Mr Emmanuel Boateng

Mr Emmanuel Boateng

Research student

Career Summary

Biography

Emmanuel B. Boateng is a doctoral student of Environmental and Occupational Health. He does research on health & safety management and machine learning. 

Emmanuel likes to play chess and monopoly in his leisure time. He tweets @bannyBoat


Keywords

  • Construction
  • Health and safety management
  • Organizational safety behavior

Languages

  • Akan (Mother)
  • English (Fluent)
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Publications

For publications that are currently unpublished or in-press, details are shown in italics.


Chapter (7 outputs)

Year Citation Altmetrics Link
2021 Pillay M, Weerasekara I, Ranawalage UCR, Boateng EB, 'Investigating the Measurement of Resilience Engineering for Improving Organisational Safety', 253-257 (2021)

This project investigates the measurement of resilience engineering. A growing body of peer-reviewed studies continues to be published on resilience engineering, demonstrates its ... [more]

This project investigates the measurement of resilience engineering. A growing body of peer-reviewed studies continues to be published on resilience engineering, demonstrates its recognition and importance to safety across a range of industrial contexts. However, little attention has focused on developing an understanding of how it has been conceptualized and measured. This is a significant gap which can limit its operationalization, benchmarking and evaluation n for research and practice. This paper presents an integrative review project currently underway which seeks to address this gap. After completing a systematic search and selection strategy seventeen articles were selected for analysis. Initial findings suggest fifteen survey instruments have been used in these studies.

DOI 10.1007/978-3-030-58282-1_40
Co-authors Manikam Pillay
2021 Pillay M, Enya A, Boateng EB, 'Investigating the Measurement of High Reliability Organisations for Health Care Safety', 283-288 (2021)

This project investigates the measurement of high reliability organisations for improving health care safety. A growing body of peer-reviewed studies continues to be published on ... [more]

This project investigates the measurement of high reliability organisations for improving health care safety. A growing body of peer-reviewed studies continues to be published on high reliability organisations, demonstrates its recognition and importance for improving safety across high risk contexts such as health care. However, little attention has focused on developing an understanding of how it has conceptualized, and factors that have been used in its measurement. This is a significant gap which can limit its operationalization for research and practice. This paper presents a systematic review project currently underway which seeks to address this gap. After completing a systematic search and selection strategy twenty-one articles were selected for analysis. Results indicate fourteen survey instruments have been used in these studies. Seventeen different definitions of HRO were identified. Nine studies used surveys for data collection. Both independent and outcomes variables were reported, these can be used to inform an initial theoretical framework and a survey instrument.

DOI 10.1007/978-3-030-58282-1_45
Co-authors Manikam Pillay, Andrew Enya Uon
2021 Boateng EB, Twumasi EA, Darko A, Tetteh MO, Chan APC, 'Predicting building-related carbon emissions: A test of machine learning models', Studies in Computational Intelligence 247-266 (2021)

This chapter evaluates and compares the performance of six machine-learning (ML) algorithms in predicting China¿s building-related carbon emissions. The models took into account f... [more]

This chapter evaluates and compares the performance of six machine-learning (ML) algorithms in predicting China¿s building-related carbon emissions. The models took into account five input parameters influencing building-related CO emissions: urbanisation, R&D, population size, GDP, and energy use. The study used quarterly data throughout 1971Q1¿2014Q4 to develop, calibrate, and validate the models. Each model was developed using 140 observations and validated on 36 observations. In tuning each ML model for comparative purposes, 10-fold with cross-validation approach was used in selecting the optimal hyperparameters and their associated arguments. The results indicate that the random forest (RF) model attained the highest coefficient of determination (R ) of 99.88%, followed by the k-nearest neighbour (KNN) (99.87%), extreme gradient boosting (XGBoost) (99.77%), decision tree (DT) (99.63%), adaptive boosting (AdaBoost) (99.56%), and the support vector regression (SVR) model (97.67%). Overall, the RF algorithm is the best performing ML algorithm in accurately predicting building-related CO emissions, whereas the best algorithm in terms of time efficiency is the DT algorithm. The KNN model is highly recommended when practitioners want to have accurate predictions in a timely manner. RF, KNN, and DT models could be added to the toolkits of environmental policymakers to provide high-quality forecasts and patterns of building-related CO emissions in an accurate and real-time manner. 2 2 2 2

