Development Of Information Model Research Articles

Informational Modeling of Cities: Method and Challenges for Institutional Implementation

ABSTRACT Although cities are increasingly permeated by information and communication technologies (ICTs), these urban centers still face theoretical and practical obstacles regarding the application of 3D technologies as a tool for urban planning and management. This study aims to ontologically structure an informational model as an urban management tool and assess the perceived challenges for implementing this model in the municipal public management of Curitiba, Brazil. The study was carried out through the collective subject discourse, the interviews’ thematic saturation, and Delphi techniques. The results indicate that the technical capacity, technological infrastructure, and financial resources for the development of a city information modeling (CIM) exist, although its activation for such development depends on a more significant political and managerial entrepreneurship in the decision-making in arrangements of multilevel governance, and, consequently, there is a need to develop public policies for staff training to stimulate and promote advanced technological innovations for urban management and planning. Implementing a CIM in large cities seems to face more management and planning problems due to the lack of institutional ontologies than technological, infrastructural, or knowledge limitations.

Convergence in simulating global soil organic carbon by structurally different models after data assimilation.

Current biogeochemical models produce carbon-climate feedback projections with large uncertainties, often attributed to their structural differences when simulating soil organic carbon (SOC) dynamics worldwide. However, choices of model parameter values that quantify the strength and represent properties of different soil carbon cycle processes could also contribute to model simulation uncertainties. Here, we demonstrate the critical role of using common observational data in reducing model uncertainty in estimates of global SOC storage. Two structurally different models featuring distinctive carbon pools, decomposition kinetics, and carbon transfer pathways simulate opposite global SOC distributions with their customary parameter values yet converge to similar results after being informed by the same global SOC database using a data assimilation approach. The converged spatial SOC simulations result from similar simulations in key model components such as carbon transfer efficiency, baseline decomposition rate, and environmental effects on carbon fluxes by these two models after data assimilation. Moreover, data assimilation results suggest equally effective simulations of SOC using models following either first-order or Michaelis-Menten kinetics at the global scale. Nevertheless, a wider range of data with high-quality control and assurance are needed to further constrain SOC dynamics simulations and reduce unconstrained parameters. New sets of data, such as microbial genomics-function relationships, may also suggest novel structures to account for in future model development. Overall, our results highlight the importance of observational data in informing model development and constraining model predictions.

Inlet Guide Vane Stagger Effects on Multistage Compressor Performance and Stall Inception

The effects of variable inlet guide vanes on compressors have been evaluated throughout the turbomachinery industry for decades. These vanes improve both the performance and stability of a machine—the extent to which is highly dependent on the individual machine and the conditions of operation. Evaluating their effects on compressor stall inception via experimental investigations can inform model development and validation since commonly used RANS and URANS tools face limitations in accurately predicting and modeling stall events. The current literature on variable inlet guide vane restaggering, however, is dominated by numerical investigations. This study offers a unique experimental investigation into restaggering effects on compressor stall behavior by evaluating experimental results from different variable inlet guide vane stagger angles in a high-pressure axial compressor. Previous investigations have shown that variable inlet guide vane restaggering may change the stall inception mechanism of axial flow compressors. This investigation found a distinct change in the stage matching of the machine without altering the stall inception mechanism, further demonstrating the need for experimental investigations to inform the specific effects that restaggering inlet guide vanes offers comparable compressors.

A Standardized Benchmarking Framework to Assess Downscaled Precipitation Simulations

