Green Infrastructure Research Articles

Assessing and predicting green gentrification susceptibility using an integrated machine learning approach

ABSTRACT Greenery initiatives, such as green infrastructures (GIs), create sustainable and climate-resilient environments. However, they can also have unintended consequences, such as displacement and gentrification in low-income areas. This paper proposes an integrated machine learning (ML) approach that combines both unsupervised and supervised ML algorithms. First, 35 indicators that contribute to green gentrification were identified and categorised into 7 categories: social, economic, demographic, housing, household, amenities, and GIs. Second, data was collected for all census tracts in New York City. Third, the green gentrification susceptibility was modelled into 6 levels using k-means clustering analysis, which is an unsupervised ML model. Fourth, the Technique for Order of Preference by Similarity to the Ideal Solution (TOPSIS) was used to map the census tracts to their green gentrification susceptibility level. Finally, different supervised ML algorithms were trained and tested to predict the green gentrification susceptibility. The results showed that the artificial neural network (ANN) model is the most accurate in classifying and predicting the green gentrification susceptibility with an overall accuracy of 96%. Moreover, the outcomes showed that the Normal Difference Vegetation Index (NDVI), the proximity to GIs, the GIs frequency, and the total area of GIs were identified as the most important indicators to predict green gentrification susceptibility. Ultimately, the proposed approach allows practitioners and researchers to perform micro-level (i.e. on the census-tracts level) predictions and inferences about green gentrification susceptibility. This allows more focused and targeted mitigation actions to be designed and implemented in the most affected communities, thus promoting environmental justice.

Sustainability in Action: Green Infrastructure as a Marker of Sustainable Urban Development

The current form and pace of urbanization is an ongoing threat to sustainable urban development. As these problems span social, environmental, and economic realms, it is imperative that any proposed solution is able to address these problems holistically. Although a number of silver-bullet solutions such as electric vehicle adoption have been introduced, they do not respond to the problems with an integrated approach. Tackling this problem will require a solution that can be changed and applied on a case-by-case basis. Therefore, the development of green infrastructure is the most efficient solution when responding to the problems threatening urban sustainability. The purpose of this paper is to argue for the efficacy of green infrastructure methods by analyzing the evidence provided about factors like temperature and pollution levels in green cities as compared to other proposed solutions, and to explore critiques of green infrastructure and discuss their potential improvements. The research provided contains a mixture of case studies, participant surveys, and other scientific findings and has the potential to make green infrastructure a priority in the future planning of cities.

Geopatial Technologies in Civil Engineering : A Critical Literature

This study investigates the role of geospatial technologies in enhancing the efficiency and effectiveness of civil engineering projects. Geospatial technologies, including Geographic Information Systems (GIS), remote sensing, and spatial analysis, have revolutionized the field of civil engineering by providing critical tools for planning, design, construction, and management of infrastructure projects. The integration of these technologies enables accurate terrain mapping, real-time monitoring, and data-driven decision-making, which are crucial for ensuring the safety, sustainability, and resilience of modern infrastructure. This research aims to explore the applications of geospatial technologies in various civil engineering projects, including highway planning, urban development, and environmental monitoring. The study also examines the potential benefits and limitations of these technologies and provides recommendations for their effective integration into civil engineering practices. By understanding the impact of geospatial technologies on civil engineering projects, this research aims to contribute to the development of more efficient, sustainable, and resilient infrastructure that supports the needs of modern society.

A pathway to urban resilience: investigating green open space priority areas to mitigate the urban heat island phenomenon in Samarinda City, Indonesia

