Gi-Wook Cha

696 total citations
29 papers, 500 citations indexed

About

Gi-Wook Cha is a scholar working on Building and Construction, Industrial and Manufacturing Engineering and Civil and Structural Engineering. According to data from OpenAlex, Gi-Wook Cha has authored 29 papers receiving a total of 500 indexed citations (citations by other indexed papers that have themselves been cited), including 19 papers in Building and Construction, 11 papers in Industrial and Manufacturing Engineering and 8 papers in Civil and Structural Engineering. Recurrent topics in Gi-Wook Cha's work include Recycled Aggregate Concrete Performance (18 papers), Municipal Solid Waste Management (11 papers) and Infrastructure Maintenance and Monitoring (8 papers). Gi-Wook Cha is often cited by papers focused on Recycled Aggregate Concrete Performance (18 papers), Municipal Solid Waste Management (11 papers) and Infrastructure Maintenance and Monitoring (8 papers). Gi-Wook Cha collaborates with scholars based in South Korea. Gi-Wook Cha's co-authors include Won‐Hwa Hong, Young‐Chan Kim, Young-Chan Kim, Hyeun Jun Moon, Jae‐Woo Park, Won-Jun Park, Se-Hyu Choi, Young‐Min Kim, Hyuncheol Seo and Young Yoon and has published in prestigious journals such as Journal of Hazardous Materials, Journal of Cleaner Production and International Journal of Environmental Research and Public Health.

In The Last Decade

Gi-Wook Cha

25 papers receiving 478 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Gi-Wook Cha South Korea 11 302 201 68 50 45 29 500
Abobakr Al-Sakkaf Canada 11 191 0.6× 44 0.2× 67 1.0× 46 0.9× 11 0.2× 60 392
Wenke Huang China 9 197 0.7× 82 0.4× 27 0.4× 72 1.4× 9 0.2× 14 363
Ghasan Alfalah Saudi Arabia 12 146 0.5× 45 0.2× 126 1.9× 44 0.9× 12 0.3× 47 366
Mónica Lopez-Alonso Spain 13 254 0.8× 24 0.1× 175 2.6× 95 1.9× 13 0.3× 32 547
K. R. A. Nunes Brazil 8 211 0.7× 191 1.0× 34 0.5× 55 1.1× 32 0.7× 13 402
Doğuş Güler Türkiye 11 247 0.8× 55 0.3× 18 0.3× 79 1.6× 17 0.4× 21 451
John Littlewood United Kingdom 11 188 0.6× 29 0.1× 36 0.5× 9 0.2× 13 0.3× 51 410
Ming-Lung Hung Taiwan 8 127 0.4× 194 1.0× 13 0.2× 78 1.6× 66 1.5× 10 368
Rouzbeh Shad Iran 10 105 0.3× 59 0.3× 41 0.6× 21 0.4× 18 0.4× 26 374
Olympia E. Demesouka Greece 8 122 0.4× 178 0.9× 28 0.4× 159 3.2× 49 1.1× 11 433

Countries citing papers authored by Gi-Wook Cha

Since Specialization
Citations

This map shows the geographic impact of Gi-Wook Cha's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Gi-Wook Cha with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Gi-Wook Cha more than expected).

Fields of papers citing papers by Gi-Wook Cha

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Gi-Wook Cha. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Gi-Wook Cha. The network helps show where Gi-Wook Cha may publish in the future.

