Traditional Construction Methods Research Articles

A Piecewise Linearization Based Method for Crossed Frequency Admittance Matrix Model Calculation of Harmonic Sources.

The integration of large-scale power electronic equipment has intensified harmonic issues in power systems. Accurate harmonic models are fundamental for evaluating and mitigating harmonic problems, but existing models still exhibit deficiencies in harmonic mechanism, model complexity and accuracy. This work proposes a calculation method of crossed frequency admittance matrix (CFAM) analytical model based on piecewise linearization, aiming to achieve accurate modeling of phase-controlled power electronic harmonic sources. Firstly, the traditional CFAM model construction methods are introduced, and the shortcomings in harmonic modeling are discussed. Subsequently, the parameter-solving process of the CFAM analytical model based on piecewise linearization is proposed. This method improves the accuracy of harmonic modeling and simplifies the construction process of the analytical model. Furthermore, taking single-phase and three-phase bridge rectifiers as examples, CFAM analytical models under intermittent and continuous load current conditions are established, respectively, and the unified harmonic models for both conditions are summarized. Finally, case studies of rectifier harmonic sources under varying circuit control parameters and supply voltage distortions are conducted through Matlab/Simulink and experiments. The results demonstrate that the proposed method provides higher accuracy and more stable performance for harmonic current estimation compared with the traditional CFAM model and constant current source model.

Modular Housing: Is it a Catholicon for the Housing Stock Deficit in Lagos, Nigeria?

Modular homes appear to be a promising housing option that keeps pace with the rapid rate of urbanisation, especially in growing cities like Lagos. Consequently, this study investigates how professionals in the built environment perceive modular homes, with an emphasis on how they could be a potential solution to the deficit of housing in Lagos State, Nigeria. The primary goals are to navigate the multifaceted aspects of modular housing, examine its merits, identify barriers to its adoption, and gauge its feasibility in the Nigerian context. To achieve these, 200 questionnaires were disseminated among professionals in the built environment in Lagos, and 147 (73.5%) were returned. Descriptive and inferential statistics were used for data analysis. Key findings revealed that modular housing, due to its rapid construction timelines, is a time-efficient solution. However, concerns about initial investment costs and design flexibility emerged as potential barriers to adoption. There is also a clear acknowledgement of the environmental benefits of modular housing, with reduced construction waste indicating its sustainability. Factor analysis is pivotal for revealing deeper insights. For instance, respondents’ lack of in-depth knowledge about modular housing was a primary barrier, accounting for 62.67% of the variance. Similarly, advocacy for modular housing was seen as a significant strategy for its adoption, with 52.38% variability. However, challenges remain. A predominant belief among respondents is that modular homes might be inferior in quality to homes built using traditional construction methods. Furthermore, bureaucratic hurdles in obtaining permits could deter investors and developers from undertaking modular housing projects. In conclusion, although modular housing offers a beacon of hope for solving mass housing problems in burgeoning cities like Lagos, clear challenges remain. This study recommends more vigorous advocacy efforts, targeted government intervention, and robust public-private partnerships.

Robot Tracking Navigation Based on Visual SLAM

Visual Simulation Localization and Mapping technology is an important research direction in the fields of computer vision and robotics. The research background of this technology mainly stems from the insufficient accuracy of traditional positioning and map construction methods in the face of environmental changes. With the development of artificial intelligence, this technology has been involved in multiple disciplinary fields. Since 2015, researchers have begun to focus on the combination with deep learning to improve algorithm robustness, dynamic scene planning and other aspects. This article which introduces the existing tracking car system and visual SLAM (Simultaneous Localization and Mapping) system framework explores the robot tracking and navigation technology based on visual SLAM. Visual SLAM technology provides robots with an autonomous navigation solution that does not require external sensors through feature extraction, real-time positioning, mapping, and closed-loop detection. This article also discusses the advantages and disadvantages of visual SLAM navigation, and offers insights into the future development of VSLAM technology in robot tracking navigation.

