Using Hybrid Artificial Intelligence Optimization Method to Predict Construction Labour Productivity

Efkan Efekan; Tolga Celik; Onur B. Tokdemir

doi:10.1007/978-3-031-32511-3_166

Using Hybrid Artificial Intelligence Optimization Method to Predict Construction Labour Productivity

Efkan Efekan^*, Tolga Celik, Onur B. Tokdemir

^*Corresponding author for this work

Department of Civil Engineering

Eastern Mediterranean University

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

Precise estimation of productivity is crucial for construction managers in making timely decisions. The aim is to propose a novel theoretical approach for predicting and optimizing Construction Labour Productivity (CLP). Numerous factors affecting CLP are the main challenge in modeling labor productivity. Accurate CLP prediction is required for effective decision-making before and during project execution. This article will explain the importance of artificial intelligence-based inference models on construction productivity and why hybrid embedded feature selection models, a new approach to increasing construction productivity, should be used more in integration with AI-based inference models.

Original language	English
Title of host publication	Building for the Future
Subtitle of host publication	Durable, Sustainable, Resilient - Proceedings of the Symposium 2023 - Volume 2
Editors	Alper Ilki, Derya Çavunt, Yavuz Selim Çavunt
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	1624-1630
Number of pages	7
ISBN (Print)	9783031325106
DOIs	https://doi.org/10.1007/978-3-031-32511-3_166
Publication status	Published - 2023
Event	International Symposium of the International Federation for Structural Concrete, fib Symposium 2023 - Istanbul, Turkey Duration: 5 Jun 2023 → 7 Jun 2023

Publication series

Name	Lecture Notes in Civil Engineering
Volume	350 LNCE
ISSN (Print)	2366-2557
ISSN (Electronic)	2366-2565

Conference

Conference	International Symposium of the International Federation for Structural Concrete, fib Symposium 2023
Country/Territory	Turkey
City	Istanbul
Period	5/06/23 → 7/06/23

Bibliographical note

Publisher Copyright:
© 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Keywords

Artificial Intelligence
Construction Productivity
Hybrid Embedded Feature Selection
Optimization

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.1007/978-3-031-32511-3_166

Cite this

Efekan, E., Celik, T., & Tokdemir, O. B. (2023). Using Hybrid Artificial Intelligence Optimization Method to Predict Construction Labour Productivity. In A. Ilki, D. Çavunt, & Y. S. Çavunt (Eds.), Building for the Future: Durable, Sustainable, Resilient - Proceedings of the Symposium 2023 - Volume 2 (pp. 1624-1630). (Lecture Notes in Civil Engineering; Vol. 350 LNCE). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-031-32511-3_166

Efekan, Efkan ; Celik, Tolga ; Tokdemir, Onur B. / Using Hybrid Artificial Intelligence Optimization Method to Predict Construction Labour Productivity. Building for the Future: Durable, Sustainable, Resilient - Proceedings of the Symposium 2023 - Volume 2. editor / Alper Ilki ; Derya Çavunt ; Yavuz Selim Çavunt. Springer Science and Business Media Deutschland GmbH, 2023. pp. 1624-1630 (Lecture Notes in Civil Engineering).

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abstract = "Precise estimation of productivity is crucial for construction managers in making timely decisions. The aim is to propose a novel theoretical approach for predicting and optimizing Construction Labour Productivity (CLP). Numerous factors affecting CLP are the main challenge in modeling labor productivity. Accurate CLP prediction is required for effective decision-making before and during project execution. This article will explain the importance of artificial intelligence-based inference models on construction productivity and why hybrid embedded feature selection models, a new approach to increasing construction productivity, should be used more in integration with AI-based inference models.",

keywords = "Artificial Intelligence, Construction Productivity, Hybrid Embedded Feature Selection, Optimization",

author = "Efkan Efekan and Tolga Celik and Tokdemir, {Onur B.}",

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Efekan, E, Celik, T & Tokdemir, OB 2023, Using Hybrid Artificial Intelligence Optimization Method to Predict Construction Labour Productivity. in A Ilki, D Çavunt & YS Çavunt (eds), Building for the Future: Durable, Sustainable, Resilient - Proceedings of the Symposium 2023 - Volume 2. Lecture Notes in Civil Engineering, vol. 350 LNCE, Springer Science and Business Media Deutschland GmbH, pp. 1624-1630, International Symposium of the International Federation for Structural Concrete, fib Symposium 2023, Istanbul, Turkey, 5/06/23. https://doi.org/10.1007/978-3-031-32511-3_166

Using Hybrid Artificial Intelligence Optimization Method to Predict Construction Labour Productivity. / Efekan, Efkan; Celik, Tolga; Tokdemir, Onur B.
Building for the Future: Durable, Sustainable, Resilient - Proceedings of the Symposium 2023 - Volume 2. ed. / Alper Ilki; Derya Çavunt; Yavuz Selim Çavunt. Springer Science and Business Media Deutschland GmbH, 2023. p. 1624-1630 (Lecture Notes in Civil Engineering; Vol. 350 LNCE).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AB - Precise estimation of productivity is crucial for construction managers in making timely decisions. The aim is to propose a novel theoretical approach for predicting and optimizing Construction Labour Productivity (CLP). Numerous factors affecting CLP are the main challenge in modeling labor productivity. Accurate CLP prediction is required for effective decision-making before and during project execution. This article will explain the importance of artificial intelligence-based inference models on construction productivity and why hybrid embedded feature selection models, a new approach to increasing construction productivity, should be used more in integration with AI-based inference models.

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Efekan E, Celik T, Tokdemir OB. Using Hybrid Artificial Intelligence Optimization Method to Predict Construction Labour Productivity. In Ilki A, Çavunt D, Çavunt YS, editors, Building for the Future: Durable, Sustainable, Resilient - Proceedings of the Symposium 2023 - Volume 2. Springer Science and Business Media Deutschland GmbH. 2023. p. 1624-1630. (Lecture Notes in Civil Engineering). doi: 10.1007/978-3-031-32511-3_166

Using Hybrid Artificial Intelligence Optimization Method to Predict Construction Labour Productivity

Abstract

Publication series

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Bibliographical note

Keywords

UN SDGs

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