Showing papers in "Buildings in 2022"
TL;DR: This study seeks to analyze the definitions and characteristics of a digital twin, its interactions with other digital technologies used in built asset delivery and operation, and its applications and challenges within the built environment context.
Abstract: The concept of digital twins is proposed as a new technology-led advancement to support the processes of the design, construction, and operation of built assets. Commonalities between the emerging definitions of digital twins describe them as digital or cyber environments that are bidirectionally-linked to their physical or real-life replica to enable simulation and data-centric decision making. Studies have started to investigate their role in the digitalization of asset delivery, including the management of built assets at different levels within the building and infrastructure sectors. However, questions persist regarding their actual applications and implementation challenges, including their integration with other digital technologies (i.e., building information modeling, virtual and augmented reality, Internet of Things, artificial intelligence, and cloud computing). Within the built environment context, this study seeks to analyze the definitions and characteristics of a digital twin, its interactions with other digital technologies used in built asset delivery and operation, and its applications and challenges. To achieve this aim, the research utilizes a thorough literature review and semi-structured interviews with ten industry experts. The literature review explores the merits and the relevance of digital twins relative to existing digital technologies and highlights potential applications and challenges for their implementation. The data from the semi-structured interviews are classified into five themes: definitions and enablers of digital twins, applications and benefits, implementation challenges, existing practical applications, and future development. The findings provide a point of departure for future research aimed at clarifying the relationship between digital twins and other digital technologies and their key implementation challenges.
58 citations
TL;DR: In this paper , the authors present a summary of carbon peak and carbon neutrality (CPCN) in buildings using a bibliometric approach and propose future research directions, which will enrich the research body of CPCN and overcome current limitations.
Abstract: Due to large energy consumption and carbon emissions (ECCE) in the building sector, there is huge potential for carbon emission reduction, and this will strongly influence peak carbon emissions and carbon neutrality in the future. To get a better sense of the current research situation and future trends and to provide a valuable reference and guidance for subsequent research, this study presents a summary of carbon peak and carbon neutrality (CPCN) in buildings using a bibliometric approach. Three areas are addressed in the review through the analysis of 364 articles published from 1990–2021: (1) Which countries, institutions, and individuals have conducted extensive and in-depth research on CPCN in buildings, and what is the status quo of their collaboration and contributions? (2) What subjects and topics have aroused wide interest and enthusiasm among scholars, and what are their time trajectories? (3) What journals and authors have grabbed the attention of many scholars, and what are the research directions related to them? Moreover, we propose future research directions. Filling these gaps will enrich the research body of CPCN and overcome current limitations by developing more methods and exploring other practical applications.
46 citations
TL;DR: PyLMDI as discussed by the authors is a potential calculation tool for index decomposition analysis that can provide calculation guidance for carbon emission reduction in the buildings sector, which is an effective global response to the crisis of climate change.
Abstract: A timely analysis for carbon emission reduction in buildings is an effective global response to the crisis of climate change. The logarithmic mean Divisia index (LMDI) decomposition analysis approach has been extensively used to assess the carbon emission reduction potential of the buildings sector. In order to simplify the calculation process and to expand its application scope, a new open-source Python tool (PyLMDI) developed in this article is used to compute the results of LMDI decomposition analysis, including multiplicative and additive decomposition. Users can quickly obtain the decomposition result by initializing the input data through a simple class data structure. In addition, the carbon emissions from commercial buildings are used as a numerical example to demonstrate the function of PyLMDI. In summary, PyLMDI is a potential calculation tool for index decomposition analysis that can provide calculation guidance for carbon emission reduction in the buildings sector. The data and codes for the numerical example are also included.
38 citations
TL;DR: Wang et al. as mentioned in this paper used historical data to model the carbon emissions of commercial building operations, the LASSO regression was applied to estimate the model results, and the whale optimization algorithm was used to optimize the nonlinear parameter.
Abstract: The rapid growth of energy consumption in commercial building operations has hindered the pace of carbon emission reduction in the building sector in China. This study used historical data to model the carbon emissions of commercial building operations, the LASSO regression was applied to estimate the model results, and the whale optimization algorithm was used to optimize the nonlinear parameter. The key findings show the following: (1) The major driving forces of carbon emissions from commercial buildings in China were found to be the population size and energy intensity of carbon emissions, and their elastic coefficients were 0.6346 and 0.2487, respectively. (2) The peak emissions of the commercial building sector were 1264.81 MtCO2, and the peak year was estimated to be 2030. Overall, this study analyzed the historical emission reduction levels and prospective peaks of carbon emissions from China’s commercial buildings from a new perspective. The research results are helpful for governments and decision makers to formulate effective emission reduction policies and can also provide references for the low-carbon development of other countries and regions.
37 citations
TL;DR: In this article , the impact of adding graphene oxide (GO) to GBFS-fly ash-based geopolymer concrete was examined and the results showed that adding 0.25 wt.% GO increases the modulus of elasticity and compressive strength of the concrete.
