Construction + Engineering Journal Articles

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    Field study to compare and evaluate summer thermal comfort of school buildings with different moderate thermal mass in their building elements
    (MDPI (Multidisciplinary Digital Publishing Institute), 2023-11-22) Su, Bin; McPherson, Peter; Jadresin-Milic, Renata; Wang, Xinxin; Shamout, Sameh; Liang, Y.; Unitec, Te Pūkenga; Architon (N.Z.)
    Previous studies show that moderate thermal mass in school building elements can pos itively impact the winter indoor thermal environment in a temperate climate with mild, humid winters. Based on a field study, this research contributes new physical data of the summer indoor thermal environment of Auckland school buildings with different designs of moderate thermal mass in their building elements to add to the previous winter field-study data and demonstrates that a school building with moderate thermal mass is adequate in a temperate climate with mild, humid winters and warm, dry summers. This field study compared and evaluated the summer indoor thermal environment of classrooms with different moderate thermal mass in their building elements during the summer school term and the summer school holidays. This study found that a classroom with thermal mass in its building elements has 19% to 21% more time in summer than a classroom without any thermal mass in its building elements when indoor air temperatures are within the thermal comfort zone, which was solely impacted by the building’s thermal performance. This study established a suitable research method to analyse the field-study data and identify the differences in the indoor thermal environments of the school buildings with different designs of moderate thermal mass in their building element.
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    Deterministic and probabilistic risk management approaches in construction projects: A systematic literature review and comparative analysis
    (MDPI (Multidisciplinary Digital Publishing Institute), 2023-05-11) Khodabakhshian, A.; Puolitaival, Taija; Kestle, Linda; Politecnico di Milano; Tampere University; Unitec, Te Pūkenga; Te Pūkenga
    Risks and uncertainties are inevitable in construction projects and can drastically change the expected outcome, negatively impacting the project’s success. However, risk management (RM) is still conducted in a manual, largely ineffective, and experience-based fashion, hindering automa-tion and knowledge transfer in projects. The construction industry is benefitting from the recent Industry 4.0 revolution and the advancements in data science branches, such as artificial intelligence (AI), for the digitalization and optimization of processes. Data-driven methods, e.g., AI and machine learning algorithms, Bayesian inference, and fuzzy logic, are being widely explored as possible so-lutions to RM domain shortcomings. These methods use deterministic or probabilistic risk reason-ing approaches, the first of which proposes a fixed predicted value, and the latter embraces the notion of uncertainty, causal dependencies, and inferences between variables affecting projects’ risk in the predicted value. This research used a systematic literature review method with the objective of investigating and comparatively analyzing the main deterministic and probabilistic methods ap-plied to construction RM in respect of scope, primary applications, advantages, disadvantages, lim-itations, and proven accuracy. The findings established recommendations for optimum AI-based frameworks for different management levels—enterprise, project, and operational—for large or small data set. [This article belongs to the Special Issue Selected Papers from the 45th Australasian Universities Building Education Association (AUBEA 2022) expanded into a journal article which is available online: Website:]
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    Pavement crack classification using deep convolutional neural network
    (College of Engineering, Universiti Teknologi MARA (UiTM), 2021-11-15) Osman, M.K.; Mohammed Zamree, M.E.A.; Idris, M.; Ahmad, K.A.; Mohamed Yusof, N.A.; Ibrahim, A.; Hasnur Rabiain, A.; Bahri, Intan; Unitec Institute of Technology; Politeknik Tunku Sultanah Bahiyah (Malaysia); UiTM Cawangan Pulau Pinang (Malaysia); THB Maintenance Sdn. Bhd. (Malaysia)
    Road safety is one of the more difficult aspects concerning the field of civil engineering. Manual road inspection and distress detection by a road surveyor is a time-consuming, dangerous, and laborious process. This paper proposes an automated method to classify three common types of road distress; namely crocodile, longitudinal and transverse cracks using a deep convolution neural network. Four processes are involved to include data collection, cracked photo enhancement, cracks classification and performance evaluation. The first process of data collection involves capturing pavement crack images using a digital camera. Secondly, the crack images are labelled according to their group and their contrast further improved using the contrast limited adaptive histogram equalization (CLAHE) method. The third process involves training the deep convolutional neural network (DCNN). In this process, two (2) DCNN models are devised which are VGG16 and 9-Layer CNN models. Simulation results show that VGGG-16 with CLAHE enhancement were able to classify pavement cracks with high accuracy, precision, recall and F1-scores of 99.5%, 98.5%, 99.5% and 98.99% respectively. Through deep learning techniques, the VGGG-16 with CLAHE has demonstrated promising potential in classifying pavement cracks.
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    Brownfield land redevelopment strategies in urban areas: Criteria contributing to the decision-making process
    (Ierek Press, 2022-12-30) Singh, Sukhjap; Kiroff, Lydia; Sharma, Rashika; Unitec, Te Pūkenga; Te Pūkenga
    Urban intensification seems to be a growing trend, especially in the context of severe land scarcity. Brownfields offer great potential in meeting the increasing demand for housing in major cities worldwide. Redevelopment projects appear to provide immediate solutions to housing shortages that are being experienced due to population pressures in large metropolitan areas. The paper explores the range of factors that property developers need to consider in their decision-making process when assessing the viability of brownfield redevelopments. This research, which employed a comparative case study approach, and examined two brownfield redevelopments in Auckland, focused on the economic, social, and environmental criteria that were utilised in the decision-making process. Document analysis of the two case studies, site observations, and semi-structured interviews with the property developers were the main data collection methods. The results suggested that the economic aspects of a brownfield redevelopment are the most important criteria that developers consider during the feasibility assessment of proposed projects. Projects that offer the potential for quick investment returns for all stakeholders are the preferred choice for developers. Brownfield redevelopments offer significant potential for invigorating local areas through urban intensification which boosts local businesses and encourages community revitalisation. The environmental concerns appear to be the lowest priority and little consideration is given to reducing the environmental impacts or incorporating green building practices in the new developments. A major shift from a purely economic focus toward a comprehensive environmental approach to new developments is needed to ensure the sustainable development of cities.
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    Simulation and techno-economic assessment of hydrogen production from biomass gasification-based processes: A review
    (MDPI (Multidisciplinary Digital Publishing Institute), 2022-11-12) Castro, J.; Leaver, Jonathan; Pang, S.; Unitec, Te Pūkenga; University of Canterbury; University of Santo Tomas
    The development of low-carbon fuels from renewable resources is a key measure to reduce carbon dioxide emissions and mitigate climate change. Biomass gasification with subsequent gas processing and purification is a promising route to produce low-carbon hydrogen. In the past decade, simulation-based modelling using Aspen Plus software has supported the investigation of future potential industrial applications of this pathway. This article aims to provide a review of the modelling and economic assessment of woody biomass gasification-based hydrogen production, with focus on the evaluation of the model accuracy in predicting producer gas composition in comparison with experimental data depending on the approach implemented. The assessment of comprehensive models, which integrate biomass gasification with gas processing and purification, highlights how downstream gas processing could improve the quality of the syngas and, thus, the hydrogen yield. The information in this article provides an overview of the current practices, challenges, and opportunities for future research, particularly for the development of a comprehensive pathway for hydrogen production based on biomass gasification. Moreover, this review includes a techno-economic assessment of biomass to hydrogen processes, which will be useful for implementation at industrial-scale.