Assessing 3D-printed concrete process parameters through Discrete Element Modeling

Three-dimensional concrete printing (3DCP) has emerged as a promising manufacturing technique in the civil engineering sector, offering myriad advantages over traditional construction methods. Despite its potential, challenges persist in optimizing the manufacturing stage of 3DCP, including determining optimal fresh concrete rheology, layer thickness, print path, and nozzle characteristics. In this study, we incorporate the Discrete Fresh Concrete model (DFC) into our Discrete Element Method (DEM) code to simulate the rheological behavior of fresh printable concrete during printing, aiming to explore a comprehensive range of process parameters and their combinations to enhance understanding and optimization 3DCP process. Through a series of simulations, we systematically vary some of the process variables such as concrete mix design, nozzle specifications, and printing speed to investigate their influence on the printed output quality. By the obtained results, we aim to identify the key parameters that significantly affect the process, offering insights for refining 3DCP technologies and helping guide their development. We believe methodologies of the type as shown here may be an efficient tool for advancing 3DCP technologies.

Collaborative Construction Method of Biomedical Knowledge Graph Based on Multi-Blockchain

The collaborative construction method of knowledge graph based on blockchain is crucial for the safe and efficient construction of biomedical knowledge graph (BioKG), and has received widespread attention from the academic and medical circles. At present, the data sources of BioKG are very wide. While using multi-source data to collaboratively build a knowledge graph, data redundancy problems often occur. Once data from a large number of sources is available, it becomes difficult for the system to ensure data consistency in a reliable way. In addition, the increase in inconsistent and redundant data also reduces the efficiency of collaboration in the system. The traditional blockchain-based collaborative construction method of BioKG is difficult to effectively solve these problems. Multi-blockchain technology provides a new solution for the construction of collaborative knowledge graphs in the biomedical field, which is characterized by good scalability and diversified applications. To this end, we propose a multi-person collaboration construction method for BioKG based on multi-blockchain, named collaborative construction method chain (CCMC). CCMC uses the multi-chain structure and clock cycle control method of the blockchain to separate user operations and system verification, and uses the read-write separation function to improve the consistency of the collaborative version. In addition, we propose a semantic fusion method that effectively reduces data redundancy in the collaborative process and improves the efficiency of BioKG collaborative construction. Performance testing and comparative evaluation confirmed that compared with previous methods, CCMC significantly improves the creation of collaborative knowledge graphs and meets the requirements of multi-person collaborative construction.

Experimental Study on the Strength Characteristics of Cast-In-Situ Mortar Specimens in a Slurry Environment

As coal resource development progresses deeper underground, the increasing depth of mine shafts poses significant challenges to the safety and stability of traditional shaft construction methods, further compounding operational difficulties. In this context, cast-in-situ concrete shaft walls in a slurry environment have emerged as an effective solution. The strength of these shaft walls is a crucial parameter for assessing their safety. To explore this, experiments were conducted on slurry preparation and mortar casting (used here as a substitute for concrete) under three different conditions: slurry environment, pure water environment, and dry environment. The cast specimens underwent compressive, tensile, shear, and microscopic observation tests to analyze the strength development patterns of the mortar specimens in these varied casting environments. The study yielded several key findings: As the casting environment becomes more complex, the strength of the mortar specimens gradually decreases. Specifically, specimens cast in a slurry environment exhibit strengths approximately 15% to 20% lower than those cast in a dry environment, although both environments show similar trends in strength development over time. Across all casting environments, the initial strength loss of the specimens is significant, while the rate of strength loss decreases in the later stages; the strength loss is minimal in specimens cast in a pure water environment and reaches its maximum in those cast in a slurry environment. Additionally, in specimens cast in a slurry environment, air void diameter tends to polarize, and the distribution of air void is denser compared to the other two environments. In conclusion, cast-in-situ mortar in a slurry environment exhibits the lowest strength and the greatest strength loss compared to specimens cast in dry and pure water environments. Nonetheless, the strength development trends over time remain similar across all conditions, providing theoretical and technical support for the construction of shaft walls in slurry environments.

