Machine learning techniques applied to construction: A hybrid bibliometric analysis of advances and future directions

García, José; Villavicencio, Gabriel; Altimiras, Francisco; Crawford, Broderick; Soto, Ricardo; Minatogawa, Vinicius; Franco, Matheus; Martí, José V.; Piqueras, Víctor Yepes

doi:10.1016/j.autcon.2022.104532

Cited by 25 publications

(13 citation statements)

References 63 publications

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“…, 2016). The adoption of data analytics in BIM-related projects has gained substantial traction as more data is generated and managed within BIM (Garcia et al. , 2022).…”

Section: Results Of the Review: Towards A Status Review And A Researc...mentioning

confidence: 99%

“…Notable exceptions were the works that did consider data more broadly; though their focus was less concerned with project delivery data, they were more concerned with the data involved in models of the project output, such as BIM (Whyte, 2019;Whyte et al, 2016). The adoption of data analytics in BIM-related projects has gained substantial traction as more data is generated and managed within BIM (Garcia et al, 2022). Integrating artificial intelligence into BIM requires careful data analytics consideration such as data collection, data preprocessing, feature selection, model development, etc.…”

Section: Data Analyticsmentioning

confidence: 99%

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Data analytics for project delivery: unlocking the potential of an emerging field

Shen,

Brookes,

Lattuf Flores

et al. 2024

IJMPB

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PurposeIn recent years, there has been a growing interest in the potential of data analytics to enhance project delivery. Yet many argue that its application in projects is still lagging behind other disciplines. This paper aims to provide a review of the current use of data analytics in project delivery encompassing both academic research and practice to accelerate current understanding and use this to formulate questions and goals for future research.Design/methodology/approachWe propose to achieve the research aim through the creation of a systematic review of the status of data analytics in project delivery. Fusing the methodology of integrative literature review with a recently established practice to include both white and grey literature amounts to an approach tailored to the state of the domain. It serves to delineate a research agenda informed by current developments in both academic research and industrial practice.FindingsThe literature review reveals a dearth of work in both academic research and practice relating to data analytics in project delivery and characterises this situation as having “more gap than knowledge.” Some work does exist in the application of machine learning to predicting project delivery though this is restricted to disparate, single context studies that do not reach extendible findings on algorithm selection or key predictive characteristics. Grey literature addresses the potential benefits of data analytics in project delivery but in a manner reliant on “thought-experiments” and devoid of empirical examples.Originality/valueBased on the review we articulate a research agenda to create knowledge fundamental to the effective use of data analytics in project delivery. This is structured around the functional framework devised by this investigation and highlights both organisational and data analytic challenges. Specifically, we express this structure in the form of an “onion-skin” model for conceptual structuring of data analytics in projects. We conclude with a discussion about if and how today’s project studies research community can respond to the totality of these challenges. This paper provides a blueprint for a bridge connecting data analytics and project management.

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“…, 2016). The adoption of data analytics in BIM-related projects has gained substantial traction as more data is generated and managed within BIM (Garcia et al. , 2022).…”

Section: Results Of the Review: Towards A Status Review And A Researc...mentioning

confidence: 99%

Section: Data Analyticsmentioning

confidence: 99%

Data analytics for project delivery: unlocking the potential of an emerging field

Shen,

Brookes,

Lattuf Flores

et al. 2024

IJMPB

View full text Add to dashboard Cite

show abstract

“…Traditional machine vision systems, foundational to industrial processes, reveal their limitations in these dynamic settings, characterized by unpredictable variables and ceaseless activity. These systems often require extensive parameter tuning and can falter, especially in uncontrolled environments marked by fluctuating lighting conditions and unexpected defects [1][2][3].…”

Section: Introductionmentioning

confidence: 99%

“…Alongside technological challenges, the construction sector faces an escalating demand for rigorous quality inspections and a diminishing pool of human inspectors. Even seemingly minor defects can tarnish a brand's reputation and compromise functional aesthetics [3].…”

Section: Introductionmentioning

confidence: 99%

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Image-Enhanced U-Net: Optimizing Defect Detection in Window Frames for Construction Quality Inspection

Vasquez,

Furuhata,

Shimada

2023

Buildings

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Ensuring the structural integrity of window frames and detecting subtle defects, such as dents and scratches, is crucial for maintaining product quality. Traditional machine vision systems face challenges in defect identification, especially with reflective materials and varied environments. Modern machine and deep learning (DL) systems hold promise for post-installation inspections but face limitations due to data scarcity and environmental variability. Our study introduces an innovative approach to enhance DL-based defect detection, even with limited data. We present a comprehensive window frame defect detection framework incorporating optimized image enhancement, data augmentation, and a core U-Net model. We constructed five datasets using cell phones and the Spot Robot for autonomous inspection, evaluating our approach across various scenarios and lighting conditions in real-world window frame inspections. Our results demonstrate significant performance improvements over the standard U-Net model, with a notable 7.43% increase in the F1 score and 15.1% in IoU. Our approach enhances defect detection capabilities, even in challenging real-world conditions. To enhance the generalizability of this study, it would be advantageous to apply its methodology across a broader range of diverse construction sites.

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Physics Informed Machine Learning (PIML) for Design, Management and Resilience-Development of Urban Infrastructures: A Review

Chew,

He,

Zhang

2024

Arch Computat Methods Eng

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Machine learning techniques applied to construction: A hybrid bibliometric analysis of advances and future directions

Cited by 25 publications

References 63 publications

Data analytics for project delivery: unlocking the potential of an emerging field

Data analytics for project delivery: unlocking the potential of an emerging field

Image-Enhanced U-Net: Optimizing Defect Detection in Window Frames for Construction Quality Inspection

Physics Informed Machine Learning (PIML) for Design, Management and Resilience-Development of Urban Infrastructures: A Review

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