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1 – 10 of 11
Article
Publication date: 1 August 2019

Sandra Matarneh, Mark Danso-Amoako, Salam Al-Bizri, Mark Gaterell and Rana Matarneh

The purpose of this study is to address challenges in the current information exchange process between building information modelling (BIM) and facilities management (FM) systems…

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Abstract

Purpose

The purpose of this study is to address challenges in the current information exchange process between building information modelling (BIM) and facilities management (FM) systems and to propose a workable solution. This study’s objective is to identify the information exchange requirements and to develop methods for seamless information flow between building information models and FM systems.

Design/methodology/approach

Data collection and analysis was based on an extensive literature review of similar studies followed by a questionnaire survey with a total of 112 participants and 2 focus groups with a total of 12 participants to validate the conceptual framework. The outputs of the survey analysis formed the background of the proposed framework to streamline information exchange process between building information models and FM systems.

Findings

The study findings form a foundation for enabling the integration of various data sources including building information models. Such integrated platforms will enable automated information exchange between the various data sources and FM systems. The study also provides key information requirements sources to complement the existing construction operations building information exchange information and to support standardization for information exchange process.

Originality/value

The contribution of this study is the identification of information exchange requirements and sources to enable seamless information flow between BIM and FM systems. The study findings will also lay the basis for research studies using the developed framework context to enable the identification of specific data outputs for FM systems inputs.

Article
Publication date: 16 December 2019

Sandra T. Matarneh, Mark Danso-Amoako, Salam Al-Bizri, Mark Gaterell and Rana T. Matarneh

This paper aims to identify a generic set of information requirements for facilities management (FM) systems, which should be included in BIM as-built models for efficient…

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Abstract

Purpose

This paper aims to identify a generic set of information requirements for facilities management (FM) systems, which should be included in BIM as-built models for efficient information exchange between BIM and FM systems, and to propose a process to identify, verify and collect the required information for use in FM systems during the project’s lifecycle.

Design/methodology/approach

Both qualitative and quantitative approaches were applied at different stages of the study’s sequential design. The collection and analysis of qualitative data was based on an extensive literature review of similar studies, standards, best practices and case study documentation. This was followed by a questionnaire survey of 191 FM practitioners in the UK. This formed the background of the third stage, which was the development of the information management process to streamline information exchange between building information models and FM systems.

Findings

The study identifies a generic list of information requirements of building information models to support FM systems. In addition, the study presents an information management process that generates a specific database for FM systems using an open data format.

Originality/value

The existing literature focuses on specific building types (educational buildings) or specific information requirements related to particular systems (mechanical systems). The existing standards, guidelines and best practices focus on the information requirements to support the operations and maintenance (O&M) phase in general. This study is different from previous studies because it develops a set of specific information requirements for building information models to support FM systems. FM organisations and owners can use the proposed list of information requirements as a base to generate specific data output for their FM systems’ input, to decrease the redundant activity of manual data entry and focus their efforts on key activities.

Open Access
Article
Publication date: 1 December 2023

Lina Gharaibeh, Sandra Matarneh, Kristina Eriksson and Björn Lantz

This study aims to present a state-of-the-art review of building information modelling (BIM) in the Swedish construction practice with a focus on wood construction. It focuses on…

Abstract

Purpose

This study aims to present a state-of-the-art review of building information modelling (BIM) in the Swedish construction practice with a focus on wood construction. It focuses on examining the extent, maturity and actual practices of BIM in the Swedish wood construction industry, by analysing practitioners’ perspectives on the current state of BIM and its perceived benefits.

Design/methodology/approach

A qualitative approach was selected, given the study’s exploratory character. Initially, an extensive review was undertaken to examine the current state of BIM utilisation and its associated advantages within the construction industry. Subsequently, empirical data were acquired through semi-structured interviews featuring open-ended questions, aimed at comprehensively assessing the prevailing extent of BIM integration within the Swedish wood construction sector.

Findings

The research concluded that the wood construction industry in Sweden is shifting towards BIM on different levels, where in some cases, the level of implementation is still modest. It should be emphasised that the wood construction industry in Sweden is not realising the full potential of BIM. The industry is still using a combination of BIM and traditional methods, thus, limiting the benefits that full BIM implementation could offer the industry.

Originality/value

This study provided empirical evidence on the current perceptions and state of practice of the Swedish wood construction industry regarding BIM maturity.

