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

^*Bu çalışma için yazışmadan sorumlu yazar

İnşaat Mühendisliği Bölümü

Eastern Mediterranean University

Araştırma sonucu: Kitap/Rapor/Konferans Bildirisinde Bölüm › Konferans katkısı › bilirkişi

2 Atıf (Scopus)

Özet

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.

Orijinal dil	İngilizce
Ana bilgisayar yayını başlığı	Building for the Future
Ana bilgisayar yayını alt yazısı	Durable, Sustainable, Resilient - Proceedings of the Symposium 2023 - Volume 2
Editörler	Alper Ilki, Derya Çavunt, Yavuz Selim Çavunt
Yayınlayan	Springer Science and Business Media Deutschland GmbH
Sayfalar	1624-1630
Sayfa sayısı	7
ISBN (Basılı)	9783031325106
DOI'lar	https://doi.org/10.1007/978-3-031-32511-3_166
Yayın durumu	Yayınlandı - 2023
Etkinlik	International Symposium of the International Federation for Structural Concrete, fib Symposium 2023 - Istanbul, Turkey Süre: 5 Haz 2023 → 7 Haz 2023

Yayın serisi

Adı	Lecture Notes in Civil Engineering
Hacim	350 LNCE
ISSN (Basılı)	2366-2557
ISSN (Elektronik)	2366-2565

???event.eventtypes.event.conference???

???event.eventtypes.event.conference???	International Symposium of the International Federation for Structural Concrete, fib Symposium 2023
Ülke/Bölge	Turkey
Şehir	Istanbul
Periyot	5/06/23 → 7/06/23

Bibliyografik not

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

BM SKH

Bu sonuç, aşağıdaki Sürdürülebilir Kalkınma Hedefine/Hedeflerine katkıda bulunur

SKH 8 İnsana Yakışır İş ve Ekonomik Büyüme

Belgeye Erişim

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

Diğer Dosyalar ve Linkler

Link to publication in Scopus

Alıntı Yap

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; Hac. 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. editör / Alper Ilki ; Derya Çavunt ; Yavuz Selim Çavunt. Springer Science and Business Media Deutschland GmbH, 2023. pp. 1624-1630 (Lecture Notes in Civil Engineering).

@inproceedings{b240a72df7004c158e716ec5f6f7f4d2,

title = "Using Hybrid Artificial Intelligence Optimization Method to Predict Construction Labour Productivity",

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.\}",

note = "Publisher Copyright: {\textcopyright} 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.; International Symposium of the International Federation for Structural Concrete, fib Symposium 2023 ; Conference date: 05-06-2023 Through 07-06-2023",

year = "2023",

doi = "10.1007/978-3-031-32511-3\_166",

language = "English",

isbn = "9783031325106",

series = "Lecture Notes in Civil Engineering",

publisher = "Springer Science and Business Media Deutschland GmbH",

pages = "1624--1630",

editor = "Alper Ilki and Derya {\c C}avunt and {\c C}avunt, \{Yavuz Selim\}",

booktitle = "Building for the Future",

address = "Germany",

}

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, hac. 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; Hac. 350 LNCE).

Araştırma sonucu: Kitap/Rapor/Konferans Bildirisinde Bölüm › Konferans katkısı › bilirkişi

TY - GEN

T1 - Using Hybrid Artificial Intelligence Optimization Method to Predict Construction Labour Productivity

AU - Efekan, Efkan

AU - Celik, Tolga

AU - Tokdemir, Onur B.

PY - 2023

Y1 - 2023

N2 - 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.

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.

KW - Artificial Intelligence

KW - Construction Productivity

KW - Hybrid Embedded Feature Selection

KW - Optimization

UR - https://www.scopus.com/pages/publications/85164259841

U2 - 10.1007/978-3-031-32511-3_166

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

M3 - Conference contribution

AN - SCOPUS:85164259841

SN - 9783031325106

T3 - Lecture Notes in Civil Engineering

SP - 1624

EP - 1630

BT - Building for the Future

A2 - Ilki, Alper

A2 - Çavunt, Derya

A2 - Çavunt, Yavuz Selim

PB - Springer Science and Business Media Deutschland GmbH

T2 - International Symposium of the International Federation for Structural Concrete, fib Symposium 2023

Y2 - 5 June 2023 through 7 June 2023

ER -

Efekan E, Celik T, Tokdemir OB. Using Hybrid Artificial Intelligence Optimization Method to Predict Construction Labour Productivity. In Ilki A, Çavunt D, Çavunt YS, editörler, 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

Özet

Yayın serisi

???event.eventtypes.event.conference???

Bibliyografik not

BM SKH

Belgeye Erişim

Diğer Dosyalar ve Linkler

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Alıntı Yap