Akçay, Emre Caner

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https://hdl.handle.net/20.500.14411/6695
Name Variants
E. C. Akçay
Emre Caner, Akcay
E.C.Akçay
Akçay,E.C.
E., Akcay
A., Emre Caner
Emre Caner, Akçay
Akcay, Emre Caner
Akcay,Emre Caner
E.,Akçay
Akcay,E.C.
E.C.Akcay
Emre Caner Akçay
A.,Emre Caner
Akçay, Emre Caner
E. C. Akcay
Job Title
Doçent Doktor
Email Address
caner.akcay@atilim.edu.tr
Main Affiliation
Civil Engineering
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Website
ORCID ID
0000-0003-4426-369X
Scopus Author ID
57194605354
Turkish CoHE Profile ID
D07819A527858CFB
Google Scholar ID
SdlDOAAAAAJ
WoS Researcher ID
FZC-8539-2022

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Dicle Üniversitesi Mühendislik Fakültesi Mühendislik Dergisi1
International Conference on Construction in the 21st Century -- 14th International Conference on Construction in the 21st Century, CITC 2024 -- 2 September 2024 through 5 September 2024 -- Rio de Janeiro -- 2086011
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Subject: Knowledge Management

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    Article
    Citation - Scopus: 1
    An Activity-Based Lessons Learned Model To Support Scheduling Decisions in Construction
    (Emerald Group Publishing Ltd, 2025) Yilmaz, Anil; Akcay, Emre Caner; Dikmen, Irem; Birgonul, M. Talat
    PurposeThe aim of this study is to develop an activity-based lessons-learned model that allows construction companies to capture, store, classify and reuse activity-related lessons learned (LL) from previous projects, thereby increasing the reliability of time estimates in scheduling.Design/methodology/approachScheduling is a knowledge-intensive process that requires the utilization of data and expert opinion elicitation from various levels of an organization in construction projects. This research consists of five successive steps: performing a needs analysis, proposing an activity-based lessons-learned process model, validating the proposed process model, developing a tool to apply the proposed model in a computer environment and testing the applicability of the tool. To implement the proposed model in practice, a web-based tool, namely the Construction Industry Scheduling with Activity-Based Lessons Learned Tool (ConSALL Tool), was developed. Its functionality was evaluated using black-box testing. The tool was then applied in a real construction project.FindingsResults show that ConSALL has the potential to improve scheduling decisions in construction projects by incorporating data and experience from previous projects. Findings from this research can be used to develop similar models and AI tools to foster activity-based learning in other project-based industries as well as the construction industry.Originality/valueThis paper presents an innovative approach to enhancing construction project scheduling by leveraging LL from past projects. The development and application of the ConSALL Tool demonstrate a practical implementation of the proposed model, providing a framework that can be adapted to other industries to improve project planning and execution.