Towards Sustainable Road Construction: Literature Review on Addressing Environmental Impacts and Promoting Green Technologies

Abstract This study investigates the environmental impacts of road construction and explores strategies to promote green technologies in Indonesia. Traditional road construction methods, particularly those using asphalt and cement, have significant energy consumption and greenhouse gas emissions. This study conducted a thorough literature review to investigate green technologies and best practices to reduce emissions during the construction process. The literature review included articles published from 2004 to 2024, drawn from various national and international academic sources, and adopted the PRISMA method. It was found that key strategies include recycled materials, warm mix asphalt technology, permeable pavement systems, green roofs, and energy-efficient equipment. To mitigate these impacts, the study recommends using recycled materials, adopting energy-efficient technologies, implementing sustainable design practices, and conducting life cycle assessments. Although Indonesia has made progress, challenges such as limited funding, technology, and coordination remain. Overcoming these challenges requires increased commitment and funding, capacity building, better coordination, and stricter regulations. Future studies should investigate the development and implementation of innovative green technologies, including advanced recycled materials, more efficient construction equipment, and innovative pavement design techniques.

Hygrothermal and airtightness performance assessment of prefabricated lightweight wall systems for cold climates

Wooden prefabricated wall panels (WPWP) are increasingly used in the residential sector due to their practical and environmental benefits compared to traditional on-site systems. However, there is limited information regarding their influence on the building envelope´s performance, particularly at panel junctions. Therefore, this study investigated the hygrothermal and airtightness performance of two WPWP systems, one standard and one optimized, focusing mainly on panel-to-panel junctions. Laboratory heat and vapor transfer, thermography, and airtightness assessments were conducted over two full-scale prefabricated walls and a reference on-site traditional wall. In addition, hygrothermal simulations were carried out utilizing WUFI Pro, to compare laboratory heat and vapor transfer results with theoretical values. The results showed that the prefabricated walls reduced heat loss through the air leakage sources of the junctions by up to 17.8% and a reduction of air infiltration by up to 76%, compared to the on-site reference. It was also found that using polyurethane foam in panel junctions generated thermal benefits. Still, care should be taken with the amount of polyurethane foam used to avoid increasing relative humidity. In addition, bio-based insulation (hemp fiber) used in the optimized prefabricated wall exhibited adequate hygrothermal performance as it slightly improved the thermal resistance without increasing the risk of interstitial condensation. Overall, the findings demonstrated that wooden prefabricated wall systems can significantly reduce heat loss and air infiltration, highlighting their potential as a hygrothermally efficient alternative to traditional construction methods.

Sustainable сonstruction тechnologies: an innovative GREEN CODE precast reinforced concrete system

Sustainable construction technologies, which today form the trends in the development of the global construction industry and related economic, political, environmental and social aspects, are firmly based on the principles of implementing the main directions: durability, climateefficient construction, carbon neutrality, energy conservation, energy efficiency, resource conservation, recycling. The concept of tomorrow is precast concrete construction systems, unique in their content and individuality, capable of providing reliable design, high profitability, high requirements for indoor climate and energy balance of buildings. The article highlights the GREEN CODE system – one of the modern industrial technologies of precast concrete, which allows you to build buildings and structures in accordance with the trends in the development of the global construction industry. In production and construction logistics, the GREEN CODE system has a higher potential for economic efficiency than traditional construction methods, and allows additional resource savings.

Confidence interval estimation for the difference of censored zero-inflated gamma distributions

This paper studies the problem of constructing confidence intervals (CIs) for the difference between coefficients of variation (CV) of two censored zero-inflated gamma distributions. Firstly, we propose a Fiducial inference based method that extends the traditional CI construction method of gamma distribution to the scenario of zero-inflated gamma distributions with censored data. Secondly, we propose a Box-Cox transformation based method, in which not only the sample data needs to be transformed, but also the detection limit of gamma distribution with censored data accordingly. Thirdly, we investigate the Method of Variance Estimate Recovery (MOVER), to combine the previous two gamma distribution CI estimates with three binomial distribution CI estimates, and obtain six combination methods. Furthermore, we conduct Monte Carlo simulations to evaluate the performances of the proposed methods, the results indicate that all CI construction methods achieve satisfactory performances in terms of coverage probability, average length and tail error rates. Finally, we perform real data analysis using 11 years of precipitation data from Zhengzhou and Lhasa.

