatively more prevalent in site management. There is a fair level of commonality in perception of technical barriers among the various categories of respondents who are active in site management. However, project managers seem to be more sensitive to some inhibiting factors, more than other respondent groups. Research limitations/implications Inherent limitations of survey strategy were experienced, but highly qualitative data were collected at a national level. The study highlights the range of barriers to ICT in site management, and compounding effects of technology-, knowledge- and management-related constraints. Practical implications The possibility of knowledge-based factors remotely manifesting in other categories was highlighted. There is need to consider all challenges in planning for ICT in projects, and prioritise technology-, knowledge- and management-related challenges. A classification for exploring challenges to ICT in the site management process is also proposed. Social implications Appreciable paucity remains in research focused on ICT in the site management process, as opposed to the popular operations/application focus of IT/ICT studies. Furthermore, there is still scarcity of ICT research in Africa, with regard to the built environment and specifically site management. Originality/value This study contributes to research in ICT innovation adoption in the construction industry, by developing a better understanding of the dynamics of perceived challenges to ICT adoption in the site management process; according to types and classifications of challenges, and roles and age groups of stakeholders. The study further sets a baseline for future studies in this area by proposing a frame of categorisation that is focused on site management.

Digital skin of the construction site

2019 ◽  
Vol 26 (2) ◽  
pp. 184-223 ◽  
Author(s):  
Ruwini Edirisinghe
Keyword(s):  
Progress Monitoring ◽  
Safety Management ◽  
Hierarchical Classification ◽  
The Body ◽  
Acceptance Testing ◽  
Context Aware ◽  
Construction Site ◽  
Design Development ◽  
Content Type ◽  
The Future

PurposeThe future construction site will be pervasive, context aware and embedded with intelligence. The purpose of this paper is to explore and define the concept of the digital skin of the future smart construction site.Design/methodology/approachThe paper provides a systematic and hierarchical classification of 114 articles from both industry and academia on the digital skin concept and evaluates them. The hierarchical classification is based on application areas relevant to construction, such as augmented reality, building information model-based visualisation, labour tracking, supply chain tracking, safety management, mobile equipment tracking and schedule and progress monitoring. Evaluations of the research papers were conducted based on three pillars: validation of technological feasibility, onsite application and user acceptance testing.FindingsTechnologies learned about in the literature review enabled the envisaging of the pervasive construction site of the future. The paper presents scenarios for the future context-aware construction site, including the construction worker, construction procurement management and future real-time safety management systems.Originality/valueBased on the gaps identified by the review in the body of knowledge and on a broader analysis of technology diffusion, the paper highlights the research challenges to be overcome in the advent of digital skin. The paper recommends that researchers follow a coherent process for smart technology design, development and implementation in order to achieve this vision for the construction industry.

scholarly journals Change Detection in Unmanned Aerial Vehicle Images for Progress Monitoring of Road Construction

Buildings ◽  
2021 ◽  
Vol 11 (4) ◽  
pp. 150
Author(s):  
Dongyeob Han ◽  
Suk Bae Lee ◽  
Mihwa Song ◽  
Jun Sang Cho
Keyword(s):  
Deep Learning ◽  
Change Detection ◽  
Construction Projects ◽  
Progress Monitoring ◽  
Road Construction ◽  
Learning Model ◽  
Construction Technology ◽  
Construction Site ◽  
Time Series Images ◽  
Deep Learning Model

Currently, unmanned aerial vehicles are increasingly being used in various construction projects such as housing developments, road construction, and bridge maintenance. If a drone is used at a road construction site, elevation information and orthoimages can be generated to acquire the construction status quantitatively. However, the detection of detailed changes in the site owing to construction depends on visual video interpretation. This study develops a method for automatic detection of the construction area using multitemporal images and a deep learning method. First, a deep learning model was trained using images of the changing area as reference. Second, we obtained an effective application method by applying various parameters to the deep learning process. The application of the time-series images of a construction site to the selected deep learning model enabled more effective identification of the changed areas than the existing pixel-based change detection. The proposed method is expected to be very helpful in construction management by aiding in the development of smart construction technology.

Exploratory study of using unmanned aerial system imagery for construction site 3D mapping

2018 ◽  
Vol 18 (3) ◽  
pp. 301-320 ◽  
Author(s):  
Juliana Sampaio Álvares ◽  
Dayana Bastos Costa ◽  
Roseneia Rodrigues Santos de Melo
Keyword(s):  
Construction Management ◽  
Exploratory Study ◽  
Construction Projects ◽  
Digital Camera ◽  
3D Models ◽  
Unmanned Aerial System ◽  
3D Mapping ◽  
Construction Site ◽  
Content Type ◽  
Potential Use

Purpose The purpose of this paper is to present an exploratory study which aims to assess the potential use of 3D mapping of buildings and construction sites using unmanned aerial system (UAS) imagery for supporting the construction management tasks. Design/methodology/approach The case studies were performed in two different residential construction projects. The equipment used was a quadcopter equipped with digital camera and GPS that allow for the registry of geo-referenced images. The Pix4D Mapper and PhotoScan software were used to generate the 3D models. The study sought to examine three main constructs related to the 3D mapping developed: the easiness of development, the quality of the models in accordance with the proposed use and the usefulness and limitations of the mapping for construction management purposes. Findings The main contributions of this study include a better understanding of the development process of 3D mapping from UAS imagery, the potential uses of this mapping for construction management and the identification of barriers and benefits related to the application of these emerging technologies for the construction industry. Originality/value The importance of the study is related to the initiative to identify and evaluate the potential use of 3D mapping from UAS imagery, which can provide a 3D view of the construction site from different perspectives, for construction management tasks applications, trying to bring positive contributions to this knowledge area.

