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Al-Chalabi, Hussan, Associate Senior LecturerORCID iD iconorcid.org/0000-0001-5620-5265
Alternative names
Biography [eng]

Hussan Al-Chalabi received the Ph.D. degree in operation and maintenance engineering from Luleå University of Technology, Luleå, Sweden, in 2014. He is currently working as an Associate Senior Lecturer with the Division of Operation and Maintenance Engineering, Luleå University of Technology, Luleå, Sweden. He obtained his B.Sc. in Mechanical Engineering from Mosul University, Iraq in 1994. He obtained his M.Sc. in Mechanical Engineering, thermal power from Mosul University, Iraq in 2008. After working for 2 years in Mosul University as a faculty member, he joined the postgraduate program of Luleå University of Technology, Luleå, Sweden and he obtained a PhD degree in Operation and Maintenance Engineering in 2014. He joined Luleå University as a Postdoctoral Researcher since 2015 for two years. He worked as a Senior Researcher in Luleå University of Technology, Sweden since March 2017 for one year. His current research interests are mainly in Operation and Maintenance, Replacement models, Optimization aspects, Reliability Engineering and LCC analysis.

Publications (2 of 2) Show all publications
Al-Douri, Y. K., Hamodi, H. & Zhang, L. (2018). Data clustering and imputing using a two-level multi-objective genetic algorithms (GA): A case study of maintenance cost data for tunnel fans. Cogent Engineering, 5(1), 1-16, Article ID 1513304.
Open this publication in new window or tab >>Data clustering and imputing using a two-level multi-objective genetic algorithms (GA): A case study of maintenance cost data for tunnel fans
2018 (English)In: Cogent Engineering, E-ISSN 2331-1916, Vol. 5, no 1, p. 1-16, article id 1513304Article in journal (Refereed) Published
Abstract [en]

Data clustering captures natural structures in data consisting of a set of objects and groups similar data together. The derived clusters can be used for scale analysis and to posit missing data values in objects, as missing data have a negative effect on the computational validity of models. This study develops a new two-level multi-objective genetic algorithm (GA) to optimize clustering in order to redact and impute missing cost data for fans used in road tunnels by the Swedish Transport Administration (Trafikverket). The first level uses a multi-objective GA based on fuzzy c-means to cluster cost data objects based on three main indices. The first is cluster centre outliers; the second is the compactness and separation ( ) of the data points and cluster centres; the third is the intensity of data points belonging to the derived clusters. Our clustering model is validated using k-means clustering. The second level uses a multi-objective GA to impute the missing cost redacted data in size using a valid data period. The optimal population has a low , 0.1%, and a high intensity, 99%. It has three cluster centres, with the highest data reduction of 27%. These three cluster centres have a suitable geometry, so the cost data can be partitioned into relevant contents to be redacted for imputing. Our model show better clustering detection and evaluation compared with k-means. The amount of missing data for the two cost objects are: labour 57%, materials 81%. The second level shows highly correlated data (R-squared 0.99) after imputing the missing data objects. Therefore, multi-objective GA can cluster and impute data to derive complete data that can be used for better estimation of forecasting.

Place, publisher, year, edition, pages
Taylor & Francis, 2018
Keywords
Data clustering, data imputing, multi-objective GA, fuzzy c-means, K-means clustering
National Category
Reliability and Maintenance Other Civil Engineering
Research subject
FOI-portföljer, Äldre portföljer
Identifiers
urn:nbn:se:trafikverket:diva-5575 (URN)10.1080/23311916.2018.1513304 (DOI)000444436800001 ()2-s2.0-85052696347 (Scopus ID)
Projects
LCC-metodik med koppling till Maximo
Funder
Swedish Transport Administration, TRV 2016/10828
Available from: 2018-08-14 Created: 2022-10-05 Last updated: 2023-09-04Bibliographically approved
Al-Douri, Y. K., Hamodi, H. & Lundberg, J. (2018). Time Series Forecasting using a Two-level Multi-objective Genetic Algorithm: A case study of cost data for tunnel fans. Algorithms, 11(8), Article ID 123.
Open this publication in new window or tab >>Time Series Forecasting using a Two-level Multi-objective Genetic Algorithm: A case study of cost data for tunnel fans
2018 (English)In: Algorithms, E-ISSN 1999-4893, Vol. 11, no 8, article id 123Article in journal (Refereed) Published
Abstract [en]

The aim of this study is to develop a novel two-level multi-objective genetic algorithm (GA) to optimize time series forecasting data for fans used in road tunnels by the Swedish Transport Administration (Trafikverket). The first level is for the process of forecasting time series cost data, while the second level evaluates the forecasting. The first level implements either a multi-objective GA based on the ARIMA model or based on the dynamic regression model. The second level utilises a multi-objective GA based on different forecasting error rates to identify a proper forecasting. Our method is compared with the ARIMA model only. The results show the drawbacks of time series forecasting using the ARIMA model. In addition, the results of the two-level model show the drawbacks of forecasting using a multi-objective GA based on the dynamic regression model. A multi-objective GA based on the ARIMA model produces better forecasting results. In the second level, five forecasting accuracy functions help in selecting the best forecasting. Selecting a proper methodology for forecasting is based on the averages of the forecasted data, the historical data, the actual data and the polynomial trends. The forecasted data can be used for life cycle cost (LCC) analysis.

Place, publisher, year, edition, pages
MDPI, 2018
Keywords
ARIMA model, data forecasting, multi-objective genetic algorithm, regression model
National Category
Other Civil Engineering
Research subject
FOI-portföljer, Äldre portföljer
Identifiers
urn:nbn:se:trafikverket:diva-5574 (URN)10.3390/a11080123 (DOI)000443614500015 ()2-s2.0-85052696396 (Scopus ID)
Projects
LCC-metodik med koppling till Maximo
Funder
Swedish Transport Administration, TRV 2016/10828
Note

Validerad;2018;Nivå 2;2018-08-14 (inah)

Available from: 2022-10-05 Created: 2022-10-05 Last updated: 2023-03-29
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ORCID iD: ORCID iD iconorcid.org/0000-0001-5620-5265

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