Post occupancy evaluation of the relocatable modular construction method for temporary educational facilities

PurposeThis study aims to evaluate user satisfaction and perceptions of the relocatable modular school (RMS) system in South Korea. It seeks to identify key factors for improvement to enhance the performance of this innovative construction method, particularly in the temporary educational sector.Design/methodology/approachA comprehensive post occupancy evaluation (POE) was conducted, considering functional spaces, aesthetics, indoor environmental quality and technical aspects. This study also includes a comparative analysis with conventional school buildings to provide insights into the efficiency and potential of RMS.FindingsRelocatable modular schools can achieve the equivalent quality of building performance and user satisfaction as those built using traditional construction methods. The satisfaction performance of both methods falls within the “satisfied” level, with mean scores ranging from 3.35 to 3.90. Notably, when compared to ES, RMS exhibits a marginally lower satisfaction level in functional performance but a higher satisfaction level in the areas of aesthetics, IEQ and technical performance. Key factors identified for enhancement include optimizing classroom shape, balancing the window-to-wall ratio, enhancing natural ventilation and addressing floor noise and vibration concerns.Originality/valueThis research is pioneering in evaluating user satisfaction and perceptions of RMS in South Korea, a relatively unexplored area. By taking a holistic approach and incorporating comparative analysis with traditional school buildings, the study provides valuable insights into the practical application and potential of modular construction in educational facilities. The results contribute significantly to the advancement of high-performance modular educational buildings, informing practitioners and stakeholders of user demands and guiding future developments in the field.

Cloud detection sample generation algorithm for nighttime satellite imagery based on daytime data and machine learning application

Highly accurate nighttime cloud detection in satellite imagery is challenging due to the absence of visible to near-infrared (0.38–3 μm, VNI) data, which is critical for distinguishing clouds from other ground features. Fortunately, Machine learning (ML) techniques can more effectively leverage the limited wavelength information and show high-accuracy cloud detection based on vast sample volume. However, accurately distinguishing cloud pixels solely through thermal infrared bands (8–14 μm, TIR) is challenging, and acquiring numerous, high-quality and representative samples of nighttime images for ML proves to be unattainable in practice. Given the thermal infrared radiation transmission process and the fact that daytime and nighttime have the same source of radiance in TIR, we propose a sample generation idea that uses daytime images to provide samples of nighttime cloud detection, which is different from the traditional sample construction methods (e.g., manually label, simulation and transfer learning method), and can obtain samples effectively. Based on this idea, nighttime cloud detection experiments were carried out for MODIS, GF-5 (02) and Himawari-8 satellites, respectively. The results were validated by the Lidar cloud product and manual labels and show that our nighttime cloud detection result has higher accuracy than MYD35 (78.17%) and the ML model trained by nighttime manual labels (75.86%). The accuracy of the three sensors is 82.19%, 88.71%, and 79.34%, respectively. Moreover, we validated and discussed the performance of our algorithm on various surface types (vegetation, urban, barren and water). The results revealed that the accuracy of the three sensors over barren was found to be poor and varied with surface types but overall high. Our study can provide a novel perspective on nighttime cloud detection of muti-spectral satellite imagery.

Traditional and modern methods of construction: Comparative study of the sustainability of single‐family homes

AbstractModern construction methods, such as additive manufacturing, are aimed to enhance the efficiency and quality of construction processes while potentially reducing waste generation and material use, thus contributing to sustainability performance (SP). However, comprehensively understanding the sustainability trade‐offs associated with these methods is crucial for guiding both research and practical applications toward sustainable development. This study aims at quantifying the SP of various construction methods for housing, including traditional, prefabricated, and additive manufacturing approaches. A sustainability index, integrating economic, environmental, social, and technological criteria, is utilized to assess different alternatives. Findings reveal promising aspects of 3D printing technologies, such as potential cost reductions through scale increase and process optimization, minimized material waste generation, creation of skilled employment opportunities, and enhanced construction flexibility and ease. Nevertheless, challenges persist, notably significant greenhouse gas emissions and limited supplier availability. Addressing these challenges is imperative for advancing the sustainable implementation of additive manufacturing in construction.

SWOT-AHP Analysis of BIM Technology Utilization in the Japanese Construction Industry

Due to its advantages, Building Information Modeling (BIM) technology has been widely accepted and developed globally. BIM is transforming traditional construction methods and enabling the resolution of issues such as labor shortages, overwork, and rising costs, which the Japanese construction industry faces. In Japan, there is an active push to promote the application of BIM technology; however, its widespread adoption is encountering numerous challenges and barriers. Although BIM technology has been applied and developed globally, there is still a lack of systematic analysis on the specific challenges and strategies of its implementation in the Japanese construction industry. This study reviews the literature on the application of BIM worldwide and surveys stakeholders in the Japanese construction industry, examining strengths, weaknesses, opportunities, and threats. By integrating Analytic Hierarchy Process (AHP) and SWOT analysis, this research prioritizes these factors, assesses their impact on the diffusion of BIM technology, and provides in-depth analysis and insights for the promotion and development of BIM technology in Japan.