Abstract: This paper reports the results of a study conducted to examine the impacts of adding graphene oxide (GO) to GBFS-fly ash-based geopolymer concrete. The geopolymer concrete’s compressive strength, thermal conductivity, and modulus of elasticity were assessed. X-ray diffraction (XRD) analysis was conducted to understand the differences in mineralogical composition and a rapid chloride penetration test (RCPT) to investigate the changes in the permeability of chloride ions imposed by GO addition. The results showed that adding 0.25 wt.% GO increases the modulus of elasticity and compressive strength of GBFS-FA concrete by 30.5% and 37.5%, respectively. In contrast, permeability to chloride ions was reduced by 35.3% relative to the GO-free counterparts. Thermal conductivity was decreased as GO dosage increased, with a maximum reduction of 33% being observed in FA65-G35 wt.% samples. Additionally, XRD showed the suitability of graphene oxide in geopolymer concrete. The present research demonstrates very promising features of GO-modified concrete that exhibit improved strength development and durability compared to traditional concrete, thus further advocating for the wider utilization of geopolymer concrete manufactured from industrial byproducts.
35 citations
TL;DR: The present study uses a bibliometric and systematic literature review (SLR) to examine the use of Building Information Modeling (BIM), the Internet of Things (IoT), and Digital Twins (DT) in the construction industry.
Abstract: The present study uses a bibliometric and systematic literature review (SLR) to examine the use of Building Information Modeling (BIM), the Internet of Things (IoT), and Digital Twins (DT) in the construction industry. The network visualization and other approaches based on the Web of Science (WOS) database and the patterns of research interactions were explored in 1879 academic publications using co-occurrence and co-citation investigations. Significant publications, conferences, influential authors, countries, organizations, and funding agencies have been recognized. Our study demonstrates that BIM, IoT, and DT in construction, Heritage BIM (HBIM), Smart Contracts, BIM, and Ontology, and VR and AR in BIM and DT are the main study themes. Finally, several prospective areas for future study are identified, including BIM and Metaverse technology, BIM and Artificial Intelligence (AI), Metaheuristic algorithms for optimization purposes in BIM, and the Circular Economy with BIM and IoT.
34 citations
TL;DR: This work is to compare ensemble deep neural network models, i.e., the super learner algorithm, simple averaging, weighted averaging, integrated stacking, as well as separate stacking ensemble models, andsuper learner models, in order to develop an accurate approach for estimating the compressive strength of FAC and reducing the high variance of the predictive models.
Abstract: Concrete is one of the most popular materials for building all types of structures, and it has a wide range of applications in the construction industry. Cement production and use have a significant environmental impact due to the emission of different gases. The use of fly ash concrete (FAC) is crucial in eliminating this defect. However, varied features of cementitious composites exist, and understanding their mechanical characteristics is critical for safety. On the other hand, for forecasting the mechanical characteristics of concrete, machine learning approaches are extensively employed algorithms. The goal of this work is to compare ensemble deep neural network models, i.e., the super learner algorithm, simple averaging, weighted averaging, integrated stacking, as well as separate stacking ensemble models, and super learner models, in order to develop an accurate approach for estimating the compressive strength of FAC and reducing the high variance of the predictive models. Separate stacking with the random forest meta-learner received the most accurate predictions (97.6%) with the highest coefficient of determination and the lowest mean square error and variance.
31 citations
TL;DR: In this article , a three-dimensional finite difference method was used to analyze the deformation and stresses of a passive pile under surcharge load in extensively deep soft soil, and the horizontal displacements of the pile agreed well with the field results.
Abstract: The three-dimensional finite difference method was used in this study to analyze the deformation and stresses of a passive pile under surcharge load in extensively deep soft soil. A three-dimensional numerical model was proposed and verified by a field test. The horizontal displacements of the pile agreed well with the field results. This study investigated the pile-foundation soil interaction, the load transfer mechanism, the excess pore water pressure (EPWP), and the horizontal resistance of the foundation soil. The results show that the soil in the corner of the loading area developed a large uplift deformation, while the center of the loading area developed a large settlement. The lateral displacement of the pile decreased sharply with the increase of the depth and increased with the surcharge load. The lateral displacement of the soil was negligible when the depth exceeded 30 m. The EPWP increased in a nonlinear way with the increase of the surcharge load and accumulated with the placement of the new lift. The distribution of the lateral earth pressure in the shallow soil layer was complex, and the negative value was observed under a high surcharge load due to the suction effect. The proportion coefficient of the horizontal resistance coefficient showed much smaller value in the situation of large lateral deformation and high surcharge load. The design code overestimated the horizontal resistance of the shallow foundation soil, which should be given attention for the design and analysis of the laterally loaded structures in extensively soft soil.
28 citations
TL;DR: According to the research results, the technological factors along with external variables and an individual’s personality had a positive influence on the perceived usefulness and the perceived ease of use of end-users of AI-based technology.