Purpose The potential for massive economic growth exists in Samarinda City due to the intensification of activities in built-up areas. This suggests the potential for increased urban disease in the relocation of Indonesia’s new capital city to a location adjacent to Samarinda. One of the most striking impacts is the urban heat island (UHI). The increase in this phenomenon can be addressed effectively and efficiently through the provision and arrangement of appropriate vegetation-based actions. Therefore, this study aims to identify priority areas of green open space (GOS) based on UHI levels. In addition, this study also aims to present alternative mitigation measures to reduce the risk of disasters due to UHI. Design/methodology/approach A mixed-method approach was used in this research, involving an initial land surface temperature analysis to identify the UHI class. This analysis was complemented by quantitative spatial analyses, such as scoring, overlay and intersect methods, to determine the priority level class and the typology of GOS priority. A qualitative analysis was also conducted through data triangulation or comparison methods, such as examining existing land use, GOS priority maps and spatial plan policies. Findings The findings show that the total UHI area in Samarinda City was 6,936.4 ha in 2019 and is divided into three classifications. In Class 1, the UHI area is very dominant, reaching 87% of the total area. Meanwhile, the main results identified two priority classes of GOS in Samarinda, namely, the medium and high categories with an area of 960.43 ha and 113.57 ha, respectively. The results also showed that there were 17 typologies associated with five alternative mitigation measures: green industry, greening parking lots, improving urban green infrastructure and buildings, urban greening and mining restoration. Research limitations/implications Specific to assessing UHI, image data were available only in medium spatial resolution, leading to a consequence of detailed accuracy. In addition, since the determination of mitigation considered local policies, the method should be used in other locations requiring adjustments to existing regulations, specifically those related to spatial planning. Originality/value This study makes a significant contribution to the understanding of the UHI phenomenon in Indonesia, especially in the urban areas of Kalimantan Island. In addition, the study presents new insights into alternative mitigation actions to reduce the risk of UHI. Innovatively, this study introduces a typology of regions associated with appropriate alternative mitigation actions, making it an important achievement for the first time in the context of this study.

Removal of Residual Chlorine from Stormwater Using Low-Cost Adsorbents and Phytoremediation

In recent decades, the pollution of water with micropollutants has become an increasing environmental concern. Since 2019, increased stormwater pollution from chlorine-based disinfectants has been recorded due to the COVID-19 pandemic. Runoff from disinfected areas and the residual chlorine present in stormwater are transported to surface water bodies, posing a risk to aquatic flora and fauna. The objectives of this study were (1) to evaluate the efficiency of different low-cost and recyclable filter materials in removing residual chlorine, and (2) to test plants’ ability to reduce residual chlorine concentrations through phytoremediation. Experiments were conducted in the laboratory (column and batch) and in the field (raised garden bed) to assess the efficiency of various filter materials (peat, wood chips, sawdust and the lightweight aggregates) in retaining residual chlorine to be implemented in green infrastructure. The best retainers of chlorine were sawdust (96%) and the LWA Leca (76%). No harmful effects of residual chlorine (changes in growth, color, leaf size, etc.) on plants (Tagetes patula or Pisum savitum) were observed and the residual chlorine in the leachate samples was below the equipment’s detection limit. Our research results will contribute to future studies aiming to remove various micropollutants from stormwater using remediation technologies.

Am I Safe at My Educational Place? Creating Secure and Sustainable Urban Learning Spaces Through Green Infrastructure and Ecological Education

Green infrastructure has become a critical part of society for environmental sustainability. Students studying in public urban spaces seem less satisfied with their living standards and environmental conditions. This research aims to determine the impact of perceived danger in urban public spaces, green infrastructure, and ecological education on student satisfaction. Additionally, this study considers the moderating effects of ecological education and green infrastructure. Data were collected from 350 students at public schools and colleges. The Partial Least Squares Structural Equation Modeling (PLS-SEM) method was used for data analysis. The study found a significant direct relationship between perceived danger in urban public spaces, green infrastructure, ecological education, and student satisfaction. Moreover, the moderating effects of green infrastructure and ecological education are significant in increasing student satisfaction. This research is based on a novel idea and contributes a newly developed model to the body of knowledge on student satisfaction and ecological education. The research has both theoretical and practical implications for improving student satisfaction through ecological education and green infrastructure. The limitations of this research are described, along with future directions to guide researchers in their future studies.

Scenario-Based Green Infrastructure Installations for Building Urban Stormwater Resilience—A Case Study of Fengxi New City, China

Global climate change has precipitated a surge in urban flooding challenges, prompting the imperative role of green infrastructure (GI) as the linchpin of sponge city construction to enhance urban sustainability and resilience. But the evaluation of urban stormwater resilience faces challenges due to the lack of a comprehensive evaluation framework taking the intrinsic features of the resilience system into account and the insufficient coverage of alternative scenarios’ performance under multiple rainfall return periods. This study, focusing on Fengxi New City, China, evaluates the suitability of GI (i.e., green roofs, rain gardens, and permeable pavements) and constructs a stormwater management model (SWMM) for urban stormwater hydrological simulation. This study also establishes a comprehensive urban stormwater resilience evaluation system and uses quantitative methods to unify the performances of scenarios under different rainfall return periods. Our analytical findings elucidate that the suitability of GI is predominantly concentrated in the northern and western areas of the study area, with the smallest suitable area observed for permeable pavements. Divergent GIs exhibit disparate performances, with rain gardens emerging as particularly efficacious. Importantly, the combination of multiple GIs yields a synergistic enhancement in resilience, underscoring the strategic advantage of adopting a diverse and integrated approach to GI implementation. This study facilitates a deeper understanding of urban stormwater resilience and assists in informed planning decisions for GI and sponge cities.