Abstract Presently, there is no standardized framework or metrics identified to assess regional climate model precipitation output. Because of this, it can be difficult to make a one-to-one comparison of their performance between regions or studies, or against coarser-resolution global climate models. To address this, we introduce the first steps toward establishing a dynamic, yet standardized, benchmarking framework that can be used to assess model skill in simulating various characteristics of rainfall. Benchmarking differs from typical model evaluation in that it requires that performance expectations are set a priori. This framework has innumerable applications to underpin scientific studies that assess model performance, inform model development priorities, and aid stakeholder decision-making by providing a structured methodology to identify fit-for-purpose model simulations for climate risk assessments and adaptation strategies. While this framework can be applied to regional climate model simulations at any spatial domain, we demonstrate its effectiveness over Australia using high-resolution, 0.5° × 0.5° simulations from the CORDEX-Australasia ensemble. We provide recommendations for selecting metrics and pragmatic benchmarking thresholds depending on the application of the framework. This includes a top tier of minimum standard metrics to establish a minimum benchmarking standard for ongoing climate model assessment. We present multiple applications of the framework using feedback received from potential user communities and encourage the scientific and user community to build on this framework by tailoring benchmarks and incorporating additional metrics specific to their application. Significance Statement We introduce a standardized benchmarking framework for assessing the skill of regional climate models in simulating precipitation. This framework addresses the lack of a uniform approach in the scientific community and has diverse applications in scientific research, model development, and societal decision-making. We define a set of minimum standard metrics to underpin ongoing climate model assessments that quantify model skill in simulating fundamental characteristics of rainfall. We provide guidance for selecting metrics and defining benchmarking thresholds, demonstrated using multiple case studies over Australia. This framework has broad applications for numerous user communities and provides a structured methodology for the assessment of model performance.

Problems and Tasks of Building a Digital Information Model of Buildings for the Implementation of Housing Capital Repair Programs

The purpose of the work is the development and use of digital information models (DIM) of apartment buildings (AB) of standard series of the Soviet period of construction at the stage of operation of the building. The stages and recommendations for the development of standard design DIM AB, requirements for the level of elaboration (detailing) of models are presented. The most common series of ABs in the central part of Arkhangelsk have been identified. With the help of Renga software, models have been developed for 4 standard series of AB. The level of elaboration is determined, on the one hand, by the possibility of using the model as an operational one without modification, on the other hand, by the possibility of refining it by increasing the level of detailing of the elements.

Development of an ontology-based asset information model for predictive maintenance in building facilities

PurposeThe purpose of this research is to develop a framework of an ontology-based Asset Information Model (AIM) for a Digital Twin (DT) platform and enhance predictive maintenance practices in building facilities that could enable proactive and data-driven decision-making during the Operation and Maintenance (O&M) process.Design/methodology/approachA scoping literature review was accomplished to establish the theoretical foundation for the current investigation. A study on developing an ontology-based AIM for predictive maintenance in building facilities was conducted. Semi-structured interviews were conducted with industry professionals to gather qualitative data for ontology-based AIM framework validation and insights.FindingsThe research findings indicate that while the development of ontology faced challenges in defining missing entities and relations in the context of predictive maintenance, insights gained from the interviews enabled the establishment of a comprehensive framework for ontology-based AIM adoption in the Facility Management (FM) sector.Practical implicationsThe proposed ontology-based AIM has the potential to enable proactive and data-driven decision-making during the process, optimizing predictive maintenance practices and ultimately enhancing energy efficiency and sustainability in the building industry.Originality/valueThe research contributes to a practical guide for ontology development processes and presents a framework of an Ontology-based AIM for a Digital Twin platform.

Development of a building information modelling based decision-making framework for green retrofitting of existing buildings

In recent years, sustainable development has received more attention in the construction industry along with lowering carbon emissions. As a result, green retrofitting of existing buildings has gained significant attention, which is effective in improving energy optimization and reducing environmental impact. Though there are many advantages and opportunities for implementing green retrofitting in existing buildings, the adoption of these practices remains relatively limited. Building Information Modelling (BIM) has become a game-changing technology in the construction industry and has been widely used for sustainability analysis of buildings and also used for retrofitting of existing buildings. However, the application of BIM for green retrofitting of existing buildings is unexplored. Hence, this paper aims to develop a framework for integrating BIM with green retrofitting to obtain suitable green retrofitting measures for existing buildings. The developed framework integrates sustainable goals with retrofitting strategies and facilitates decision-making on various green retrofitting measures. The developed framework is validated through a case study of an existing building and uses decision-making tools such as a pairwise-comparison matrix and priority matrix for the identification and selection of the best green retrofitting measures. On the application of the developed framework in a case study, 13 green retrofit measures were identified and three suitable retrofit measures were selected. This enabled the reduction of building energy requirements by 37 % and improved water saving by 7.2 %. This framework helps stakeholders, to visualize, analyze, and optimize various retrofit scenarios and make well-informed decisions on suitable retrofitting approaches. The framework can be implemented in any type of existing building and enables the identification of best retrofitting measures.