Abstract: In the era of the Fourth Industrial Revolution, artificial intelligence (AI) is a core technology, and AI-based applications are expanding in various fields. This research explored the influencing factors on end-user’s intentions and acceptance of AI-based technology in construction companies using the technology acceptance model (TAM) and technology–organisation–environment (TOE) framework. The analysis of end-users’ intentions for accepting AI-based technology was verified by applying the structure equation model. According to the research results, the technological factors along with external variables and an individual’s personality had a positive influence (+) on the perceived usefulness and the perceived ease of use of end-users of AI-based technology. Conversely, environmental factors such as suggestions from others appeared to be disruptive to users’ technology acceptance. In order to effectively utilise AI-based technology, organisational factors such as the support, culture, and participation of the company as a whole were indicated as important factors for AI-based technology implementation.
28 citations
TL;DR: In this paper , the influence of clay mineralogy on pore structure and permeability is analyzed, and then the effective e (eeff) and effective SSA (Seff) are proposed.
Abstract: Clay soil is a common building foundation material, and its permeability is very important for the safety of foundation pits and the later settlement of buildings. However, the traditional Kozeny-Carman (K-C) equation shows serious discrepancies when predicting the permeability of clay in building foundation treatment. Therefore, solving the application of K-C equation in clay is a problem faced by the engineers and scholars. In this paper, the influence of clay mineralogy on pore structure and permeability is analyzed, and then the effective e (eeff) and effective SSA (Seff) are proposed. Based on the eeff and Seff, the permeability prediction model modified on Kozeny-Carman is built. Then, seepage experiments are conducted on two types of clay samples to test this prediction model; at the same time, the MIP combining freeze-drying methods are used to obtain the Seff and eeff. Through the discussion of the test results, three main conclusions are obtained: (1) there are invalid pores in clay due to the influence of clay mineral, this is the reason for which K-C equation is unsuitable for clay; (2) the eeff and Seff can reflect the structural state of clay during seepage; (3) the results of the permeability prediction model in this paper agree well with the test results, which indicates that this prediction model is applicable to clay. The research results of this paper are significant to solve the academic problem that K-C equation is not applicable to clay and significant to ensure the safety of building foundation pits in clay areas.
27 citations
TL;DR: In this article , a long short-term memory (LSTM) model was proposed to predict the HSC compressive strength using 324 data sets with five input independent variables, namely water, cement, fine aggregate, coarse aggregate, and superplasticizer.
Abstract: Compressive strength is an important mechanical property of high-strength concrete (HSC), but testing methods are usually uneconomical, time-consuming, and labor-intensive. To this end, in this paper, a long short-term memory (LSTM) model was proposed to predict the HSC compressive strength using 324 data sets with five input independent variables, namely water, cement, fine aggregate, coarse aggregate, and superplasticizer. The prediction results were compared with those of the conventional support vector regression (SVR) model using four metrics, root mean square error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE), and correlation coefficient (R2). The results showed that the prediction accuracy and reliability of LSTM were higher with R2 = 0.997, RMSE = 0.508, MAE = 0.08, and MAPE = 0.653 compared to the evaluation metrics R2 = 0.973, RMSE = 1.595, MAE = 0.312, MAPE = 2.469 of the SVR model. The LSTM model is recommended for the pre-estimation of HSC compressive strength under a given mix ratio before the laboratory compression test. Additionally, the Shapley additive explanations (SHAP)-based approach was performed to analyze the relative importance and contribution of the input variables to the output compressive strength.
TL;DR: A comprehensive review of the engineering control preventive measures to mitigate COVID-19 spread, healthy building design, and material was carried out in this paper , where the current state-of-the-art preventive measures presented include ultraviolet germicidal irradiation (UVGI), bipolar ionization, vertical gardening, and indoor plants.
Abstract: The COVID-19 pandemic forced the accessibility, social gathering, lifestyle, and working environment to be changed to reduce the infection. Coronavirus spreads between people in several different ways. Small liquid particles (aerosols, respiratory droplets) from an infected person are transmitted through air and surfaces that are in contact with humans. Reducing transmission through modified heating, ventilation, and air conditioning (HVAC) systems and building design are potential solutions. A comprehensive review of the engineering control preventive measures to mitigate COVID-19 spread, healthy building design, and material was carried out. The current state-of-the-art engineering control preventive measures presented include ultraviolet germicidal irradiation (UVGI), bipolar ionization, vertical gardening, and indoor plants. They have potential to improve the indoor air quality. In addition, this article presents building design with materials (e.g., copper alloys, anti-microbial paintings) and smart technologies (e.g., automation, voice control, and artificial intelligence-based facial recognition) to mitigate the infections of communicable diseases.
TL;DR: In this paper , a review of the existing studies on the structural optimization of CFS sections and the thermal performance of such CFS structures is presented, and the methodologies used in the existing literature for optimizing CFS members for both structural and thermal performances have been summarized and presented systematically.