Co-authorship network of co-authors of Gi-Wook Cha

This figure shows the co-authorship network connecting the top 25 collaborators of Gi-Wook Cha. A scholar is included among the top collaborators of Gi-Wook Cha based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Gi-Wook Cha. Gi-Wook Cha is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Cha, Gi-Wook, et al.. (2025). Development of an Optimal Machine Learning Model to Predict CO2 Emissions at the Building Demolition Stage. Buildings. 15(4). 526–526. 6 indexed citations
2.
Cha, Gi-Wook, et al.. (2024). Optimal Machine Learning Model to Predict Demolition Waste Generation for a Circular Economy. Sustainability. 16(16). 7064–7064. 5 indexed citations
3.
Cha, Gi-Wook, Won‐Hwa Hong, Se-Hyu Choi, & Young‐Chan Kim. (2023). Developing an Optimal Ensemble Model to Estimate Building Demolition Waste Generation Rate. Sustainability. 15(13). 10163–10163. 5 indexed citations
4.
Cha, Gi-Wook, Won‐Hwa Hong, & Young‐Chan Kim. (2023). Performance Improvement of Machine Learning Model Using Autoencoder to Predict Demolition Waste Generation Rate. Sustainability. 15(4). 3691–3691. 17 indexed citations
5.
Cha, Gi-Wook, et al.. (2023). Developing a Prediction Model of Demolition-Waste Generation-Rate via Principal Component Analysis. International Journal of Environmental Research and Public Health. 20(4). 3159–3159. 22 indexed citations
6.
Cha, Gi-Wook, et al.. (2023). Predicting Generation of Different Demolition Waste Types Using Simple Artificial Neural Networks. Sustainability. 15(23). 16245–16245. 1 indexed citations
7.
Zhang, Yuanlong, Young‐Chan Kim, & Gi-Wook Cha. (2023). Assessment of deep learning-based image analysis for disaster waste identification. Journal of Cleaner Production. 428. 139351–139351. 5 indexed citations
8.
Cha, Gi-Wook, Hyeun Jun Moon, & Young‐Chan Kim. (2022). A hybrid machine-learning model for predicting the waste generation rate of building demolition projects. Journal of Cleaner Production. 375. 134096–134096. 45 indexed citations
9.
Cha, Gi-Wook, et al.. (2022). Development of Machine Learning Model for Prediction of Demolition Waste Generation Rate of Buildings in Redevelopment Areas. International Journal of Environmental Research and Public Health. 20(1). 107–107. 17 indexed citations
10.
Cha, Gi-Wook, et al.. (2021). Comparison of Random Forest and Gradient Boosting Machine Models for Predicting Demolition Waste Based on Small Datasets and Categorical Variables. International Journal of Environmental Research and Public Health. 18(16). 8530–8530. 82 indexed citations
11.
Hong, Won‐Hwa, et al.. (2020). Risk assessment of asbestos containing materials in a deteriorated dwelling area using four different methods. Journal of Hazardous Materials. 410. 124645–124645. 12 indexed citations
12.
Cha, Gi-Wook, Hyeun Jun Moon, Young‐Min Kim, et al.. (2020). Development of a Prediction Model for Demolition Waste Generation Using a Random Forest Algorithm Based on Small DataSets. International Journal of Environmental Research and Public Health. 17(19). 6997–6997. 65 indexed citations
13.
Kim, Young‐Chan, et al.. (2020). Quantifying asbestos fibers in post-disaster situations: Preventive strategies for damage control. International Journal of Disaster Risk Reduction. 48. 101563–101563. 4 indexed citations
14.
Cha, Gi-Wook, et al.. (2020). Evaluating recycling potential of demolition waste considering building structure types: A study in South Korea. Journal of Cleaner Production. 256. 120385–120385. 40 indexed citations
15.
Cha, Gi-Wook, Young-Chan Kim, Hyeun Jun Moon, & Won‐Hwa Hong. (2017). New approach for forecasting demolition waste generation using chi-squared automatic interaction detection (CHAID) method. Journal of Cleaner Production. 168. 375–385. 41 indexed citations
16.
Cha, Gi-Wook, et al.. (2017). Analysis on the Reduction of Cooling Load and Improvement of Visual Environment by applying a Kinetic Shading Device in Summer. Journal of The Korean Society of Living Environmental System. 24(6). 810–823. 1 indexed citations
17.
Cha, Gi-Wook, Won‐Hwa Hong, Jin-Ho Kim, & Young-Chan Kim. (2017). A Study on Demolition Waste Amount Characteristics of Detached Houses in Urban Regeneration Project District by analyzing Building Material Volume. 33(4). 69–77. 1 indexed citations
18.
Cha, Gi-Wook, Won‐Hwa Hong, & Jin Ho Kim. (2017). A Study on CO<sub>2</sub> Emissions in End-of-Life Phase of Residential Buildings in Korea: Demolition, Transportation and Disposal of Building Materials. Key engineering materials. 730. 457–462. 3 indexed citations
19.
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Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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2026