Optimal integration and planning of PV and wind renewable energy sources into distribution networks using the hybrid model of analytical techniques and metaheuristic algorithms: A deep learning-based approach

The optimal integration of Renewable Energy Sources (RESs) into distribution networks is crucial for sustainable energy generation. However, existing approaches utilized for optimal RES integration, including metaheuristic and analytical methods, exhibit limitations. Analytical methods are simple to implement, computationally efficient, and able to model power system mechanisms, yet they face scalability issues, making them inefficient for large-scale distribution systems. On the other hand, metaheuristic approaches, while flexible for complex problems, suffer from premature convergence problems, limiting their application in real distribution networks. To address these issues, this paper introduces a novel hybrid model that combines the strengths of analytical and metaheuristic techniques. The proposed model employs a deep learning-based method called bi-directional long short-term memory (B-LSTM) network is utilized to handle the uncertainties associated with the Photovoltaic (PV) and Wind Turbine (WT) generation and load demand. Subsequently, a distinctive analytical multi-objective index-based procedure is developed to minimize the real and reactive energy loss and voltage deviation by analyzing the branch current change caused by the RES penetration in distribution networks. The optimization problem is then solved using the binary particle swarm optimization (BPSO) technique. Importantly, the presented method considers the long-term nature of optimal RES integration and reflects the seasonal impacts of the load and RES output powers. The proposed approach is applied to the IEEE 33-bus test system. The simulation results show that the introduced hybrid model, by addressing the limitations of existing approaches, offers a promising solution for efficient and effective RES integration in distribution systems while requiring low computational effort, thereby advancing the transition towards sustainable energy infrastructure.

Bridging Geo-Data and Natural Gas Pipeline Design Standards: A Systematic Review of BIM-GIS Integration for Natural Gas Pipeline Asset Management

In today’s world, effective management and the use of spatial data are of great importance in many sectors. Industries such as land management, asset management, and infrastructure management are areas where spatial data are heavily utilized. Advanced technologies such as Geographic Information Systems (GISs) and Building Information Modeling (BIM) are used in the processes of collecting, analyzing, and managing geographically enabled data (geo-data). These technologies enable the effective processing of large datasets, improve decision-making processes based on geographic information, and facilitate more efficient collaboration across sectors. This study conducts an in-depth examination of the existing literature on asset management, infrastructure management, and BIM-GIS integration using bibliometric analysis and systematic literature review methods. Bibliometric analysis is employed to determine statistical values such as current research trends, frequently cited authors, most used keywords, and country performances in the relevant field. This study’s results highlight future research trends and significant gaps in the areas of asset management, infrastructure management, natural gas pipelines, and BIM-GIS integration. In particular, this study demonstrates the critical importance of asset management and BIM-GIS integration for sustainable infrastructure design, construction, and management. In this context, attention is drawn to the importance of data standardization, digitization, systematic integration, and contemporary land management requirements.

Enhancing Robustness in Precast Modular Frame Optimization: Integrating NSGA-II, NSGA-III, and RVEA for Sustainable Infrastructure

The advancement toward sustainable infrastructure presents complex multi-objective optimization (MOO) challenges. This paper expands the current understanding of design frameworks that balance cost, environmental impacts, social factors, and structural integrity. Integrating MOO with multi-criteria decision-making (MCDM), the study targets enhancements in life cycle sustainability for complex engineering projects using precast modular road frames. Three advanced evolutionary algorithms—NSGA-II, NSGA-III, and RVEA—are optimized and deployed to address sustainability objectives under performance constraints. The efficacy of these algorithms is gauged through a comparative analysis, and a robust MCDM approach is applied to nine non-dominated solutions, employing SAW, FUCA, TOPSIS, PROMETHEE, and VIKOR decision-making techniques. An entropy theory-based method ensures systematic, unbiased criteria weighting, augmenting the framework’s capacity to pinpoint designs balancing life cycle sustainability. The results reveal that NSGA-III is the algorithm converging towards the most cost-effective solutions, surpassing NSGA-II and RVEA by 21.11% and 10.07%, respectively, while maintaining balanced environmental and social impacts. The RVEA achieves up to 15.94% greater environmental efficiency than its counterparts. The analysis of non-dominated solutions identifies the A4 design, utilizing 35 MPa concrete and B500S steel, as the most sustainable alternative across 80% of decision-making algorithms. The ranking correlation coefficients above 0.94 demonstrate consistency among decision-making techniques, underscoring the robustness of the integrated MOO and MCDM framework. The results in this paper expand the understanding of the applicability of novel techniques for enhancing engineering practices and advocate for a comprehensive strategy that employs advanced MOO algorithms and MCDM to enhance sustainable infrastructure development.