Abstract: The construction and building sectors are currently responsible globally for a significant share of the total energy consumption and energy-related carbon dioxide emissions. The use of Modern Methods of Construction can help reduce this, one example being the use of cold-formed steel (CFS) construction. CFS channel sections have inherent advantages, such as their high strength-to-weight ratio and excellent potential for recycling and reusing. CFS members can be rolled into different cross-sectional shapes and optimizing these shapes can further improve their load-bearing capacities, resulting in a more economical and efficient building solution. Conversely, the high thermal conductivity of steel can lead to thermal bridges, which can significantly reduce the building’s thermal performance and energy efficiency. Hence, it is also essential to consider the thermal energy performance of the CFS structures. This paper reviews the existing studies on the structural optimization of CFS sections and the thermal performance of such CFS structures. In total, over 160 articles were critically reviewed. The methodologies used in the existing literature for optimizing CFS members for both structural and thermal performances have been summarized and presented systematically. Research gaps from the existing body of knowledge have been identified, providing guidelines for future research.
TL;DR: In this article , the authors examined the acousto-structural behavior of a sandwich cylindrical shell benefiting from hexagonal honeycomb structures in its core and functionally graded porous (FGP) layers on its outer and inner surfaces.
Abstract: To examine the acousto-structural behavior of a sandwich cylindrical shell benefiting from hexagonal honeycomb structures in its core and functionally graded porous (FGP) layers on its outer and inner surfaces, a comprehensive study based on an analytical model which also considers the effect of an external flow is conducted. A homogenous orthotropic model is used for the honeycomb core while its corresponding material features are found from the modified Gibson’s equation. The distribution pattern of FGP parts is either even or logarithmic-uneven, and a special rule-of-mixture relation governs their properties. Based on the first-order shear deformation theory (FSDT), Hamilton’s principle is exploited to derive the final coupled vibro-acoustic equations, which are then solved analytically to allow us to calculate the amount of sound transmission loss (STL) through the whole structure. This acoustic property is further investigated in the frequency domain by changing a set of parameters, i.e., Mach number, wave approach angle, structure’s radius, volume fraction, index of functionally graded material (FGM), and different honeycomb properties. Overall, good agreement is observed between the result of the present study and previous findings.
TL;DR: Wang et al. as mentioned in this paper used the vector autoregressive model to construct a theoretical model of green technology innovation behavior (GTIB) in construction enterprises and analyzed the mechanism of action of the factors influencing the GTIB of construction enterprises.
Abstract: The Green Technology Innovation Behavior (GTIB) of construction enterprises is crucial for promoting green development in the construction industry. In order to clarify the mechanism of action affecting the GTIB of construction enterprises, this paper considers the context of green development in the construction industry based on the vector autoregressive model and constructs a theoretical model of GTIB in construction enterprises. Time series data collected by the Chinese government (2000–2018) were used to analyze the mechanism of action of the factors influencing the GTIB of construction enterprises by EViews 10.0. The results of the paper showed the following: (1) direct government investment has the greatest impact on the GTIB of construction enterprises and has made a positive contribution; (2) the added value of Gross Domestic Product (GDP) of the construction industry has a relatively small impact on the GTIB of construction enterprises; (3) the role of environmental regulation on the GTIB of construction enterprises is non-linear. This paper further broadens the research to the factors influencing the GTIB of construction enterprises. Meanwhile, this paper provides a reference basis for local governments to formulate policies related to the GTIB of construction enterprises.
TL;DR: In this paper , the authors present an examination of architectural variety and spatial possibilities in current serial and modular multi-storey mass timber construction, and compare the different structural and design aspects to achieve a comprehensive overview of possibilities in timber construction.
Abstract: Throughout the last two decades the timber building sector has experienced a steady growth in multi-storey construction. Although there has been a growing number of research focused on trends, benefits, and disadvantages in timber construction from various technical perspectives, so far there is no extensive literature on the trajectory of emerging architectural typologies. This paper presents an examination of architectural variety and spatial possibilities in current serial and modular multi-storey timber construction. It aims to draw a parallel between architectural characteristics and their relation to structural systems in timber. The research draws from a collection of 350 contemporary multi-storey timber building projects between 2000 and 2021. It consists of 300 built projects, 12 projects currently in construction, and 38 design proposals. The survey consists of quantitative and qualitative project data, as well as classification of the structural system, material, program, massing, and spatial organization of the projects. It then compares the different structural and design aspects to achieve a comprehensive overview of possibilities in timber construction. The outcome is an identification of the range of morphologies and a better understanding of the design space in current serial and modular multi-storey mass timber construction.
TL;DR: In this paper , the authors proposed a model for three-echelon supply chain supply management in off-site construction with stochastic constraints, where multiple factories produce various types of components and ship them to supplier warehouses to meet the needs of the construction sites.
