Model Building Research Articles

Wind tunnel experiment of ventilation rate and airflow characteristics in natural ventilation induced by wind and buoyancy effects

In this paper, the investigation focused on natural ventilation induced by wind and buoyancy effects through a wind tunnel experiment. A scaled-down model of a single-zone building featuring two opposing openings at varying heights was subjected to uniform heating from a source on the interior floor. Wind pressure coefficient differences were measured in a sealed model to assess the wind’s driving force. Indoor and outdoor temperatures were recorded to ascertain the buoyancy-driven driving force. The ventilation rate was assessed using the tracer gas constant emission method, while airflow characteristics were quantified through particle image velocimetry (PIV). The comparison between predicted and measured ventilation rates revealed generally good accuracy in cases where wind assists buoyancy. However, errors were evident in cases of opposing wind and buoyancy due to neglecting the effect of wind turbulence. The airflow in cases of opposing wind and buoyancy effects demonstrates the most dynamic changes, as illustrated through flow visualizations and transient temperature fluctuations at the openings.

Research on Energy-Saving Transformation of Rural Residential Building Envelope Structures and Heating Modes in Northeast China

Rural areas in Northeast China present a large demand for heating energy in winter, but there are problems in such areas with poor thermal performance of building envelopes and poor indoor thermal comfort. In addition, coal-fired boilers are still widely used. China’s “Dual-Carbon Goals” and “Clean Heating” policy call for the creation of a green and comfortable living environment for rural residential buildings. This paper considers the impact of the improvement of the thermal performance of envelope structures on the initial investment of the transformation program and the rated power of the ASHP and proposes an energy-saving transformation method to replace traditional coal-fired boilers with the ASHP on the basis of the improvement of the thermal performance of envelope structures. By establishing a typical rural residential building model in Northeast China, this energy-saving method is simulated based on TRNSYS. The results show that the payback period of investment of the transformation method of “envelope structure + heating system” is not superior to that of the transformation method of only improving the thermal performance of the envelope structure, but it has advantages in the comprehensive life-cycle benefits and it has great advantages in improving the satisfaction of rural residents in the use of heating systems.

Applications for Semantic 3D Streetspace Models and Their Requirements—A Review and Look at the Road Ahead

In addition to geometric accuracy, topological information, appearance and georeferenced data, semantic capabilities are key strengths of digital 3D city models. This provides the foundation for a growing number of use cases, far beyond visualization. While these use cases mostly focused on models of buildings or the terrain so far, the increasing availability of data on roads and other transportation infrastructure opened up a range of emerging use cases in the field of semantic 3D streetspace models. While there are already a number of implemented examples, there is also a potential for new use cases not yet established in the field of 3D city modeling, which benefit from detailed representations of roads and their environment. To ensure clarity in our discussions, we introduce an unambiguous distinction between the terms `application domain’, `use case’, `functionality’ and `software application’. Based on these definitions, use cases are categorized according to their primary application domain and discussed with respect to relevant literature and required functionalities. Furthermore, requirements of functionalities towards semantic 3D streetspace models are determined and evaluated in detail with regard to geometric, semantic, topological, temporal and visual aspects. This article aims to give an overview on use cases in the context of semantic 3D streetspace models and to present requirements of respective functionalities, in order to provide insight for researchers, municipalities, companies, data providers, mapping agencies and other stakeholders interested in creating and using a digital twin of the streetspace.

Comparison of energy prediction models for residential buildings: A case study in Himachal Pradesh, India