Sustainable edge computing: Challenges and future directions

AbstractThe advent of edge computing holds immense promise for advancing the digitization of society, ushering in critical applications that elevate the overall quality of life. Yet, the practical implementation of the edge paradigm proves more challenging than anticipated, encountering disruptions primarily due to the constraints of applying conventional cloud‐based strategies at the network's periphery. Increasingly influenced by sustainability commitments, industry regulations currently view edge computing as a potential threat, primarily due to the energy inefficiency of solutions situated in close proximity to data generation sources and the rising density of computing. This paper presents a proactive strategy to transform the perceived threat into an opportunity, steering the sustainable evolution of future edge infrastructures to make them both environmentally and economically competitive for accelerated adoption. The vision outlined addresses key challenges associated with edge deployment and operation, emphasizing energy efficiency, fault‐tolerant automation, and collaborative orchestration. The proposed approach integrates two‐phase immersion cooling, formal modeling, machine learning, and federated management to effectively harness heterogeneity, propelling the sustainability of edge computing. To substantiate the efficacy of this approach, the paper details initial efforts towards establishing the sustainability of an edge infrastructure designed for an Advanced Driver Assistance Systems application.

Training the Next Generation of Local Public Health Leaders: A Case Study of Community Health Organizers in Pennsylvania.

Few short-term training programs exist for persons with limited experience or training in public health to support public health initiatives. We describe a public health training designed by the Pennsylvania (PA) Training Center for Health Equity for the PA Community Health Organizer (CHO) program. The CHO program was created to address the immediate needs of underserved communities and promote lasting health equity during the pandemic. CHOs are professionals who promote community action and align efforts with local organizations to build sustainable public health infrastructure and apply evidence-based practices to program policy, planning, and development. The training content, delivered by Project Extension for Community Healthcare Outcomes (ECHO) in 12 monthly sessions, focused upon foundational public health concepts in a novel community case study approach. The ECHO All Teach, All Learn training model was successful in providing relevant public health information to this new workforce, and the pre-/post-training evaluation demonstrated a positive increase in knowledge across all domains.

Delimitation and Prioritization of Climate-Affected Areas Using the Example of a Post-Industrial City

This study adapted and tested an approach to identifying areas that are particularly vulnerable to climate-related hazards using the example of the post-industrial city of Knurów in Poland. This study applied a multi-criteria method using the analytical hierarchy process based on GIS map data. The analysis was divided into statistical regions defined for the city. Fifteen attributes were defined for each statistical region. The applied methods provided verified spatial information related to specific climate change hazards. The results showed that the most vulnerable areas were the areas with intensive development in the city center and in the southwestern part of the city. Among the 15 attributes, the most significant were T1 (number of inhabitants in zones with a higher potential thermal risk index by statistical district) with a value of 0.163, G2 (percentage of the sum of tree-shaded areas in built-up areas within the territory of a given statistical district) with 0.143 and H3 (number of buildings in areas of drainless basins and 100-year water). This method effectively identified the most vulnerable areas. The use of such a method can help in the preparation of planning documents and urban adaptation plans by determining the thermally and hydrologically vulnerable areas with the least developed green infrastructure as an exposure-mitigating factor.

Quantifying the impacts of green roof design factors on microclimate regulation: A case study in Washington, D.C.

This study explores and quantifies the impacts of green roofs (GR) design factors on microclimate regulation. Against rapid urbanization and intensifying climate changes, green infrastructure has received increasing attention due to its contribution to dealing with the urban heat islands effect (UHIE) and increasing urban sustainability. GR is one example of green infrastructure and is widely used in cities. However, its microclimate regulation benefits have not been thoroughly explored and require more exploration. There are insufficient studies on quantifying GR design factors impacts on microclimate regulation. Meanwhile, little research has explored the impact of regulating microclimate when accessibility is considered as a design factor. Using a GR in Washington, D.C., as a case study, this study models 14 roof scenarios with several common GR design factors, generates their simulated microclimate patterns, and quantifies each design factors impact based on statistical analyses. Results present quantitative data on how studied design factors change microclimate patterns and find that accessibility does affect some microclimate aspects but only to a limited rate. This study provides suggestions and insights to optimize GR design guidelines. Overall, this study contributes to the refinement of GR research, promotes better GR design, and responds to urbanization and climate issues.