Abstract: Off-site construction is becoming more popular as more companies recognise the benefits of shifting the construction process away from the construction site and into a controlled manufacturing environment. However, challenges associated with the component supply chain have not been fully addressed. As a result, this study proposes a model for three-echelon supply chain supply management in off-site construction with stochastic constraints. In this paper, multiple off-site factories produce various types of components and ship them to supplier warehouses to meet the needs of the construction sites. Each construction site is directly served by a supplier warehouse. The service level for each supplier warehouse is assumed to be different based on regional conditions. Because of the unpredictable nature of construction projects, demand at each construction site is stochastic, so each supplier warehouse should stock a certain number of components. The inventory control policy is reviewed regularly and is in (R, s, S) form. Two objectives are considered: minimising total cost while achieving the desired delivery time for construction sites due to their demands and balancing driver workloads during the routeing stage. A grasshopper optimisation algorithm (GOA) and an exact method are used to solve this NP-hard problem. The findings of this study contribute new theoretical and practical insights to a growing body of knowledge about supply chain management strategies in off-site construction and have implications for project planners and suppliers, policymakers, and managers, particularly in companies where an unplanned supply chain exacerbates project delays and overrun costs.
TL;DR: In this article , the main contents of Intelligent Green Buildings (IGB) and the application and role of Digital Twins (DTs) in intelligent buildings are summarized and the advantages of DTs are further investigated in the context of IGB for DT smart cities.
Abstract: At present, the integration of green building, the intelligent building industry and high-quality development are facing a series of new opportunities and challenges. This review aims to analyze the digital development of smart green buildings to make it easier to create contiguous ecological development areas in green ecological cities. It sorts out the main contents of Intelligent Green Buildings (IGB) and summarizes the application and role of Digital Twins (DTs) in intelligent buildings. Firstly, the basic connotations and development direction of IGB are deeply discussed, and the current realization and applications of IGB are analyzed. Then, the advantages of DTs are further investigated in the context of IGB for DT smart cities. Finally, the development trends and challenges of IGB are analyzed. After a review and research, it is found that the realization and application of IGB have been implemented, but the application of DTs remains not quite integrated into the design of IGB. Therefore, a forward-looking design is required when designing the IGBs, such as prioritizing sustainable development, people’s livelihoods and green structures. At the same time, an IGB can only show its significance after the basic process of building the application layer is performed correctly. Therefore, this review contributes to the proper integration of IGB and urban development strategies, which are crucial to encouraging the long-term development of cities, thus providing a theoretical basis and practical experience for promoting the development of smart cities.
TL;DR: In this paper , a systematic review of the literature from the Scopus database on construction project complexity is presented, focusing on the following topics: identifying and measuring project complexity, schedule performance and cost estimation, system integration and dynamic capabilities, and risk assessment and uncertainty.
Abstract: The construction industry has been experiencing a rapid increase in complex projects for the last two decades. Simultaneously, project complexity has received more attention from academics and practitioners worldwide. Many studies suggest that perceiving complexity is critical for successful construction project management. This study investigates the current status and future trends in construction project complexity (CPC) literature from the Scopus database. This review systematically uses bibliometric and scientometric methods through co-occurrence and co-citation analysis. First, 644 academic documents were retrieved from the Scopus database. Then, co-occurrence and co-citation analysis were performed along with network visualization to examine research interconnections’ patterns. As a result, relevant keywords, productive authors, and important journals have been highlighted. The prominent research topics within the literature on construction project complexity focus on the following topics: identifying and measuring project complexity, schedule performance and cost estimation, system integration and dynamic capabilities, and risk assessment and uncertainty. Finally, the potential research directions are developing towards safety performance, organizational resilience, and integrated project delivery (IPD). The study still has a limitation. The review focuses only on the academic documents retrieved from the Scopus database, thus restricting the coverage of the reviewed literature relating to construction project complexity. To the best of the author’s knowledge, this study is the first study that provides a systematic review of the literature from the Scopus database on construction project complexity.
TL;DR: In this article , a conceptual partner selection framework for the digital green innovation management of prefabricated construction towards urban building 5.0 is proposed, which can be used to assist PCEs to select joint investment partners of digital green and innovative projects for sustainable urban development.
Abstract: Digital green innovation management activities are the core of low-carbon intelligent development of prefabricated construction enterprises (PCEs) for sustainable urban development. PCEs have to seek joint venture partners to avoid the financial risk of digital green innovation projects. The purpose of this study is to develop a conceptual partner selection framework for the digital green innovation management of prefabricated construction towards urban building 5.0. In this study, first, symbiosis theory and six analysis methods were integrated to innovatively build a 3W1H-P framework system for the joint venture capital partner selection of digital green innovation projects. Second, the dual combination weighting method was innovatively proposed to avoid subjective and objective deviation in attribute weight and time weight. Finally, empirical research was carried out to verify the scientific nature, reliability, and practicability of the framework system and selection model. The results of this study show that the framework system and selection model proposed can be used to assist PCEs to select joint investment partners of digital green and innovative projects for sustainable urban development.
TL;DR: In this paper , a comprehensive study using Life Cycle Assessment (LCA) was conducted to quantify and compare the quantity of carbon emissions from two commonly designed houses in the Auckland region, one built from light timber and the other from light steel, both designed for a lifespan of 90 years.
