Construction + Engineering Journal Articles
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Item Estimating emissions from tyre tread wear of motor vehicles in New Zealand(ePress, Unitec | Te Pūkenga, 2024-07-23) Kudin, Roman; Singh, Niranjan; Chand, Prabhat; Bakmeedeniya, Anura; Tawaketini, Jone; Unitec, Te Pūkenga; Te PūkengaOver 5.6 million road vehicles are registered in New Zealand, with a wide range of contaminants released as a result of their operation. The material wearing out from tyre tread has previously been identified as a contaminant of potential concern (COPC) in New Zealand, but its amount has not been quantified. Numerous studies confirm that the wear rate of a tyre depends on multiple factors and can vary significantly, even for the same type of vehicle with the same tyres installed. Such factors have already been classified and weighted; therefore, they were not the subject of the current research. The aim of this study is to estimate the total amount of material released into the environment in New Zealand due to the tread wear of tyres. In this research, the New Zealand motor vehicle fleet was divided into categories according to the available statistical data. The estimation of the amount of the tyre material released into the environment was done separately in each category of vehicle, using the three-point method (Low, Medium and High). The calculation was performed using the statistical average annual distances driven by vehicles, and the tyre abrasion rates, also called emission factors (mg/km). The emission factors were adopted through a thorough review of the available studies of tyre abrasion rates in different countries. The three-point estimate shows that every year roughly 6.5 to 15.5 thousand tonnes of material from tyre tread wear is released into the environment in New Zealand, which gives between 1.26 and 2.97 kg when converted per capita. Such quantification is an important step in understanding the impacts of this contaminant on New Zealand ecosystems. Further research of tyre tread wear material is needed with regard to its emission factors; deposition, migration and concentrations in environmental compartments; accumulation and degradation paths; effects on living organisms and human health.Item 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.Item 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 PūkengaRisks 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: https://www.mdpi.com/2075-5309/13/5/1312]Item 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.Item 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 PūkengaUrban 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.