Underpass Water Logging System Using IOT and ML

In recent years, the surge in population growth has exacerbated numerous challenges for both humans and the environment, including issues like inadequate infrastructure and frequent occurrences of waterlogging in underpasses. During heavy rainfall, underpasses often become inundated, posing significant risks to passing vehicles and exacerbating traffic congestion. This is primarily due to ineffective drainage systems and flawed designs for diverting rainwater away from the underpasses. To address these pressing issues, this paper proposes an IoT-based solution aimed at monitoring and managing heavy traffic within underpasses. The primary objective is to establish a comprehensive framework capable of continuously monitoring the water levels within underpasses. By leveraging IoT technology, this framework can activate warning signs and implement traffic control measures in real-time to mitigate the risks posed by waterlogging and alleviate traffic congestion. This innovative approach not only offers a proactive solution to the immediate challenges faced by underpasses but also lays the foundation for a more sustainable and efficient urban infrastructure management system. Through the integration of IoT sensors and intelligent control mechanisms, this framework promises to enhance the resilience of underpasses to adverse weather conditions while optimizing traffic flow, ultimately contributing to safer and more livable urban environments . Keywords — Internet of Things, sensors, Wi-Fi module, LCD, cloud architecture.

Multi-Scenario analysis of rooftop greening regulation on runoff effects based on adaptive Evaluation: A case study of Macau, China

The reduction in internal ecological spaces within urban environments and the expansion of impervious surfaces have been identified as factors significantly increasing the susceptibility of cities to flood disasters. Rooftop greening, an essential component of urban stormwater management strategies, offers a versatile solution for augmenting permeable surfaces and delivering ecological services while seamlessly integrating with existing urban infrastructure. This approach has been proven effective in enhancing urban resilience against rainfall-induced flooding events. Hence, the precise identification of suitable building rooftops for green infrastructure within urbanized regions, combined with the simulation of runoff regulation effects brought about by these rooftop greening interventions, holds substantial practical significance. This study innovatively constructs a technical method for measuring the runoff control performance of urban-scale green roofs from identification, evaluation to simulation. Taking the Macao Special Administrative Region as an example, a green roof suitability evaluation index system that is consistent with urban characteristics has been constructed, integrating machine learning, comprehensive evaluation, SWMM simulation methods to evaluate the suitability of roof greening in urban areas and simulate the impact of multi-senarios green roof construction types on runoff regulation. Finally, a comprehensive strategy for rooftop greening construction in Macau is proposed, with the aim of amplifying the runoff regulation capabilities of such initiatives. The main findings of the research include the following: (1) The research area exhibits a notably high aggregate potential for rooftop greening, with suitable rooftop areas comprising approximately 34% of the total rooftop expanse. Notably, construction vintage and roof elevation are identified as the most substantive constraints on rooftop greening. (2) Discernible differences in rooftop greening construction scale exert a significant influence on runoff regulation capacity, with the magnitude of greening positively correlated with its runoff regulation efficacy. Distinct greening typologies elicit varying impacts on runoff regulation, with semi-intensive rooftop greening surpassing extensive approaches in runoff regulation performance. Disparate distribution patterns of rooftop greening engender dissimilar runoff regulation effects, with rooftop greening layouts situated distant from drainage outlets facilitating runoff mitigation, while those in close proximity to drainage outlets enhance peak flow reduction effects.

Sustainability through the use of Building Information Modelling in infrastructure planning

This study examines the role of Building Information Modelling (BIM) in infrastructure planning from a sustainability perspective. It emphasises how BIM can contribute to the promotion of ecological, economic and social sustainability. Interviews with experts have shown that BIM can not only optimise the energy consumption of infrastructure projects, but also lead to precise material quantification and optimisation. Despite the benefits, the use of BIM in infrastructure planning is limited, especially in small and medium-sized projects and with local authorities. The study identifies challenges such as the complexity of BIM software systems and the additional costs of implementing them. Recommendations for action include training clients and engineering firms in the use of BIM for cost forecasting and extending the digital twin concept beyond the construction and planning process. The results show that widespread BIM application is only possible if both clients and planning offices share sustainability goals and are prepared to bear the additional costs for BIM planning. The work emphasises the need to use BIM beyond building construction for sustainable infrastructure development and provides insights into the practice of engineering firms in the infrastructure sector.