Abstract: In New Zealand, housing is typically low density, with light timber framing being the dominant form of construction with more than 90% of the market. From 2020, as a result of the global pandemic, there was a shortage of timber in New Zealand, resulting in increased popularity for light steel framing, the main alternative to timber for housing. At the same time, the New Zealand government is committed to sustainability practises through legislation and frameworks, such as the reduction of whole-of-life carbon emissions for the building industry. New Zealand recently announced reducing its net greenhouse gas emissions by 50% within 2030. Life cycle assessment (LCA) is a technique for assessing the environmental aspects associated with a product over its life cycle. Despite the popularity of LCA in the construction industry of New Zealand, prior research results seem varied. There is no unified NZ context database to perform an LCA for buildings. Therefore, in this paper, a comprehensive study using LCA was conducted to quantify and compare the quantity of carbon emissions from two commonly designed houses in the Auckland region, one built from light timber and the other from light steel, both designed for a lifespan of 90 years. The cradle-to-cradle system boundary was used for the LCA. From the results of this study, it was found that the light steel house had 12.3% more carbon in total (including embodied and operational carbons) when compared to the light timber house, of which the manufacturing of two houses had a difference of 50.4% in terms of carbon emissions. However, when the end-of-life (EOL) analysis was included, it was found that the extra carbon could be offset due to the steel’s recyclability, reducing the amount of embodied carbon in the manufacturing process. Therefore, there was no significant difference in carbon emissions between the light steel and the light timber building, with the difference being only 12.3%.
TL;DR: Wang et al. as discussed by the authors investigated the spatial and temporal evolution of carbon emissions in China's construction industry and their decoupling relationship with economic growth relying on GIS tools and decoupled model in an attempt to provide a basis for the formulation of differentiated construction emission reduction policies and plans.
Abstract: The construction industry is the backbone of most countries, but its carbon emissions are huge and growing rapidly, constraining the achievement of global carbon-peaking and carbon-neutrality goals. China’s carbon emissions are the highest in the world, and the construction industry is the largest contributor. Due to significant differences between provinces in pressure, potential, and motivation to reduce emissions, the “one-size-fits-all” emission reduction policy has failed to achieve the desired results. This paper empirically investigates the spatial and temporal evolution of carbon emissions in China’s construction industry and their decoupling relationship with economic growth relying on GIS tools and decoupling model in an attempt to provide a basis for the formulation of differentiated construction emission reduction policies and plans in China. The study shows that, firstly, the changes in carbon emissions and carbon intensity in the provincial construction industry are becoming increasingly complex, with a variety of types emerging, such as declining, “inverted U-shaped”, growing, “U-shaped”, and smooth fluctuating patterns. Secondly, the coefficient of variation is higher than 0.65 for a long time, indicating high spatial heterogeneity. However, spatial agglomeration and correlation are low, with only a few cluster-like agglomerations formed in the Pearl River Delta, Yangtze River Delta, Bohai Bay, Northeast China, and Loess and Yunnan–Guizhou Plateau regions. Thirdly, most provinces have not reached peak carbon emissions from the construction industry, with 25% having reached peak and being in the plateau stage, respectively. Fourthly, the decoupling relationship between carbon emissions from the construction industry and economic growth, as well as their changes, is increasingly diversified, and most provinces are in a strong and weak decoupling state. Moreover, a growing number of provinces that have achieved decoupling are moving backward to re-coupling, due to the impact of economic transformation and the outbreaks of COVID-19, with the degraded regions increasingly concentrated in the northeast and northwest. Fifthly, we classify China’s 30 provinces into Leader, Intermediate, and Laggard policy zones and further propose differentiated response strategies. In conclusion, studying the trends and patterns of carbon-emission changes in the construction industry in different regions, revealing their spatial differentiation and correlation, and developing a classification management strategy for low carbonized development of the construction industry help significantly improve the reliability, efficiency, and self-adaptability of policy design and implementation.
TL;DR: In this article , a systematic review was conducted on the available literature on supply chain management within prefabricated house-building research from the perspective of suppliers, and the qualitative analysis was performed to identify the key themes and keywords.
Abstract: Prefabricated house-building companies, as suppliers or supply chains, which use manufacturing as a business approach towards industrialization, struggle to implement principles and optimal practices driven from well-established and validated theories in operational research. Supply chain management has a mature body of knowledge that has been widely adopted by research on offsite construction to improve its performance at an organisational level. However, there is no comprehensive review available in the literature for supply chain management theory within prefabricated house building research from the perspective of suppliers. In this study, a systematic review was conducted on the available literature on supply chain management within prefabricated house-building research. Initially, qualitative analysis was performed to identify the key themes. Later, quantitative analyses were applied to validate the overlapping themes and keywords. Further, key trends related to focus, methods and theories or frameworks were reported. The findings were discussed in the context of recent developments in all principal component bodies of supply chain management for future work. This study also provides a brief guide for potential future review studies to explore interdisciplinary intervention within the offsite stream.
TL;DR: The process for developing an AI model that differentiates between various types of construction waste is delineated and the Fréchet Inception Distance method was used to increase the amount of learning data.
