Applying Regression Analysis to Predict and Classify Construction Cycle Time

Siu, Ming-Fung Francis; Ekyalimpa, Ronald; Lu, Ming; AbouRizk, Simaan M.

doi:10.1061/9780784413029.084

Cited by 5 publications

(1 citation statement)

References 11 publications

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“…Regression analysis has been widely applied in the area of construction engineering and management (CEM). Applications include production cycle time forecasting (Siu et al 2013) productivity prediction (Aziz 2009;Chong et al 2005) and cost estimation (Du and Bormann 2012;Jafarzadeh et al 2013). In the geotechnical engineering area, regression analysis has also been applied to estimate soil vertical displacement (Chiru-Danzer and Christopher 2000), soil liquefaction probability (Lai et al 2006;Liao et al 1988) and displacement of spread footings (Stuedlein and Holtz 2013).…”

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confidence: 99%

Developing a Statistical Model for Building Settlement Prediction

Günhan

2014 ASEE Annual Conference &Amp; Exposition Proceedings

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Texas is well known for its complex soil conditions and corresponding building settlement issues. As a result, it is important to introduce to the students of Construction Science and Management (CSM) program the basic building settlement concepts including causes, consequences and treatments. One important topic is how the design of a building (e.g., height, weight and foundation types) affect building settlement on various types of soil. This requires the ability to calculate building settlement. However, theoretical models that are used in the geotechnical engineering area are too complicated to non-engineering students. This paper develops a statistical model to predict the building settlement. An interactive education tool has also been developed based on the statistical model, which is much easier for CSM students to explore the relationship between building & soil characteristics and building final settlement. The model and the tool will be tailored into the Construction Materials and Methods course at the University of Texas at San Antonio as a part of the soil and foundation topic.

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Developing a Statistical Model for Building Settlement Prediction

Günhan

2014 ASEE Annual Conference &Amp; Exposition Proceedings

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Big Data in the construction industry: A review of present status, opportunities, and future trends

Bilal

Oyedele

Qadir

et al. 2016

Advanced Engineering Informatics

527

307

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The ability to process large amounts of data and 1 to extract useful insights from data has revolutionised society. 2 This phenomenon-dubbed as Big Data-has applications for a 3 wide assortment of industries, including the construction industry. 4 The construction industry already deals with large volumes of 5 heterogeneous data; which is expected to increase exponentially 6 as technologies such as sensor networks and the Internet of 7 Things are commoditized. In this paper, we present a detailed 8 survey of the literature, investigating the application of Big Data 9 techniques in the construction industry. We reviewed related 10 works published in the databases of American Association of 11 Civil Engineers (ASCE), Institute of Electrical and Electronics 12 Engineers (IEEE), Association of Computing Machinery (ACM), 13 and Elsevier Science Direct Digital Library. While the application 14 of data analytics in the construction industry is not new, the 15 adoption of Big Data technologies in this industry remains at a 16 nascent stage and lags the broad uptake of these technologies in 17 other fields. To the best of our knowledge, there is currently no 18 comprehensive survey of Big Data techniques in the context of 19 the construction industry. This paper fills the void and presents a 20 wide-ranging interdisciplinary review of literature of fields such 21 as statistics, data mining and warehousing, machine learning, and 22 Big Data Analytics in the context of the construction industry. 23 We discuss the current state of adoption of Big Data in the 24 construction industry and discuss the future potential of such 25 technologies across the multiple domain-specific sub-areas of the 26 construction industry. We also propose open issues and directions 27 for future work along with potential pitfalls associated with Big 28 Data adoption in the industry. 29 I. INTRODUCTION 30 The world is currently inundated with data, with fast advanc-31 ing technology leading to its steady increase. Today, companies 32 deal with petabytes (10 15 bytes) of data. Google processes 33 above 24 petabytes of data per day [1], while Facebook gets 34 more than 10 million photos per hour [1]. The glut of data 35 increased in 2012 is approximately 2.5 quintillion (10 18) bytes 36 per day [2]. This data growth brings significant opportunities 37 to scientists for identifying useful insights and knowledge. 38 Arguably, the accessibility of data can improve the status 39 quo in various fields by strengthening existing statistical and 40 algorithmic methods [3], or by even making them redundant 41 [4]. 42 The construction industry is not an exception to the per-43 vasive digital revolution. The industry is dealing with sig-44 nificant data arising from diverse disciplines throughout the 45 life cycle of a facility. Building Information Modelling (BIM) 46 is envisioned to capture multi-dimensional CAD information 47 systematically for supporting multidisciplinary collaboration 48 among the stakeholders [5]. BIM data is typically 3D ge-49 ometric enc...

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Generalized linear model-based data analytic approach for construction equipment management

Yang

Zhong

2023

Advanced Engineering Informatics

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Applying Regression Analysis to Predict and Classify Construction Cycle Time

Cited by 5 publications

References 11 publications

Developing a Statistical Model for Building Settlement Prediction

Developing a Statistical Model for Building Settlement Prediction

Big Data in the construction industry: A review of present status, opportunities, and future trends

Generalized linear model-based data analytic approach for construction equipment management

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