Abstract The demand for consumption of energy has been increasing globally with the tremendous increase in population. Different studies have proved that inadequate energy management and planning may lead to energy crisis which is a result of inadequacies in energy prediction. Accurate prediction of energy demand is important as underestimation may lead to shortage in supply and overestimation may lead to overinvestment in energy generation. Various available literature has been reviewed for determining the various factors responsible for affecting the energy consumption of residential buildings. Based on the factors determined, survey questionnaire has been formulated and survey was conducted in 400 residential buildings in one of northern states of India, i.e., Himachal Pradesh. It was observed by reviewing various studies that different models developed for energy consumption by different researchers were based on either of the three approaches, namely, engineering-based, AI-based, and hybrid approaches. Three tools namely, case-based reasoning, artificial neural network, and multilinear regression, based on these approaches were individually used for developing the model in this study, and their prediction results were compared. It was observed that the accuracy in the overall predicted results was highest in the proposed ANN model, followed by CBR model, and MLR model, with an overall accuracy of 99.93%, 96.3%, and 91.7%, respectively. The error obtained in the predicted results using ANN, CBR and MLR ranges from -4.0 to +3.0%, -15.0 to +26.0%, -30.0% to 20.0%, respectively. The overall RMSE of ANN, CBR, and MLR model was 1.44%, 11.7%, and 19.5%, respectively. It is concluded that ANN model is best suitable for predicting the short and long-term energy consumption with very high accuracy, as compared to the CBR and MLR. The results discussed in this study can be advantageously used for enhancing the consumption of operational energy in existing as well as proposed buildings.

Assessing the Fire Properties of Various Surface Treatments on Timber Components in Ancient Chinese Buildings: A Case Study from the Xianqing Temple in Changzhi, Shanxi, China

Traditional and modern coatings play a key role in enhancing the fire resistance of ancient Chinese buildings. However, further comparative analysis is needed on the fire properties of the two coatings and their effects on different timber structural components. This study focuses on the main hall of the Shanxi Changzhi Xianqing Temple, a typical traditional column and beam construction built between the Song and Jin periods. Firstly, the combustion characteristics of various timber structural component samples with different surface treatments (traditional “Yi-ma-wu-hui” and modern flame retardants) were analyzed using cone calorimeter. Secondly, the fire development process of the Xianqing Temple building model was analyzed by a fire dynamics simulator (FDS), and the effect mechanism of different surface treatments on the burning process was further studied. The results show that the fire resistance of timber structural components is significantly improved after modern and traditional surface treatments. The traditional method is more effective in delaying the peak heat release rate and reducing the surface temperature during combustion, while the modern surface treatment significantly prolongs the ignition time of the timber structural components. The FDS results confirm that modern and traditional surface treatments significantly improve the fire resistance of the building, delaying the flashover time by about 300 s, with no collapse occurring within 800 s. In addition, the fire resistance of buildings after traditional surface treatment is better compared to traditional methods. The above research results can provide direct data support for the selection and optimization of fireproof coatings and treatment methods for ancient buildings.

Transformative social work education through a joint MSW programme: from participatory action research to policy and practice innovation in China

ABSTRACT In China, social work was reestablished in 1988. Despite the number of social work programmes increased very fast throughout the 1990s, there was a huge need for social work-trained educators. The Hong Kong Polytechnic University and Peking University launched the MSW (China) Programme in 2000 to create a critical mass of social work educators who would assume future leadership in social work development in mainland China. Participatory action research was employed as a methodology to explore how social work education could transform students into catalysts for social work education and social development. Purposive sampling was adopted, and seven cohorts totaling 238 graduates who graduated from the programme were the participants in this research. Data collection and analysis were carried out throughout the action research process. We found the triple capacity building model to be a good pedagogy to foster transformation among participants, and to steer the knowledge co-creation, practice innovation, policy reform, and social change through social work education. In this paper, we will provide an overview of the background, vision, and mission, as well as the teaching pedagogy of the programme and a case study to demonstrate how social work education has an impact on policy and practice innovation in China.

Research on Summer Environmental Thermal Comfort Model of Buildings in Hot Summer and Cold Winter Area

Research on Summer Environmental Thermal Comfort Model of Buildings in Hot Summer and Cold Winter Area

Transcending disciplinary boundaries: Nature based solutions (NbSs) for Caribbean coastal resilience