Abstract: The demand for categorising technology that requires minimum manpower and equipment is increasing because a large amount of waste is produced during the demolition and remodelling of a structure. Considering the latest trend, applying an artificial intelligence (AI) model for automatic categorisation is the most efficient method. However, it is difficult to apply this technology because research has only focused on general domestic waste. Thus, in this study, we delineate the process for developing an AI model that differentiates between various types of construction waste. Particularly, solutions for solving difficulties in collecting learning data, which is common in AI research in special fields, were also considered. To quantitatively increase the amount of learning data, the Fréchet Inception Distance method was used to increase the amount of learning data by two to three times through augmentation to an appropriate level, thus checking the improvement in the performance of the AI model.
TL;DR: In this article , the effects of two nature-based solutions (NBSs) techniques are reviewed and analysed: increasing surface greenery/vegetation (ISG) and increasing surface reflectivity (ISR).
Abstract: Canada is warming at double the rate of the global average caused in part to a fast-growing population and large land transformations, where urban surfaces contribute significantly to the urban heat island (UHI) phenomenon. The federal government released the strengthened climate plan in 2020, which emphasizes using nature-based solutions (NBSs) to combat the effects of UHI phenomenon. Here, the effects of two NBSs techniques are reviewed and analysed: increasing surface greenery/vegetation (ISG) and increasing surface reflectivity (ISR). Policymakers have the challenge of selecting appropriate NBSs to meet a wide range of objectives within the urban environment and Canadian-specific knowledge of how NBSs can perform at various scales is lacking. As such, this state-of-the-art review intends to provide a snapshot of the current understanding of the benefits and risks associated with the implantation of NBSs in urban spaces as well as a review of the current techniques used to model, and evaluate the potential effectiveness of UHI under evolving climate conditions. Thus, if NBSs are to be adopted to mitigate UHI effects and extreme summertime temperatures in Canadian municipalities, an integrated, comprehensive analysis of their contributions is needed. As such, developing methods to quantify and evaluate NBSs’ performance and tools for the effective implementation of NBSs are required.
TL;DR: In this paper , an experimental study on vibration velocities of piled raft supported embankment and foundations is presented in soft soil with different underground water levels, showing that the change in water level has slight impaction on the peak spectrum of vibration velocity at harmonic frequencies.
Abstract: In recent years, the high development of high-speed railway lines cross through areas with poor geological conditions, such as soft soil, offshore and low-lying marsh areas, resulting geotechnical problems, such as large settlements and reduction of bearing capacity. As a new soil reinforcement method in high speed railway lines, the piled raft structure has been used to improve soil conditions and control excess settlement. In order to study the dynamic behavior of piled raft supported ballastless track system in soft soil, an experimental study on vibration velocities of piled raft supported embankment and foundations is presented in soft soil with different underground water levels. Vibration velocities at specified positions of the piled raft supported embankment and foundations are obtained and discussed. The vibration velocity curves on various testing locations of piled raft foundations are clearly visible and have sharp impulse and relaxation pattern, corresponding to loading from train wheels, bogies, and passages. Vibration velocity distribution in the horizontal direction at three train speeds clearly follows an exponential curves. Most of the power spectrums of vibration velocity at various locations are mainly concentrated at harmonic frequencies. The change in water level has slight impaction on the peak spectrum of vibration velocity at harmonic frequencies. The vibration power induced by train loads are transmitted, absorbed, and weakened to a certain extent through embankment and piled raft structure. The dynamic response character of embankments are affected by their self-vibration characteristics and the dynamic bearing capacity of the piled raft structure.
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Abstract: The rapid growth of using the short links in steel buildings due to their high shear strength and rotational capacity attracts the attention of structural engineers to investigate the performance of short links. However, insignificant attention has been oriented to efficiently developing a comprehensive model to forecast the shear strength of short links, which is expected to enhance the steel structures’ constructability. As machine learning algorithms was successfully used in various fields of structural engineering, the current study fills the gap in estimating the shear strength of short links using sophisticated machine learning algorithms. The deriving factors such as web and flange slenderness ratios, the flange-to-web area ratio, the forces in web and flange, and the link length ratio were investigated in this study, which is imperative to formulate an integrated prediction model. Consequently, the aim of this study utilizes advanced machine learning (ML) models (i.e., Extreme Gradient Boosting (XGBOOST), Light Gradient Boosting Machine (LightGBM), and Artificial Neural Network (ANN) to produce accurate forecasting for the shear strength. In this study, publicly available datasets were used for the training, testing, and validation. Different evaluation metrics were employed to evaluate the prediction’s performance of the used models, such as Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), and Coefficient of Determination (R2). The prediction result displays that the XGBOOST and LightGBM provided better, and more reliable results compared to ANN and the AISC code. The XGBOOST and LightGBM models yielded higher values of R2, lower (RMSE), (MAE), and (MAPE) values and have shown to perform more accurate. Therefore, the overall outcomes showed that the LightGBM outperformed the XGBOOST model. Moreover, the overstrength ratio predicted by the LightGBM showed an excellent performance compared to the Gene Expression and Finite Element-based models. The developed models are vital for practitioners to predict the shear strength accurately, which pave the road towards wider application for automation in the steel buildings.