DOI 10.1007/978-3-030-52067-0_11
2020 Acheampong A, Boateng E, 'Supporting Environmental Decision Making Application of Machine Learning Techniques to Australia s Emissions', Applied Intelligent Decision Making in Machine Learning, CRC Press, Boca Raton (2020)
DOI 10.1201/9781003049548
Co-authors Alex Acheampong
2020 Boateng EB, Pillay M, Davis P, 'Developing a safety culture index for construction projects in developing countries: a proposed fuzzy synthetic evaluation approach', Advances in Safety Management and Human Factors, Springer, Cham, Switzerland 167-179 (2020) [B1]
DOI 10.1007/978-3-030-20497-6_16
Citations Scopus - 2
Co-authors Peter Davis, Manikam Pillay
2020 Boateng EB, Davis P, Pillay M, 'Role of human safety intervention on the impact of safety climate on workers safety behaviours in construction projects: A conceptual model', Advances in Safety Management and Human Factors, Springer, Cham, Switzerland 190-200 (2020) [B1]
DOI 10.1007/978-3-030-20497-6_18
Co-authors Peter Davis, Manikam Pillay
2019 Boateng EB, Pillay M, Davis P, 'Predicting the Level of Safety Performance Using an Artificial Neural Network', Human Systems Engineering and Design. Proceedings of the 1st International Conference on Human Systems Engineering and Design (IHSED2018): Future Trends and Applications, Springer International Publishing, Cham, Switzerland 705-710 (2019) [B1]
DOI 10.1007/978-3-030-02053-8_107
Citations Scopus - 6Web of Science - 4
Co-authors Peter Davis, Manikam Pillay
Show 4 more chapters

Journal article (12 outputs)

Year Citation Altmetrics Link
2021 Tetteh MO, Chan APC, Ameyaw EE, Darko A, Yevu SK, Boateng EB, 'Management control structures and performance implications in international construction joint ventures: critical survey and conceptual framework', Engineering, Construction and Architectural Management, (2021)

Purpose: Management control is needed in international joint ventures (IJVs) for successful management and performance. While IJV management control and performance concept has be... [more]

Purpose: Management control is needed in international joint ventures (IJVs) for successful management and performance. While IJV management control and performance concept has been widely explored, in the construction sector, the core understanding of the design of the two concepts is still lacking. This has resulted in the neglect of important questions and directions for research and practice improvement. This study aims to conduct a critical survey of prior studies addressing the conceptualization of management control and performance in IJVs and to propose a framework for studying the performance implications of management control in international construction joint ventures (ICJVs). Design/methodology/approach: Using Scopus database and search terms, a systematic desktop search was conducted to retrieve empirically related peer-reviewed papers for this study. Findings: Drawing on the transaction cost, institutional and relational logic, the first inclusive hypothetical model for studying the relationship between different dimensions of management control mechanism and multiple performance criteria in ICJVs is presented. The model proposes a measurement method for both the management control and performance and explains how they can be established in ICJVs. Practical implications: The proposed framework provides a methodology to understand the dynamics of management control and performance implications in ICJV. Specifically, uncovering the critical paths will assist ICJV frontliners to approach management control in a more holistic and systematic way to promote achievement of ICJV goals. Originality/value: The study gives a firm ground to the construction industry, which is accurate and educational for related fields concentrating on several other forms of cooperative relationships.