Details

Construction Innovation , vol. 24 no. 7
Type: Research Article
ISSN: 1471-4175

Keywords

Article
Publication date: 15 March 2022

Lina Gharaibeh, Kristina Maria Eriksson, Bjorn Lantz, Sandra Matarneh and Faris Elghaish

The wood construction industry has been described as slow in adapting efficiency-increasing activities in its operations and supply chain. The industry is still facing challenges…

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Abstract

Purpose

The wood construction industry has been described as slow in adapting efficiency-increasing activities in its operations and supply chain. The industry is still facing challenges related to digitalization, such as fragmentation, poor traceability and lack of real-time information. This study evaluates the status of digitalization in construction supply chains by thematically analyzing the existing literature and mapping research trends.

Design/methodology/approach

A review of the key literature from 2016 to 2021 was performed. The results highlight various technologies and their applications within supply chains and identify research gaps, especially between theoretical frameworks and actual implementation using a scientometric-thematic analysis.

Findings

This paper provides a conceptual framework to further aid researchers in exploring the current trends in Supply Chain 4.0 and its applications in the wood construction industry compared to other more advanced industries. Suggested directions for future research in the wood construction Supply Chain 4.0 are outlined.

Originality/value

The existing literature still lacks a comprehensive review of the potential of a digitalized supply chain, especially in the construction industry. This framework is pivotal to continue explaining and observing the best ways to accelerate and implement Supply Chain 4.0 practices for digitalized supply chain management (SCM) while focusing specifically on the wood construction industry. The literature review results will help develop a comprehensive framework for future research direction to create a clearer vision of the current state of digitalization in supply chains and focus on the wood construction supply chain, thus, fully achieving the benefits of Supply Chain 4.0 in the wood construction industry.

Details

Smart and Sustainable Built Environment, vol. 13 no. 1
Type: Research Article
ISSN: 2046-6099

Keywords

Article
Publication date: 27 May 2020

Lina Ghazi Gharaibeh, Sandra T. Matarneh, Mazen Arafeh and Ghaleb Sweis

The problem of design changes in the construction industry is common worldwide, and the Jordanian market is no exception. The purpose of this paper is to identify the factors…

Abstract

Purpose

The problem of design changes in the construction industry is common worldwide, and the Jordanian market is no exception. The purpose of this paper is to identify the factors causing design changes in construction projects in Jordan in both the public and private sectors. Furthermore, this research will examine the effect of these factors on project's performance during the construction phase.

Design/methodology/approach

This research commences by identifying the factors causing design changes in construction projects worldwide through an intensive literature review. The identified factors were then filtered to those applicable to the Jordanian construction market based on the results obtained from a questionnaire survey and real case construction projects. In total, 252 professionals from the Jordanian construction industry and 10 completed and/or ongoing construction projects in different parts of Jordan were compared.

Findings

The results find that the top major factors affecting design changes are owner's requirements; design errors and omissions and value engineering. The research also studies and documents the impacts of design changes on project cost, schedule and quality.

Originality/value

The results obtained from this research will assist the construction professionals representing owners, consultants and contractors in applying control measures to minimize the occurrence of the identified factors causing design changes and to mitigate their sever impacts on projects in terms of cost, schedule and quality.

Details

International Journal of Productivity and Performance Management, vol. 70 no. 4
Type: Research Article
ISSN: 1741-0401

Keywords

Article
Publication date: 9 November 2021

Faris Elghaish, Sandra T. Matarneh, Saeed Talebi, Soliman Abu-Samra, Ghazal Salimi and Christopher Rausch

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…

Abstract

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.

Details

Construction Innovation , vol. 22 no. 3
Type: Research Article
ISSN: 1471-4175

Keywords

Content available
Article
Publication date: 2 June 2022

Faris Elghaish, Sandra T. Matarneh, David John Edwards, Farzad Pour Rahimian, Hatem El-Gohary and Obuks Ejohwomu

This paper aims to explore the emerging relationship between Industry 4.0 (I4.0) digital technologies (e.g. blockchain, Internet of Things (IoT) and artificial intelligence (AI)…

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Abstract

Purpose

This paper aims to explore the emerging relationship between Industry 4.0 (I4.0) digital technologies (e.g. blockchain, Internet of Things (IoT) and artificial intelligence (AI)) and the construction industry’s gradual transition into a circular economy (CE) system to foster the adoption of circular economy in the construction industry.

Design/methodology/approach

A critical and thematic analysis conducted on 115 scientific papers reveals a noticeable growth in adopting digital technologies to leverage a CE system. Moreover, a conceptual framework is developed to show the interrelationship between different I4.0 technologies to foster the implantation of CE in the construction industry.