TL;DR: The proposed approach combines the analytic hierarchy process (AHP) with the fuzzy technique for order of preference by similarity to ideal solution (fuzzy TOPSIS) in a multicriteria decision-making (MCDM) model to support the decision makers in construction projects differentiating among a variety of deep excavation supporting systems (DESSs).
Abstract: This paper introduces and further applies an approach to support the decision makers in construction projects differentiating among a variety of deep excavation supporting systems (DESSs). These kinds of problems include dealing with uncertainty in data, multi-criteria affecting the decision, and multi-alternatives to select one from them. The proposed approach combines the analytic hierarchy process (AHP) with the fuzzy technique for order of preference by similarity to ideal solution (fuzzy TOPSIS) in a multicriteria decision-making (MCDM) model. The MCDM model emphasize the ability to combine expert knowledge, cost calculations, and laboratory test results for soil properties to achieve the scope. The model proved it had a superior ability to deal with the complexity and vague data that are related to construction projects. Furthermore, it was applied to a real case study for a governmental housing project in Egypt. Secant pile walls, sheet pile walls, and soldier piles and lagging are selected and studied as being the most common DESSs and as they satisfy the project requirements. The model utilized four criteria and fourteen comparing factors, including site characteristics, safety, cost, and environmental impacts. Based on the results of the model application on the investigated case study, a decision was reached that using secant piles as a supporting system in this project is mostly preferred. Furthermore, sheet pile wall, and soldier piles and lagging, come next in the ranking order. A sensitivity analysis is carried out to investigate how sensitive the results are to the criteria weights. In addition, the paper discusses in detail the reasons and factors which affect and control the decision-making process.
TL;DR: In this paper , the authors proposed intelligent models for fly ash (FA)-based concrete comprising cement, fine and coarse aggregates (FAg and CAg), FA, and water as mix constituents based on environmental impact (P) considerations.
Abstract: Infrastructure design, construction and development experts are making frantic efforts to overcome the overbearing effects of greenhouse gas emissions resulting from the continued dependence on the utilization of conventional cement as a construction material on our planet. The amount of CO2 emitted during cement production, transportation to construction sites, and handling during construction activities to produce concrete is alarming. The present research work is focused on proposing intelligent models for fly ash (FA)-based concrete comprising cement, fine and coarse aggregates (FAg and CAg), FA, and water as mix constituents based on environmental impact (P) considerations in an attempt to foster healthier and greener concrete production and aid the environment. FA as a construction material is discharged as a waste material from power plants in large amounts across the world. Its utilization as a supplementary cement ensures a sustainable waste management mechanism and is beneficial for the environment too; hence, this research work is a multi-objective exercise. Intelligent models are proposed for multiple concrete mixes utilizing FA as a replacement for cement to predict 28-day concrete compressive strength and life cycle assessment (LCA) for cement with FA. The data collected show that the concrete mixes with a higher amount of FA had a lesser impact on the environment, while the environmental impact was higher for those mixes with a higher amount of cement. The models which utilized the learning abilities of ANN (-BP, -GRG, and -GA), GP and EPR showed great speed and robustness with R2 performance indices (SSE) of 0.986 (5.1), 0.983 (5.8), 0.974 (7.0), 0.78 (19.1), and 0.957 (10.1) for Fc, respectively, and 0.994 (2.2), 0.999 (0.8), 0.999 (1.0), 0.999 (0.8), and 1.00 (0.4) for P, respectively. Overall, this shows that ANN-BP outclassed the rest in performance in predicting Fc, while EPR outclassed the others in predicting P. Relative importance analyses conducted on the constituent materials showed that FA had relatively good importance in the concrete mixes. However, closed-form model equations are proposed to optimize the amount of FA and cement that will provide the needed strength levels without jeopardizing the health of the environment.
TL;DR: In this article , the authors developed empirical predictive models for the Marshall parameters, i.e., Marshall Stability (MS) and Marshall Flow (MF) for Asphalt Base Course (ABC) and Asphalt Wearing Course (AWC) of flexible pavements.
Abstract: The traditional method to obtain optimum bitumen content and the relevant parameters of asphalt pavements entails time-consuming, complicated and expensive laboratory procedures and requires skilled personnel. This research study uses innovative and advanced machine learning techniques, i.e., Multi-Expression Programming (MEP), to develop empirical predictive models for the Marshall parameters, i.e., Marshall Stability (MS) and Marshall Flow (MF) for Asphalt Base Course (ABC) and Asphalt Wearing Course (AWC) of flexible pavements. A comprehensive, reliable and wide range of datasets from various road projects in Pakistan were produced. The collected datasets contain 253 and 343 results for ABC and AWC, respectively. Eight input parameters were considered for modeling MS and MF. The overall performance of the developed models was assessed using various statistical measures in conjunction with external validation. The relationship between input and output parameters was determined by performing parametric analysis, and the results of trends were found to be consistent with earlier research findings stating that the developed predicted models are well trained. The results revealed that developed models are superior and efficient in terms of prediction and generalization capability for output parameters, as evident by the correlation coefficient (R) (in this case >0.90) for both ABC and AWC.