Caribbean Small Island Developing States are experiencing multiple interconnected crises including susceptibility to natural hazards, increased vulnerability to climate change, biodiversity loss and reduced accessibility to development funding. Nature-based Solutions can provide an integrated approach to boost Caribbean coastal resilience as they are economic and practical solutions that capably balance competing interests. This study used a participatory method, Group Model Building, to engage stakeholders across varying disciplines and levels of decision-making to collaboratively identify the underlying causes of low Nature-based Solutions uptake regionally and potential intervention mechanisms. Six overarching factors contributed to low adoption, including data and knowledge limitations, a propensity towards hard engineering, low collaborative engagement, and limited implementation capacity. Some areas identified for intervention were comprehensive data collection, reducing uncertainty surrounding the implementation of Nature-based Solutions, effective collaboration, using case studies and best practices, and knowledge translation and dissemination. The outputs demonstrated a critical role for the engineer in mainstreaming Nature-based Solutions through adopting a systematic and holistic design approach. Concomitant to this is the need for engineers to operate successfully within transdisciplinary teams to enhance transformative communication skills and ensure that solutions not only minimize environmental impact but also support the conservation of biodiversity and ecosystem services.

Development of novel computational models based on artificial intelligence technique to predict liquids mixtures separation via vacuum membrane distillation

The fundamental objective of this paper is to use Machine Learning (ML) methods for building models on temperature (T) prediction using input features r and z for a membrane separation process. A hybrid model was developed based on computational fluid dynamics (CFD) to simulate the separation process and integrate the results into machine learning models. The CFD simulations were performed to estimate temperature distribution in a vacuum membrane distillation (VMD) process for separation of liquid mixtures. The evaluated ML models include Support Vector Machine (SVM), Elastic Net Regression (ENR), Extremely Randomized Trees (ERT), and Bayesian Ridge Regression (BRR). Performance was improved using Differential Evolution (DE) for hyper-parameter tuning, and model validation was performed using Monte Carlo Cross-Validation. The results clearly indicated the models’ effectiveness in temperature prediction, with SVM outperforming other models in terms of accuracy. The SVM model had a mean R2 value of 0.9969 and a standard deviation of 0.0001, indicating a strong and consistent fit to the membrane data. Furthermore, it exhibited the lowest mean squared error, mean absolute error, and mean absolute percentage error, signifying superior predictive accuracy and reliability. These outcomes highlight the importance of selecting a suitable model and optimizing hyperparameters to guarantee accurate predictions in ML tasks. It demonstrates that using SVM, optimized with DE improves accuracy and consistency for this specific predictive task in membrane separation context.

Assessing and mitigating building fire risk based on the scenario clusters in Bangladesh

The increasing occurrence of fire hazards in residential buildings in Bangladesh has led to severe property damage, physical injuries, and fatalities, underscoring a critical research gap in effective fire risk management. This study aims to address this gap by identifying key fire risks and proposing mitigation strategies using BIM technology. The research employs a comprehensive survey and scenario clustering methodology to identify these risks and assess the efficacy of various fire-resistant materials. Data collection involved questionnaires distributed to 200 respondents, including building owners, occupants, engineers, and firefighters. These clusters are based on the metrics of fatality numbers and property damage to quantify fire risk. Findings highlight the significant role of wall and ceiling lining materials (RII = 0.919) in contributing to fire risk. Further analysis was conducted using a residential building model developed in Autodesk Revit and simulated in Pyrosim software, assessing various lining materials. Quantitative results demonstrated that fire brick is the most effective material, exhibiting the lowest heat release rate (968 kW/m2), minimum adiabatic surface temperature (28°C), minimum heat transfer coefficient (1.75 W/m2/K), and lowest surface burning rate (7.5e−08 Kg/m2/s). These results suggest that fire brick is the optimal choice for enhancing fire resistance in residential buildings.

Optimizing Energy Efficiency with a Cloud-Based Model Predictive Control: A Case Study of a Multi-Family Building

The Energy Performance of Buildings Directive (EPBD) has set a target to achieve carbon-neutral building stock and generate 80% of its electricity from renewable sources by 2050. While Model Predictive Control (MPC) can contribute significantly to energy flexibility in buildings, its remote implementation remains relatively unexplored, especially in the residential sector. The purpose of this research is to demonstrate the reliability, robustness, and computational efficiency of a cloud-based application of an MPC called Smart Energy Management (SEM) on a multi-family residential building. The SEM was tested on a virtual building model in TRNSYS using an open-source distributed event streaming platform for data exchange and synchronization. Simplified models for thermal behavior prediction, including an R3C3 model of the building, were developed in C++. The SEM was evaluated in eight scenarios with varying weather conditions, optimization criteria, and runtime periods. The results demonstrate that the SEM maintains stability and robustness over a 2-week period with a 15-minute planning resolution while ensuring thermal comfort. The C++ implementation of the optimization algorithm enables SEM deployment on low-spec servers, supporting cost-effective applications in real buildings with minimal intervention.