Assessment of the Potential Contribution of the Urban Green System to the Carbon Balance of Cities

Reducing greenhouse gas emissions is a crucial challenge in urban areas characterized by high energy consumption and reduced exposure to nature. In this context, the urban green system could play a pivotal role. In the literature, scholars have analyzed both the ability of species-specific and layout-specific green infrastructure to increase carbon sequestration and the best location sites for new green infrastructure to increase the provision of overall ecosystem services. There is a lack of studies helping green urban planners and designers choose where and which green infrastructure to implement based on vegetation species-specific performance and the local carbon emissions of city components. This paper uses tree inventory data from a medium-sized city in central Italy (Perugia) to develop a spatial analysis of urban park performance in carbon sequestration. Then, the method evaluates the carbon emission of a public city building to generate a spatialized balance between building demand and tree supply to support local decisions about the best locations for new green infrastructure and the choice between species. The paper contributes to GIS-based tools that vary the recommended location sites and species for new green infrastructure based on the demanded ecosystem service.

Sustainable Tourism Infrastructure Development

This study looks at the development of sustainable tourist infrastructure, focusing only on physical factors. In terms of resource efficiency and environmental sustainability, planning, design, and management techniques are assessed through a review of the literature and case studies. In order to pinpoint problems and offer viable solutions, the research carefully examines how much energy, water, garbage, and transportation systems are used. The report promotes minimising environmental effect and optimising resource conservation in tourism infrastructure projects by highlighting eco-friendly technology and design ideas. Innovative strategies, such as the use of sustainable construction materials, integrated renewable energy, water-saving techniques, and effective transportation systems, are demonstrated in successful case studies. The research prioritises physical factors to ensure lowest ecological impact and long-term environmental resilience, advocating for sustainable tourist infrastructure by integrating theoretical and practical insights. The findings contribute to advancing sustainable development goals within the tourism sector, offering actionable recommendations for policymakers, planners, and developers to create destinations in harmony with natural surroundings. Keywords— Sustainable tourism, Tourism infrastructure, Physical parameters, Water usage, Waste management, Transportation systems

Improved implementation of concentrated plasticity models in real-time hybrid simulation

Real-time hybrid simulation (RTHS) is a cost-effective experimental testing technique for evaluating the dynamic response of structural systems. In RTHS, a portion of the structure is modeled numerically (i.e., numerical substructure), while the remaining part is tested physically (i.e., experimental substructure). These substructures are coupled in real time. A significant challenge in RTHS is accurately computing the dynamic response of the numerical substructure in real time during testing. Constrained by this challenge, numerical substructures in RTHS are often oversimplified (e.g., spring-mass-dashpot models), failing to capture more complex dynamic behaviors and limiting the applicability of RTHS. However, with the increasing use of innovative structural systems, new materials, and advanced design guidelines to ensure resilient and sustainable civil infrastructure against natural hazards, the demand for real-time executable higher-fidelity numerical substructures in RTHS is growing. Consequently, concentrated plasticity model (CPM) has gained attention as the numerical substructure in RTHS for its good balance between numerical accuracy and computational efficiency, as well as its popularity in structural engineering applications. Currently, static condensation is utilized to prevent an increase in computational degrees of freedom (DOFs) induced by the hinges in CPM, aiming to save computational cost. However, this method proves counterproductive for nonlinear analyses. While reducing the number of computational DOFs, it complicates the stiffness representation, thereby necessitating more demanding arithmetic operations for stiffness updating. In this study, multi-point constraints are applied to enhance the computational efficiency, specifically for nonlinear analyses involving stiffness updating, and the versatility of CPM. Moreover, direct stiffness updating method and sparse matrix-vector multiplication are introduced for further enhancement in computation. An open-source MATLAB/Simulink-based computational tool, namely the Hybrid2D, is developed to promote the use of the improved CPMs in RTHS as numerical substructures. Numerical simulations are first conducted to verify the accuracy and the computational efficiency of the improved CPMs in Hybrid2D. Subsequently, two RTHS examples are experimentally conducted to demonstrate the effectiveness of the Hybrid2D in enabling RTHS with a higher-fidelity numerical substructure for more complex structural systems.