Infrastructure Engineering - Research Publications

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    New normal remote communication for collaboration
    Vaz-Serra, P ; Hui, KP ; Aye, L ( 2021-12-19)
    Presented at the 12th International Conference on Structural Engineering and construction Management (ICSECM) 2021, Kandy, Sri Lanka (17-19 December)
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    Effects of learning curve models on onshore wind and solar PV cost developments in the USA
    Castrejon-Campos, O ; Aye, L ; Hui, FKP (PERGAMON-ELSEVIER SCIENCE LTD, 2022-05-01)
    Technological innovation planning for developing and deploying clean energy technologies plays a key role in reducing greenhouse gas emissions and transition to a low-carbon future. Learning curve theory has been adopted as a common framework for exploring the relationship between endogenous technological learning and technology cost developments. The aim of this article is to analyse the effects of selecting different learning curve approaches (i.e. model formulations) to describe energy technology cost changes over time. Experience and knowledge stock are chosen as the sources of learning to be considered. A new definition of experience was developed to account for the interaction between global and local experience. The new definition of experience also accounts for learning sub-processes (i.e. learning-by-doing, learning-by-using, and experience spillovers) to estimate total experience gained through technology deployment. An integrative model is developed for estimating the effects of learning-by-deploying and learning-by-researching on cost developments for onshore wind and solar PV in the USA. Publicly available data from government departments and organisations were utilised. It was found that technology cost developments are better explained when: (1) experience is defined as a function of global and local experience; (2) knowledge stock is also considered in the model formulation; and (3) technological processes affect only a fraction of the total capital cost. The findings suggested that the application of learning rates for model-based energy planning is context-dependent and how technological factors are explicitly defined may have significantly different policy implications (i.e. different technology costs predictions based on alternative model formulations).
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    Dataset on effects of learning curve models on onshore wind and solar PV cost developments in the USA (Version 2)
    Castrejon Campos, O ; Aye, L ; Hui, KP ( 2022-02-21)
    This dataset includes input data to estimate learning-by-deploying (LbD) and learning-by-researching (LbR) rates for onshore wind and solar PV in the United States of America (USA). Using different learning curve approaches the simulated technological-based cost developments are also presented. Coefficient of determination (R squared) and Root Mean Square Error (RMSE) were applied for quantification of the agreement between simulated and observed technological-based costs.
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    Dataset on effects of learning curve models on onshore wind and solar PV cost developments in the USA
    Castrejon Campos, O ; Aye, L ; Hui, K ( 2021-11-03)
    This dataset includes input data to estimate learning-by-deploying (LbD) and learning-by-researching (LbR) rates for onshore wind and solar PV in the United States of America (USA). Using different learning curve approaches the simulated technological-based cost developments are also presented. Coefficient of determination (R squared) and Root Mean Square Error (RMSE) were applied for quantification of the agreement between simulated and observed technological-based costs.
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    Construction Project Managers Graduate Agile Competencies Required to Meet Industry Needs
    Vaz-Serra, P ; Hui, F ; Aye, L ; Dissanayake, R ; Mendis, P ; Weerasekera, K ; De Silva, S ; Shiromal, F (Springer, Singapore, 2021-01-01)
    The construction industry is embracing new management challenges to deal with the ever-increasing needs for collaboration, environmental and social responsibilities. Improvements in construction project management competencies are essential to helping the construction sector to embrace the new challenges. Building engineering management capabilities through the correct training are therefore essential. In research involving the twenty-four largest contractors in Australia ‘Lean construction’ was identified as an important skill to be included in academic programs that has not yet fully been embraced. Contractors are not yet seeing ‘lean’ and ‘agile’ methods as important approaches to improve communication within the teams and between projects. This research highlighted that although contractors identified communication as one of themain skills needed to achieve a good performance in project construction management they do not yet recognise that training in lean and agile methodologies will help them to improve communication not only between professionals but between projects and organisations involved in each project in improving business goals.
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    Dataset on effects of learning curve models on clean energy technology cost developments
    Castrejón Campos, O ; Aye, L ; Hui, KF ( 2021-01-21)
    This dataset includes input data to estimate learning-by-doing (LbD) and learning-by-researching (LbR) rates for onshore wind and solar PV in the United States. Using different learning curve approaches the simulated technology cost developments are also presented. Coefficient of determination (R square) and Root Mean Square Error (RMSE) were applied for quantification of the agreement between simulated and observed technology costs.