Experimental Study on Seismic Performance Evaluation of a Multi-Story Steel Building Model with Rolling-Type Seismic Base Isolation

Critical structures such as hospitals, high-precision manufacturing facilities, telecommunications centers, and fire stations, especially, need to maintain their functionality even during severe earthquakes. In this sense, seismic isolation technology serves as a vital design method for preserving their functionality. Seismic isolators, also known as earthquake isolation systems, are used to reduce the effects of earthquakes on buildings by isolating them from the ground they are located on. By ensuring that less acceleration and force demand is transmitted to the superstructure, both the building and the equipment and the devices in the building are prevented from being damaged by earthquakes. This experimental study aims to conduct vibration tests on a small-scale multi-story steel-building model equipped with a specially designed rolling-type seismic base isolation system. The relationship between the test model and the prototype was achieved by frequency simulation. The tests will be performed on a shake table under six different earthquake accelerations to examine the model’s dynamic behavior. The primary goal is to evaluate the isolation performance of the rolling-type seismic base isolator under seismic loads, with a focus on recording the vibrations at the top of the test building. It has been observed that the isolator placed at the base of the building significantly reduced the peak acceleration and displacement values of the floor motion. Under the most severe earthquake record applied to the shake table, the acceleration at the top of the building with the isolator was found to be reduced by approximately 50%, compared to the non-isolated case.

Controlling Cell Interactions with DNA Directed Assembly.

The creation of complex cellular environments is critical to mimicking tissue environments that will play a critical role in next-generation tissue engineering, stem cell programming, and therapeutic screening. To address this growing need, techniques capable of manipulating cell-cell and cell-material interactions are required that span single-cell to 3D tissue architectures. DNA programmed assembly and placement of cells present a powerful technique for the bottom-up synthesis of living microtissues for probing key questions in cell-cell and cell-material-driven behaviors through its refined control over placement and architecture. This review examines the current state of the art in the programming of cellular interactions with DNA and its applications spanning tissue model building, fundamental cellular biology, and cell manipulation for measurements across a host of applications.

Prediction of Performance and Emissions Diesel Engines Fueled-Biodiesel Using Artificial Neural Network (ANN) Resilient Backpropagation Algorithm (Rprop)

In order to increase energy security and improve environmental quality, the Indonesian Goverment set a target of 23% renewable energy mix in 2025, one of which is the Mandatory Bioediesel Program. A higher biodiesel blending ratio will affect the performance and emissions of diesel engines because biodiesel is chemically different from diesel oil. Research related to the prediction of diesel engine performance and emissions using Artificial Neural Network (ANN) has been conducted, but the author sees a research opportunity for the implementation of the ANN Resilient Backpropagation (Rprop) algorithm. The data used to create the ANN model prediction was secondary data from previous research. The model designed multi input and multi output (MIMO) with 4 input variables and 7 output variables. Model building done by varying the number of neurons and hidden layers. Model evaluation selected based on the largest coefficient of determination parameter R2 and the smallest RMSE or MAPE. The results showed that the ANN single layer 4-20-7 network architecture is the best model for predicting diesel engine performance and emissions with test data R2 , RMSE and MAPE of 0.962532, 6.699428 and 6.0% respectively, while for overall data testing has a performance of 0.982869, 3.908542 and 4.3%. The results also show that based on the ANN prediction results, the increasing biodiesel ratio can increase NOx emissions and decrease HC, CO and CO emissions2 . In terms of performance, the addition of biodiesel can increase BSFC and BP and decrease BTE. The results also show that the addition of ZnO concentration can reduce emissions while in terms of performance it will increase BTE and reduce BSFC and BP.