Full-Color Emissive Zirconium-Organic Frameworks Constructed via in Situ "One-Pot" Single-Site Modification for Tryptophan Detection and Energy Transfer.

Organic linker-based luminescent metal-organic frameworks (LMOFs) have received extensive studies due to the unlimited species of emissive organic linkers and tunable structure of MOFs. However, the multiple-step organic synthesis is always a great challenge for the development of LMOFs. As an alternative strategy, in situ "one-pot" strategy, in which the generation of emissive organic linkers and sequential construction of LMOFs happen in one reaction condition, can avoid time-consuming pre-synthesis of organic linkers. In the present work, we demonstrate the successful utilization of in situ "one-pot" strategy to construct a series of LMOFs via the single-site modification between the reaction of aldehydes and o-phenylenediamine-based tetratopic carboxylic acid. The resultant MOFs possess csq topology with emission covering blue to near-infrared. The nanosized LMOFs exhibit excellent sensitivity and selectivity for tryptophan detection. In addition, two component-based LMOFs can also be prepared via the in situ "one-pot" strategy and used to study energy transfer. This work not only reports the construction of LMOFs with full-color emissions, which can be utilized for various applications, but also indicates that in situ "one-pot" strategy indeed is a useful and powerful method to complement the traditional MOFs construction method for preparing porous materials with tunable functionalities and properties.

Advancing Heritage and Vernacular Studies: The Role of the Journal of Mediterranean Cities in Sustainable Urban Development

The launch of the "Journal of Mediterranean Cities" represents a jumping step in the fields of heritage and vernacular studies in the Mediterranean area. Known for its heritage, impressive architecture, city context and vibrant urban settings the Mediterranean region offers a distinctive backdrop for exploring the connections between heritage preservation, architectural innovation, urban renewal, traditional construction methods and community conservation. The journal aims to address knowledge gaps by promoting collaboration among academics, architects, urban planners and policymakers to preserve the identity of the Mediterranean despite the fact of advocating for urban growth. By integrating disciplines like architecture, urban planning and regional development the Journal strives to encourage analyses and innovative problem-solving approaches that align with initiatives such as Urban Development (SUDs) and Union for the Mediterranean (UfM) Action Plans. Highlighting the importance of preserving communities and cultural heritage this journal will highlight the significance of cooperation among stakeholders in shaping the future of Mediterranean cities. The journal is set to be a resource for scholars, policymakers and professionals in the field of architecture and urbanism who are committed to enhancing the fabric of urban spaces in this region.

Research on the Construction Method of Curriculum Teaching Knowledge Graph Based on Bi-LSTM and CNN Algorithm

The aim of the paper is to explore a method of constructing a curriculum teaching knowledge graph by combining Bi-LSTM and convolutional neural network (CNN) algorithm. The field of education is constantly seeking innovation to improve teaching results and student learning experience. Knowledge graph, as an advanced technology of structured representation of knowledge, is expected to provide effective support for teaching management and personalized learning. First, this paper introduces the background and significance of the curriculum teaching knowledge graph. By establishing knowledge maps, we can more clearly present the knowledge system and correlation in the curriculum, which helps teachers to design more targeted teaching content and provide personalized learning paths for students. However, traditional knowledge graph construction methods are often faced with problems such as incomplete information capture and inaccurate semantic association, so it is necessary to introduce advanced deep learning algorithms to improve the quality of knowledge graph. Secondly, this paper elaborates on the construction method of fusion Bi-LSTM and CNN algorithm. Bi-LSTM, as a recurrent neural network capable of capturing sequence information, can better model the evolution process of knowledge in the course. As a CNN is good at extracting local features, CNN can effectively capture the spatial structure information in the knowledge graph. By integrating two, we can improve the expression ability and reasoning accuracy of knowledge graph. Further, the experimental results show that the fusion Bi-LSTM and the CNN algorithm have significantly improved the accuracy of information capture and inference compared with the traditional method. In summary, this paper proposes an innovative construction method of curriculum teaching knowledge graph by integrating Bi-LSTM and CNN algorithm, which provides new ideas and solutions for informatization and personalized teaching in the field of education. In the future, the applicability of this method in different disciplines and teaching scenarios can be further discussed, and more advanced technologies can be combined to continuously improve and expand the research.