DOI 10.1108/ECAM-07-2020-0579
2021 Umeh AC, Naidu R, Shilpi S, Boateng EB, Rahman A, Cousins IT, et al., 'Sorption of PFOS in 114 Well-Characterized Tropical and Temperate Soils: Application of Multivariate and Artificial Neural Network Analyses', Environmental Science and Technology, 55 1779-1789 (2021) [C1]

The influence of soil properties on PFOS sorption are not fully understood, particularly for variable charge soils. PFOS batch sorption isotherms were conducted for 114 temperate ... [more]

The influence of soil properties on PFOS sorption are not fully understood, particularly for variable charge soils. PFOS batch sorption isotherms were conducted for 114 temperate and tropical soils from Australia and Fiji, that were well-characterized for their soil properties, including total organic carbon (TOC), anion exchange capacity, and surface charge. In most soils, PFOS sorption isotherms were nonlinear. PFOS sorption distribution coefficients (Kd) ranged from 5 to 229 mL/g (median: 28 mL/g), with 63% of the Fijian soils and 35% of the Australian soils showing Kd values that exceeded the observed median Kd. Multiple linear regression showed that TOC, amorphous aluminum and iron oxides contents, anion exchange capacity, pH, and silt content, jointly explained about 53% of the variance in PFOS Kd in soils. Variable charge soils with net positive surface charges, and moderate to elevated TOC content, generally displayed enhanced PFOS sorption than in temperate or tropical soils with TOC as the only sorbent phase, especially at acidic pH ranges. For the first time, two artificial neural networks were developed to predict the measured PFOS Kd (R2 = 0.80) in the soils. Overall, both TOC and surface charge characteristics of soils are important for describing PFOS sorption.

DOI 10.1021/acs.est.0c07202
Co-authors Sonia Shilpi, Anthony Umeh, Ravi Naidu, Sreenivasulu Chadalavada, Dane Lamb
2020 Ampofo AG, Boateng EB, 'Beyond 2020: Modelling obesity and diabetes prevalence', Diabetes Research and Clinical Practice, 167 (2020) [C1]
DOI 10.1016/j.diabres.2020.108362
Citations Scopus - 4Web of Science - 3
2019 Pillay M, Enya A, Boateng EB, 'High reliability organisations and collective mindfulness for improving healthcare safety management: a scoping review protocol of factors, measures and instruments', International Journal of Occupational and Environmental Safety, 3 8-13 (2019)
DOI 10.24840/2184-0954_003.002_0002
Co-authors Andrew Enya Uon, Manikam Pillay
2019 Kissi E, Adjei-Kumi T, Amoah P, Boateng E, 'Identifying key economic indicators influencing tender price index prediction in the building industry: a case study of Ghana', International Journal of Construction Management, 19 106-112 (2019) [C1]
DOI 10.1080/15623599.2017.1389641
Citations Scopus - 3Web of Science - 5
2019 Acheampong AO, Boateng EB, 'Modelling carbon emission intensity: Application of artificial neural network', Journal of Cleaner Production, 225 833-856 (2019) [C1]
DOI 10.1016/j.jclepro.2019.03.352
Citations Scopus - 38Web of Science - 32
Co-authors Alex Acheampong
2019 Bannor B E, Acheampong AO, 'Deploying artificial neural networks for modeling energy demand: international evidence', International Journal of Energy Sector Management, 14 285-315 (2019) [C1]

Purpose: This paper aims to use artificial neural networks to develop models for forecasting energy demand for Australia, China, France, India and the USA. Design/methodology/appr... [more]

Purpose: This paper aims to use artificial neural networks to develop models for forecasting energy demand for Australia, China, France, India and the USA. Design/methodology/approach: The study used quarterly data that span over the period of 1980Q1-2015Q4 to develop and validate the models. Eight input parameters were used for modeling the demand for energy. Hyperparameter optimization was performed to determine the ideal parameters for configuring each country¿s model. To ensure stable forecasts, a repeated evaluation approach was used. After several iterations, the optimal models for each country were selected based on predefined criteria. A multi-layer perceptron with a back-propagation algorithm was used for building each model. Findings: The results suggest that the validated models have developed high generalizing capabilities with insignificant forecasting deviations. The model for Australia, China, France, India and the USA attained high coefficients of determination of 0.981, 0.9837, 0.9425, 0.9137 and 0.9756, respectively. The results from the partial rank correlation coefficient further reveal that economic growth has the highest sensitivity weight on energy demand in Australia, France and the USA while industrialization has the highest sensitivity weight on energy demand in China. Trade openness has the highest sensitivity weight on energy demand in India. Originality/value: This study incorporates other variables such as financial development, foreign direct investment, trade openness, industrialization and urbanization, which are found to have an important effect on energy demand in the model to prevent underestimation of the actual energy demand. Sensitivity analysis is conducted to determine the most influential variables. The study further deploys the models for hands-on predictions of energy demand.