Automated activity and progress analysis based on non-monotonic reasoning of construction operations

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Karsten Winther Johansen ◽  
Rasmus Nielsen ◽  
Carl Schultz ◽  
Jochen Teizer
Keyword(s):  
Real Time ◽  
Construction Management ◽  
Abductive Reasoning ◽  
Sensor Data ◽  
Location Tracking ◽  
Construction Site ◽  
Domain Expert ◽  
Site Specific ◽  
Content Type ◽  
Location Sensing

PurposeReal-time location sensing (RTLS) systems offer a significant potential to advance the management of construction processes by potentially providing real-time access to the locations of workers and equipment. Many location-sensing technologies tend to perform poorly for indoor work environments and generate large data sets that are somewhat difficult to process in a meaningful way. Unfortunately, little is still known regarding the practical benefits of converting raw worker tracking data into meaningful information about construction project progress, effectively impeding widespread adoption in construction.Design/methodology/approachThe presented framework is designed to automate as many steps as possible, aiming to avoid manual procedures that significantly increase the time between progress estimation updates. The authors apply simple location tracking sensor data that does not require personal handling, to ensure continuous data acquisition. They use a generic and non-site-specific knowledge base (KB) created through domain expert interviews. The sensor data and KB are analyzed in an abductive reasoning framework implemented in Answer Set Programming (extended to support spatial and temporal reasoning), a logic programming paradigm developed within the artificial intelligence domain.FindingsThis work demonstrates how abductive reasoning can be applied to automatically generate rich and qualitative information about activities that have been carried out on a construction site. These activities are subsequently used for reasoning about the progress of the construction project. Our framework delivers an upper bound on project progress (“optimistic estimates”) within a practical amount of time, in the order of seconds. The target user group is construction management by providing project planning decision support.Research limitations/implicationsThe KB developed for this early-stage research does not encapsulate an exhaustive body of domain expert knowledge. Instead, it consists of excerpts of activities in the analyzed construction site. The KB is developed to be non-site-specific, but it is not validated as the performed experiments were carried out on one single construction site.Practical implicationsThe presented work enables automated processing of simple location tracking sensor data, which provides construction management with detailed insight into construction site progress without performing labor-intensive procedures common nowadays.Originality/valueWhile automated progress estimation and activity recognition in construction have been studied for some time, the authors approach it differently. Instead of expensive equipment, manually acquired, information-rich sensor data, the authors apply simple data, domain knowledge and a logical reasoning system for which the results are promising.

scholarly journals Relationships between Social Support, Social Status Perception, Social Identity, Work Stress, and Safety Behavior of Construction Site Management Personnel

2021 ◽  
Vol 13 (6) ◽  
pp. 3184
Author(s):  
Ying-Hua Huang ◽  
Chen-Yu Sung ◽  
Wei Tong Chen ◽  
Shu-Shun Liu
Keyword(s):  
Social Support ◽  
Professional Identity ◽  
Social Status ◽  
Work Stress ◽  
Structural Equation ◽  
Equation Modeling ◽  
Construction Site ◽  
Site Management ◽  
Safety Behavior ◽  
Management Personnel

The occupational injury death rate and mortality ratio owing to cerebrovascular and cardiovascular diseases in the construction industry are the highest among all industries in Taiwan. Reducing work stress and improving safety behavior is a must for reducing occupational disasters and diseases. Construction site management personnel’s safety behavior is an important paradigm for construction workers. This study explored the relationships among work stress, safety behavior, professional identity, social status perception, and social support for construction site management personnel by using structural equation modeling (SEM). The results indicated that low work stress can lead to favorable safety behavior. Greater company support, family support, and professional identity reduce work stress. Social status perception negatively influences work stress indirectly through the mediation of professional identity. The results revealed that construction site management personnel working within an exempt employee system (i.e., no overtime pay and compensatory leave) exhibited a significantly higher effort/reward ratio than those without this system. Gender, headquarter location, and site location also significantly influenced the on-site management personnel’s effort/reward ratio.

scholarly journals A Review of Recent Deep Learning Approaches in Human-Centered Machine Learning

Sensors ◽  
2021 ◽  
Vol 21 (7) ◽  
pp. 2514
Author(s):  
Tharindu Kaluarachchi ◽  
Andrew Reis ◽  
Suranga Nanayakkara
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Review Paper ◽  
Learning Systems ◽  
Learning Approaches ◽  
Application Development ◽  
Research Gaps ◽  
Domain Experts ◽  
Working Definition ◽  
Real World Application

After Deep Learning (DL) regained popularity recently, the Artificial Intelligence (AI) or Machine Learning (ML) field is undergoing rapid growth concerning research and real-world application development. Deep Learning has generated complexities in algorithms, and researchers and users have raised concerns regarding the usability and adoptability of Deep Learning systems. These concerns, coupled with the increasing human-AI interactions, have created the emerging field that is Human-Centered Machine Learning (HCML). We present this review paper as an overview and analysis of existing work in HCML related to DL. Firstly, we collaborated with field domain experts to develop a working definition for HCML. Secondly, through a systematic literature review, we analyze and classify 162 publications that fall within HCML. Our classification is based on aspects including contribution type, application area, and focused human categories. Finally, we analyze the topology of the HCML landscape by identifying research gaps, highlighting conflicting interpretations, addressing current challenges, and presenting future HCML research opportunities.

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