Safety-risk assessment system for prefabricated building construction in China

PurposePrefabricated building (PB) uses factory production and onsite assembly, which differs from traditional construction methods. This special construction approach may lead to dissimilar safety risks and challenges. Traditional safety assessment methods may not adequately and accurately assess the safety risks of PB construction. This paper aims to develop a new concept and methodology for targeted improvement in assessing PB safety risks.Design/methodology/approachRisk factors and indicators were established based on literature review and expert inputs. A structural equation model (SEM) was developed to investigate the relationships among three main risk categories: objects, workers and management. SEM analyzed the intricate associations between indicators and deepened understanding of safety risks. The model was tailored for China’s PB construction projects to enhance safety-risk management.FindingsThe cloud model evaluation validated the SEM model. A PB case study project tested and verified the model, evaluated its efficacy and quantified its safety performance and grade. We identified significant safety risk impacts across the three risk categories, safety-control level and specific areas that require improvement. The SEM model established a robust safety evaluation indicator system for comprehensive safety assessment of PB construction.Practical implicationsPractical recommendations provide valuable insights for decision-makers to enhance construction efficiency without compromising safety. This study contributed to the conceptual foundation and devised a novel method for evaluating safety performance in PB construction for safer and more efficient practices.Originality/valueThis study departed from the traditional method of calculating weights, opting instead for the SEM method to determine the weights of individual risk indicators. Additionally, we leveraged the cloud model to mitigate the influence of subjective factors in analyzing questionnaire survey responses. The feasibility and reliability of our proposed method were rigorously tested and verified by applying it to the PB case.

3D constructing: Exploring the potential of 3D concrete and clay printing with generative design for architectural innovation

The architectural and construction sectors are experiencing a transformative shift with the advent of 3D constructing. This research delves into the potential of 3D printing technologies when combined with generative design principles to reshape traditional construction methods. Through a comprehensive lens, the study evaluates the generative design workflow, decision-making algorithms, machines capacities and material considerations. Results indicate that, when synergized with generative design, these technologies offer sustainable, efficient, and tailored solutions. The use of accesible materials, such as clay and cement, demonstrates their superiority over conventional alternatives. However, the challenge lies in harmoniously integrating these innovations into architectural practice, ensuring a balance between cutting-edge advancements and practical application. Conclusively, this new approach heralds a future of sustainable and user-centric design, but its full potential can only be tapped with a holistic strategy that encompasses both design and material facets.

Computational Design with Kurtboğaz: The Generation of Timber Structures with an Aggregative Design Algorithm

This paper investigates the form-finding capacity of the traditional timber-joint construction method, Kurtboğaz, aiming to explore new architectural forms and possibilities through computational design techniques to preserve vernacular construction methods and integrate them into contemporary architecture. It presents an Aggregative Design Algorithm (ADA) that creates different structures based on designer rules and simple assembly rules of Kurtboğaz, leading to unique emergent forms through random rule application. The paper also explores how reinforcement learning, a type of machine learning, can improve this design process through a theoretical framework. The study tries to use a rule-based generative algorithm to explore the modular and reconfigurable characteristics of the Kurtboğaz. The ADA enables random rule application, leading to diverse forms. However, several challenges may be encountered during the application of ADA because of its random aggregation, such as collision avoidance, structural integrity, boundary detection, and the optimization of structural parameters. The study suggests using Reinforcement Learning (RL) in the ADA framework to address these problems. Incorporating RL is anticipated to enable the algorithm to adaptively learn and optimize the form-finding process, enhancing the performance and applicability of the Kurtboğaz method in contemporary architectural practice. In the future, with this generative process described by the study, designs that create spatial differences with the help of walls, floors, and rooms on a human scale can be realized. The study also plans to explore the synergy between craftsmanship and digital fabrication in the future