DOI 10.1108/IJESM-06-2019-0008
Citations Scopus - 2Web of Science - 1
Co-authors Alex Acheampong
2018 Badu E, Kissi E, Boateng E, Antwi-Afari M, 'Tertiary Educational Infrastructural Development in Ghana: Financing, Challenges and Strategies', Africa Education Review, 1-17 (2018)
DOI 10.1080/18146627.2016.1251295
Citations Scopus - 3Web of Science - 2
2017 Kissi E, Boateng EB, Adjei-Kumi T, Badu E, 'Principal component analysis of challenges facing the implementation of value engineering in public projects in developing countries', International Journal of Construction Management, 17 142-150 (2017)
DOI 10.1080/15623599.2016.1233088
Citations Scopus - 19Web of Science - 7
2017 Kissi E, Adjei-Kumi T, Badu E, Boateng EB, 'Factors affecting tender price in the Ghanaian construction industry', Journal of Financial Management of Property and Construction, 22 252-268 (2017)
DOI 10.1108/JFMPC-09-2016-0044
Citations Scopus - 10Web of Science - 9
2017 Kissi E, Offei I, Boateng E, Badu E, 'Networking for knowledge capacity building of procurement professionals in Ghana', International Journal of Construction Supply Chain Management, 6 34-47 (2017)
DOI 10.14424/ijcscm602016-34-47
2017 Kissi E, Adjei-Kumi T, Amoah P, Boateng E, 'Assessment of Critical Barriers to Tender Price Indices Development in the Ghanaian Building Industry', Journal of Construction Project Management and Innovation, 7 1933-1953 (2017)
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Conference (7 outputs)

Year Citation Altmetrics Link
2020 Boateng E, Pillay M, Gajendran T, Davis P, 'Development of the Human Safety Intervention Questionnaire on Construction Projects', Proceedings of the Joint CIB W099 & TG59 International Web-Conference 2020: Good Health, Wellbeing & Decent Work, online (2020) [E1]
Co-authors Manikam Pillay, Thayaparan Gajendran, Peter Davis
2019 Boateng EB, Davis P, Pillay M, 'Predictors of Safety Behaviour in the Construction Industry: A Systematic Review', CIB World Building Congress 2019. Constructing Smart Cities, Hong Kong SAR, China (2019) [E1]
Co-authors Peter Davis, Manikam Pillay
2019 Fugar F, Boateng E, Eshun B, 'Development of job satisfaction index for construction employees in developing countries based on Frederick Herzberg s motivation theory', Proceedings of the WABER 2019 Conference, Accra, Ghana (2019) [E1]
DOI 10.33796/waberconference2019.29
2017 Ayarkwa J, Acheampong A, Waife F, Boateng E, 'Factors Affecting the Implementation of Sustainable Construction in Ghana: The Architect's Perspective', Kumasi, Ghana (2017)
2016 Agyekum K, Ayarkwa J, Amoah P, Boateng E, 'Challenges to Fire Safety Management in Multi-Storey Students' Hostels', Johannesburg, South Africa (2016)
2016 Agyekum K, Boateng E, De-Graft JO, 'Fire Safety Preparedness in the Central Business District of Kumasi, Ghana', Kumasi, Ghana (2016)
2015 Kissi E, Boateng E, Adjei-Kumi T, 'Strategies for Implementing Value Management in the Construction Industry of Ghana', Livingstone, Zambia (2015)
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Mr Emmanuel Boateng

Contact Details

Email emmanuel.boateng@uon.edu.au
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