Improvements and Heterogeneities of the Global Centroid Moment Tensor Catalog

Abstract Earthquake catalogs are heterogeneous, especially those developed over long time spans. Changes in seismological monitoring, which provides the records on which these catalogs are based, are common. Typically, instruments and networks become more sensitive over time, allowing for the detection and characterization of smaller earthquakes. In pursuit of improvement, new methods for routine data analysis are occasionally introduced, modifying the procedures for catalog compilation. The resulting heterogeneities may not be evident to users, but they should be unveiled and considered in any application of the catalog, especially in statistical seismology, which analyzes large earthquake data sets. The Global Centroid Moment Tensor catalog is considered the most homogeneous database of global seismicity. However, a detailed analysis of its heterogeneities has been lacking. This work reviews changes in the catalog’s development from 1976 to 2023 and reveals how these have caused improvements and heterogeneities in the resulting data. Several periods are distinguished, separated by milestones in the methods employed for moment tensor inversion and catalog compilation, as well as by the advent of global broadband monitoring in 2004. These changes are shown to have caused variations in the catalog’s completeness and in the determinations of centroid depths, scalar seismic moments, and moment tensors. The magnitude of completeness is measured here in detail, both temporally and spatially. It has decreased over the years and shows spatial variations within each period, correlated to regional differences in network monitoring and compilation biases. Moment tensor determinations have been significantly different since 2004, resulting in a different frequency distribution of rake angles and a different dependence of the double-couple component as a function of rake. This work is expected to benefit all future uses of the catalog, enabling better characterization of seismicity properties and improved building and testing of models for earthquake occurrence.

Adapting Group Model Building for Mental Healthcare: A Participatory Co-Design Approach.

Countries around the world are experiencing both a growing need for mental health services and major gaps in the delivery of mental healthcare. The World Health Organisation's 2022 report on mental health emphasised co-designed and systems-level innovations responsive to the voice of those with lived experience. However, evidence for implementing such methodologies is sparse. Group model building (GMB), a participatory systems-based approach, has been used in various fields, but its application is limited within mental healthcare settings. It is recognised as a valuable tool for engaging stakeholders, including those with lived experience, in understanding and redesigning health systems and services. The participatory approach aligns with the mental health sector's shift towards co-design and person-centred care. This study assessed the acceptability of GMB as a method for co-design within mental healthcare, aiming to explore its potential for meaningful collaborative improvement and systems-level insights. Participants from a regional Mental Health and Drug and Alcohol Service (MHDAS) engaged in GMB workshops and focus groups. Eighteen individuals, including lived experience workers and healthcare professionals, provided feedback that informed adjustments to the workshops' design, language, timing and facilitation techniques, enhancing GMB's relevance for mental health co-design. The findings demonstrate the value and acceptability of the adapted GMB process in acute mental healthcare, marking a significant step towards evidence-based, person-centred service improvement. This study highlights the potential effectiveness of participatory methods in identifying and addressing systemic challenges, paving the way for future mental health reforms. Future work will focus on implementing and evaluating co-designed interventions, aiming for more inclusive and effective mental healthcare and advancing innovation and reform in the field.

Environmental impacts and BIM: a Portuguese heritage case study

PurposeThis paper aims to compare the possible environmental impacts of different rehabilitation solutions proposed to a case study of a Portuguese vernacular building, bearing in mind the need to search for less impactful practices in accordance with global agendas and, simultaneously, the importance of preserving heritage assets.Design/methodology/approachThe two proposed solutions differ from each other in the selection of materials, varying from the exclusive use of conventional contemporary materials to the use of locally sourced and natural-based materials, considering the Portuguese and local scenario. Results obtained from Life Cycle Assessment procedures are the basis of comparison, which were developed using a Building Information Model (BIM)-integrated analytical plugin.FindingsProducing Heritage Building Information Model (HBIM) models was essential to the analysis, and therefore is a vital part of the used methodology; nevertheless, the modelling of heritage buildings itself plays an important part in preserving heritage assets once it provides precise documenting and managing tools. The results depict variation in values for different indicators in both cases, whose interpretation might need further development to get to deeper conclusions. It has been found that the software tools are still not well adapted to analyse heritage or historic buildings, and therefore adaptations had to take place to overcome these limitations.Originality/valueThe study distinguishes itself by involving the production of HBIM models and the use of BIM-integrated Life Cycle Assessment plugins as well as its analyses, to validate this approach.