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    Lean Practices Using Building Information Modeling (BIM) and Digital Twinning for Sustainable Construction
    Sepasgozar, SME ; Hui, FKP ; Shirowzhan, S ; Foroozanfar, M ; Yang, L ; Aye, L (MDPI, 2021-01-01)
    There is a need to apply lean approaches in construction projects. Both BIM and IoT are increasingly being used in the construction industry. However, using BIM in conjunction with IoT for sustainability purposes has not received enough attention in construction. In particular, the capability created from the combination of both technologies has not been exploited. There is a growing consensus that the future of construction operation tends to be smart and intelligent, which would be possible by a combination of both information systems and sensors. This investigation aims to find out the recent efforts of utilizing BIM for lean purposes in the last decade by critically reviewing the published literature and identifying dominant clusters of research topics. More specifically, the investigation is further developed by identifying the gaps in the literature to utilize IoT in conjunction with BIM in construction projects to facilitate applying lean techniques in a more efficient way in construction projects. A systematic review method was designed to identify scholarly papers covering both concepts “lean” and “BIM” in construction and possibilities of using IoT. A total of 48 scholarly articles selected from 26 construction journals were carefully reviewed thorough perusal. The key findings were discussed with industry practitioners. The transcriptions were analyzed employing two coding and cluster analysis techniques. The results of the cluster analysis show two main directions, including the recent practice of lean and BIM interactions and issues of lean and BIM adoption. Findings revealed a large synergy between lean and BIM in control interactions and reduction in variations, and surprisingly there are many uncovered areas in this field. The results also show that the capability of IoT is also largely not considered in recent developments. The number of papers covering both lean and BIM is very limited, and there is a large clear gap in understanding synergetic interactions of lean concepts applying in BIM and IoT in specific fields of construction such as sustainable infrastructure projects.
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    Connection to Nature
    Aye, L ; Hui, KPF ( 2020-12-10)
    Melbourne School of Engineering, Health and Wellbeing Session, 10 December 2020
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    A Systematic Content Review of Artificial Intelligence and the Internet of Things Applications in Smart Home
    Sepasgozar, S ; Karimi, R ; Farahzadi, L ; Moezzi, F ; Shirowzhan, S ; M. Ebrahimzadeh, S ; Hui, F ; Aye, L (MDPI AG, 2020-04-28)
    This article reviewed the state-of-the-art applications of the Internet of things (IoT) technology applied in homes for making them smart, automated, and digitalized in many respects. The literature presented various applications, systems, or methods and reported the results of using IoT, artificial intelligence (AI), and geographic information system (GIS) at homes. Because the technology has been advancing and users are experiencing IoT boom for smart built environment applications, especially smart homes and smart energy systems, it is necessary to identify the gaps, relation between current methods, and provide a coherent instruction of the whole process of designing smart homes. This article reviewed relevant papers within databases, such as Scopus, including journal papers published in between 2010 and 2019. These papers were then analyzed in terms of bibliography and content to identify more related systems, practices, and contributors. A designed systematic review method was used to identify and select the relevant papers, which were then reviewed for their content by means of coding. The presented systematic critical review focuses on systems developed and technologies used for smart homes. The main question is ”What has been learned from a decade trailing smart system developments in different fields?”. We found that there is a considerable gap in the integration of AI and IoT and the use of geospatial data in smart home development. It was also found that there is a large gap in the literature in terms of limited integrated systems for energy efficiency and aged care system development. This article would enable researchers and professionals to fully understand those gaps in IoT-based environments and suggest ways to fill the gaps while designing smart homes where users have a higher level of thermal comfort while saving energy and greenhouse gas emissions. This article also raised new challenging questions on how IoT and existing developed systems could be improved and be further developed to address other issues of energy saving, which can steer the research direction to full smart systems. This would significantly help to design fully automated assistive systems to improve quality of life and decrease energy consumption.
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    Green Buildings in Makassar, Indonesia
    Hui, K ; Ulya, PF ; Wilson, S ; Meyliawati, A ; Aye, L ; Gou, Z (Springer Nature, 2020)
    Indonesia has one of the world’s largest populations, which creates a demand for buildings. Construction and operation of buildings have impacts on environment. To create sustainable cities, Indonesia applied the smart cities concept and selected Makassar as one of three role model cities. This chapter explores the current situation in Makassar with respect to green building adoption, the challenges faced and opportunities in market transformation. The Green Building Council of Indonesia (GBCI) in Makassar is heavily involved with market transformation for green building practices and has four main activities: market transformation, training and education, green building certification and stakeholder engagement. GBCI has developed the GREENSHIP rating tool, an assessment system covering categories associated with the green building concept as it applies to Indonesia. The embracing of the green building concept, however, is still low in Makassar. Market transformation is a challenging task, and there is still a lack of formal education programmes and courses available to architects, engineers and the construction industry to drive the transformation. The initial higher cost of green building presents as a major barrier to the uptake of green building even though these costs are mitigated after a period of 4–5 years through a reduction in operational costs. Government regulations that support green building practices and education of the community about the benefits of green building may support/improve uptake of green building.