Findings

Most of the existing bodies of research provide conceptual solutions rather than developing workable applications and the future of smart cities. Moreover, the coalescence of different technologies is highly recommended to enable tracking of building assets’ and components’ (e.g. fixtures and fittings and structural components) performance, which enables users to optimize the salvage value of components reusing or recycling them just in time and extending assets’ operating lifetime. Finally, circular supply chain management must be adopted for both new and existing buildings to realise the industry's CE ambitions. Hence, further applied research is required to foster CE adoption for existing cities and infrastructure that connects them.

Originality/value

This paper investigates the interrelationships between most emerging digital technologies and circular economy and concludes with the development of a conceptual digital ecosystem to integrate IoT, blockchain and AI into the operation of assets to direct future practical research applications

Article
Publication date: 28 December 2021

Faris Elghaish, Sandra T. Matarneh and Mohammad Alhusban

The digital construction transformation requires using emerging digital technology such as deep learning to automate implementing tasks. Therefore, this paper aims to evaluate the…

Abstract

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.

Article
Publication date: 8 September 2020

Faris Elghaish, Sandra Matarneh, Saeed Talebi, Michail Kagioglou, M. Reza Hosseini and Sepehr Abrishami

In this study, a critical literature review was utilized in order to provide a clear review of the relevant existing studies. The literature was analyzed using the meta-synthesis…

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Abstract

Purpose

In this study, a critical literature review was utilized in order to provide a clear review of the relevant existing studies. The literature was analyzed using the meta-synthesis technique to evaluate and integrate the findings in a single context.

Design/methodology/approach

Digital transformation in construction requires employing a wide range of various technologies. There is significant progress of research in adopting technologies such as unmanned aerial vehicles (UAVs), also known as drones, and immersive technologies in the construction industry over the last two decades. The purpose of this research is to assess the current status of employing UAVs and immersive technologies toward digitalizing the construction industry and highlighting the potential applications of these technologies, either individually or in combination and integration with each other.

Findings

The key findings are: (1) UAVs in conjunction with 4D building information modeling (BIM) can be used to assess the project progress and compliance checking of geometric design models, (2) immersive technologies can be used to enable controlling construction projects remotely, applying/checking end users’ requirements, construction education and team collaboration.

Practical implications

A detailed discussion around the application of UAVs and immersive technologies is provided. This is expected to support gaining an in-depth understanding of the practical applications of these technologies in the industry.

Originality/value

The review contributes a needed common basis for capturing progress made in UAVs and immersive technologies to date and assessing their impact on construction projects. Moreover, this paper opens a new horizon for novice researchers who will conduct research toward digitalized construction.

Details

Smart and Sustainable Built Environment, vol. 10 no. 3
Type: Research Article
ISSN: 2046-6099

Keywords

Article
Publication date: 16 August 2021

Faris Elghaish, Saeed Talebi, Essam Abdellatef, Sandra T. Matarneh, M. Reza Hosseini, Song Wu, Mohammad Mayouf, Aso Hajirasouli and The-Quan Nguyen

This paper aims to Test the capabilities/accuracies of four deep learning pre trained convolutional neural network (CNN) models to detect and classify types of highway cracks, as…

Abstract

Purpose

This paper aims to Test the capabilities/accuracies of four deep learning pre trained convolutional neural network (CNN) models to detect and classify types of highway cracks, as well as developing a new CNN model to maximize the accuracy at different learning rates.

Design/methodology/approach

A sample of 4,663 images of highway cracks were collected and classified into three categories of cracks, namely, “vertical cracks,” “horizontal and vertical cracks” and “diagonal cracks,” subsequently, using “Matlab” to classify the sample to training (70%) and testing (30%) to apply the four deep learning CNN models and compute their accuracies. After that, developing a new deep learning CNN model to maximize the accuracy of detecting and classifying highway cracks and testing the accuracy using three optimization algorithms at different learning rates.

Findings

The accuracies result of the four deep learning pre-trained models are above the averages between top-1 and top-5 and the accuracy of classifying and detecting the samples exceeded the top-5 accuracy for the pre-trained AlexNet model around 3% and by 0.2% for the GoogleNet model. The accurate model here is the GoogleNet model as the accuracy is 89.08% and it is higher than AlexNet by 1.26%. While the computed accuracy for the new created deep learning CNN model exceeded all pre-trained models by achieving 97.62% at a learning rate of 0.001 using Adam’s optimization algorithm.

Practical implications

The created deep learning CNN model will enable users (e.g. highway agencies) to scan a long highway and detect types of cracks accurately in a very short time compared to traditional approaches.

Originality/value

A new deep learning CNN-based highway cracks detection was developed based on testing four pre-trained CNN models and analyze the capabilities of each model to maximize the accuracy of the proposed CNN.

Details

Journal of Engineering, Design and Technology , vol. 20 no. 4
Type: Research Article
ISSN: 1726-0531

Keywords

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