Information Technologies for Calculating Anthropogenic Heat Flux in Urban Areas

Modern megalopolis face challenges in effectively managing energy resources and minimizing the impact of human activities on the environment. One of the significant aspects of anthropogenic impact is the heat flow generated by buildings, transport and industry. This article presents a set of programs for assessing anthropogenic heat flow (AHF) caused by heat loss from buildings during the heating season. The study is based on a spatial geometric model of the city constructed using OpenStreetMap data. The thermophysical properties of enclosing structures and specific characteristics of thermal energy consumption are obtained from building codes. The proposed method includes the stages of buildings modeling, filtering, supplementing information from Yandex Maps and State Information System of Housing and Communal Services, eliminating collisions, assigning specific characteristics and calculating AHF. The method is implemented through scripts for the Rhinoceros platform, known for its wide functionality and visual programming environment Grasshopper. The proposed approach allows us to effectively analyze and visualize anthropogenic heat flow in cities, which is a key step in developing strategies for sustainable energy management and reducing negative environmental impacts.

Modeling and scenario building for climate change adaptation planning: The case of large mining in Chile

The mining sector in Chile is a strategic industrial sector for the country. Existing evidence shows that it faces several serious climate change threats; precipitation and flooding, droughts, heatwaves, among others. This scene put pressure on the government and the industry to develop efficient adaptation plans. To date, adaptation plans are designed under the rather linear paradigm of predict-then-act or the impact-lea approach as they are characterized in the IPCC Assessment Report 5 (AR5). Literature and the IPCC reports have identified the limits of those planning paradigms showing the relevance of adaptation barriers or enabling conditions, which should be considered as intrinsic part of the planning problem.In methodological terms, planning for climate change risks implies an enriched adaptation plan problem, previously characterized only by an operative climate risk management, that must be described at the beginning of the decision-making process. In this study, our objective is to contribute to the climate change adaptation planning of large-scale mining in Chile. The study is based on a climate change adaptation planning approach that overcomes the limitations of the current paradigm. In doing so, we start from the understanding that what emerges as an object of analysis from adding to the climate risks their enabling managing conditions is a social system. The social system whose function is that social climate risk management takes place. Therefore, we call it the Social Management System for Adaptation to Climate Change (SMSACC). As such the SMSACC should be the adaptation planning key object.In the first place we modeled that system applying a qualitative system methodology and then we developed it into a mathematical model based on graph theory, in particular the signed digraphs. This allows us to simulate two types of intervention on the enlarged object of analysis of the large mining adaptation plan. On the one hand, we carried out a future scenario analysis based on prospective tools which enables us to understand the system's answer to different future behavior of its environment, including climate change. On the other hand, we simulate different strategic interventions options on the system, which facilitates understanding the system's reaction under different public policy approaches.The modeling and simulation results provided an insightful understanding of the dilemma of social adaptation management of large mining in Chile, and as such they are useful input for the planning process.

Level of social security expenditures and economic growth rate based on econometric regression modelling: New evidence from OECD countries

This research article examines the relationship between the level of social welfare expenditure and economic growth rates, based on unbalanced panel data from 38 OECD countries covering the period from 1985 to 2022. Four hypotheses are formulated regarding the impact of social expenditure on economic growth rates. Through multiple iterations of regression model building, employing various combinations of dependent and independent variables, and conducting tests for stationarity and causality, compelling empirical evidence was obtained on the negative influence of social welfare spending on economic growth rates. The study takes into account both government and non-governmental expenditures on social welfare, a novelty in this field. This approach allows for a detailed examination of the effects of different components on economic growth and provides a more comprehensive understanding of the relationships. The findings indicate that countries with high levels of social welfare spending experience a slowdown in economic growth rates. This is associated with increasing demands on social security systems, their growing inclusivity, and the escalating required levels of financing, which are increasingly covered by debt sources. The research highlights the need to strike a balance between social expenditures and economic growth rates and proposes a set of measures to ensure economic growth outpaces the indexing of social expenditures. The abstract underscores the relevance of the study in light of the widespread recognition of the necessity to combat inequality, poverty, and destitution, and calls on OECD countries’ governments to pay increased attention to social policy in order to achieve sustainable and balanced economic growth.