Achieving Sustainable Structural Steel Design by Estimating Fabrication Labor Cost Based on BIM Data

Mohsenijam, Arash; Lu, Ming

doi:10.1016/j.proeng.2016.04.056

Cited by 8 publications

(5 citation statements)

References 12 publications

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“…Competitive pressure is measured in terms of peer organizations’ competitiveness by early identification of cost estimation, material supplies required, labour cost estimation (Mohsenijam and Lu, 2016) and readjustments of business processes (Song et al , 2017). The items for competitive pressure include good reputations in the industry, benefits gained using BIM, demand from customers and external actors’ support.…”

Section: Methodsmentioning

confidence: 99%

Examining the effect of interoperability factors on building information modelling (BIM) adoption in Malaysia

Ahmed

Shehzad²,

Khurshid³

et al. 2022

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Purpose Building information modelling (BIM) has transformed the traditional practices of the Architecture, Engineering and Construction (AEC) industry. BIM creates a collaborative digital representation of built environment data. Competitive advantage can be achieved with collaborative project delivery and rich information modelling. Despite the abundant benefits, BIM’s adoption in the AEC is susceptible to confrontation. A substantial impediment to BIM adoption often cited is data interoperability. Other facets of interoperability got limited attention. Other academic areas, including information systems, discuss the interoperability construct ahead of data interoperability. These interoperability factors have yet to be surveyed in the AEC industry. This study aims to investigate the effect of interoperability factors on BIM adoption and develop a comprehensive BIM adoption model. Design/methodology/approach The theoretical foundations of the proposed model are based on the European interoperability framework (EIF) and technology, organization, environment framework (TOE). Quantitative data collection from construction firms is gathered. The model has been thoroughly examined and validated using partial least squares structural equation modelling in SmartPLS software. Findings The study’s findings indicate that relative advantage, top management support, government support, organizational readiness and regulation support are determinants of BIM adoption. Financial constraints, complexity, lack of technical interoperability, semantic interoperability, organizational interoperability and uncertainty are barriers to BIM adoption. However, compatibility, competitive pressure and legal interoperability do not affect BIM adoption. Practical implications Finally, this study provides recommendations containing the essential technological, organizational, environmental and interoperability factors that AEC stakeholders can address to enhance BIM adoption. Originality/value To the best of the authors’ knowledge, this paper is one of the first studies to combine TOE and EIF in a single research model. This research provides empirical evidence for using the proposed model as a guide to promoting BIM adoption. As a result, the highlighted determinants can assist organizations in developing and executing successful policies that support BIM adoption in the AEC industry.

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Section: Methodsmentioning

confidence: 99%

Examining the effect of interoperability factors on building information modelling (BIM) adoption in Malaysia

Ahmed

Shehzad²,

Khurshid³

et al. 2022

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show abstract

“…Some of the investigations related to the design and construction of steel structures include the use of BIM data to achieve a sustainable steel design by estimating labor cost [23] and the use of BIM-based data mining to estimate job man-hour requirements in structural steel fabrication [24]. However, most investigations dealt with design and sustainability related topics, such as the development of a BIM extension for the sustainability appraisal of conceptual steel design [25], using BIM technology for the design of steel bridges [26], [27], BIM based connection design in steel structures [28], the design of steel box girders [29], and others.…”

Section: Structural Steel Construction-bim Integrationmentioning

confidence: 99%

Leveraging advances in automation to benefit the construction industry in the structural steel sector

Elhouar

Terreno

2022

IOP Conf. Ser.: Mater. Sci. Eng.

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The global structural steel market was reported to be valued at $96 Billion in 2018, and according to some projections, it will amount to about $140 Billion by 2025. This means that the structural steel industry is expected to have a Compound Annual Growth Rate (CAGR) of about 5.4% over this six-year period. Consequently, this sector is set to perform better than the construction industry as a whole, which is projected to have a CAGR of 4.2% over approximately the same period. This projection represents good news for the structural steel industry but it also magnifies some of the challenges that the industry has been facing or has faced in the past. For example, the US Bureau of Labor Statistics projects that overall employment of ironworkers will see an 11% growth from 2018 to 2028, which is much faster than the average growth for all occupations. On the other side of the equation, the industry is facing some challenges that may make it difficult for it to meet the projected demand. For example, there is a very well documented shortage in the construction labor force, and this shortage is already causing problems and driving construction costs upward. Moreover, there is an expectation of increased accountability towards the environment while dealing with generally larger and more complex projects. In this paper, the authors propose that increased automation may be the most effective way to solve the labor shortage problem in the steel construction sector. An overview of advancements in related automation technology is first presented, followed by an analysis of the areas of the industry that may benefit from an increased use or the introduction of automation. The authors conclude by presenting a set of recommendations for adding more automation in the production, servicing, fabrication, and erection of structural steel.

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“…BIM technology offers high-quality data through the BIM models [24], and makes it possible to gather and classify information [48,49] specific to different disciplines in a single 3D model. In steel construction, the information extracted from these models can be used for constructability and quantitative estimation [48,50]. This information can also be used for cost and time estimation [51].…”

Section: Bim For Data Extractionmentioning

confidence: 99%

Bim Machine Learning and Design Rules to Improve the Assembly Time in Steel Construction Projects

et al. 2021

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Integrating the knowledge and experience of fabrication during the design phase can help reduce the cost and duration of steel construction projects. Building Information Modeling (BIM) are technologies and processes that reduce the cost and duration of construction projects by integrating parametric digital models as support of information. These models can contain information about the performance of previous projects and allow a classification by linear regression of design criteria with a high impact on the duration of the fabrication. This paper proposes a quantitative approach that applies linear regressions on previous projects’ BIM models to identify some design rules and production improvement points. A case study applied on 55,444 BIM models of steel joists validates this approach. This case study shows that the camber, the weight of the structure, and its reinforced elements greatly influence the fabrication time of the joists. The approach developed in this article is a practical case where machine learning and BIM models are used rather than interviews with professionals to identify knowledge related to a given steel structure fabrication system.

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Achieving Sustainable Structural Steel Design by Estimating Fabrication Labor Cost Based on BIM Data

Cited by 8 publications

References 12 publications

Examining the effect of interoperability factors on building information modelling (BIM) adoption in Malaysia

Examining the effect of interoperability factors on building information modelling (BIM) adoption in Malaysia

Leveraging advances in automation to benefit the construction industry in the structural steel sector

Bim Machine Learning and Design Rules to Improve the Assembly Time in Steel Construction Projects

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