A Multi-Story Expandable Systematic Hierarchical Construction Information Classification System for Implementing Information Processing in Highway Construction

In the field of infrastructure construction, progress in digital transformation remains limited; this is particularly true in road construction, an infrastructure facility involving design, construction, and operation stages. Many construction subjects are involved at each stage of this cycle, generating substantial construction information. To drive the digital transformation of the construction industry, a construction information classification system is necessary for the development of a systematic construction information model. This study focuses on categorizing construction information into objects and activities, defining unit work by combining these two categories, and allowing for the flexible processing of construction information. A construction information classification system was developed, representing the evolving construction-related methodological information throughout the project’s lifecycle. Applying this multi-story expandable systematic hierarchical system to a highway project demonstrates the representation of key tasks in each construction phase, enabling the future stage-specific expansion of construction information for individual tasks. The proposed system could advance the digital transformation in infrastructure construction.

Trends in Informatization of Electronic Music Composition Data in the Context of Distance Education

INTRODUCTION: With the development of information technology in education, many educational institutions and companies have flocked to the emerging online education market. MOOC is a new method of e-learning that has changed the world and significantly impacted the educational community.OBJECTIVES: This paper aims to present typical features for developing an online music education platform using MOOC music, AI-based music learning software, and interactive real-time music learning.METHODS: This paper discusses different models of music teaching methods and pedagogy and summarizes the advantages and disadvantages of such models.RESULTS: Online music education is an internet technology that fully uses the Internet right to organize good music education.CONCLUSION: Based on the characteristics and content of music education, this paper presents possible suggestions for the sustainable and healthy development of new data and information models for music education on the Internet in the future.

Upsampling wave temporal resolution: Investigating wave parameters and the influence on WEC power performance

Power production of wave energy converters (WEC) predicted in the time domain utilize wave resource parameters and time-domain hydrodynamic model simulations of the WEC. While the hydrodynamic model provides high temporal resolution of power production (10’s of Hz), the wave resource parameters are often based on frequency-domain calculations with temporal resolution of 30 minutes to an hour. However, real ocean wave conditions vary on much shorter time scales. Relying on frequency-domain calculations will not be sufficient to capture the short-term variability and accurately predict WEC power production for a standardized methodology that follows power system requirements. These requirements need forecasted data with high sampling frequency for accurate energy predictions. Looking specifically at resource characterization, high temporal resolution datasets are not publicly available or do not exist for many coastal locations. Due to data availability, low temporal resolution datasets are being used in a majority of studies to generate representative wave conditions as inputs to numerical simulations. Representative wave conditions are used to generate wave spectrums. The issue with this practice is spectrums are then used to predict the efficiency of systems that will not accurately capture the variability of waves in short timeframes. Creating a standardized methodology to increase the temporal resolution of metaocean conditions to inform model development can provide better forecasting of power production. Random amplitude, Fourier coefficient methods have been suggested for WEC simulations of finite durations to improve the observed variability in wave heights and power production. Variability using this method does increase for finite durations compared to the commonly used deterministic amplitude method. In this paper we will investigate the influence of wave parameters (significant wave height, maximum wave height, and energy period) on the prediction of WEC power production. A better understanding of the influence of these parameters will provide a path towards future standardization methodology for resource inputs for time-domain modeling.

Analysis of Modern Software for Virtual Construction (4D Modeling)

Statement of the problem. Due to the uniqueness of each capital construction object and the variety of digital modeling software systems, it is of scientific and practical interest to substantiate the feasibility of using a particular software package. Results. An analysis of the functionality of popular software systems for the development of digital information models at the construction stage using BIM and TIM technologies (Navisworks Manage, Bexel Manager, Synchro 4D Pro, Asta Powerproject BIM, DELMIA) is provided. Their major disadvantages and advantages are identified and detailed. Conclusions. The choice of a software package by a contractor for the development of a construction model (4D model) according to BIM or TIM should be based on an analysis of a number of factors, one of which is the technical complexity of the construction object. The richer in resources and attributes the building is, the more reliable and closer it will be to the real conditions of construction, and the less organizational errors will be detected at the construction site.

Calibrating low-cost sensors to measure vertical and horizontal gradients of NO2 and O3 pollution in three street canyons in Berlin

Despite improvements in air quality over the last several decades, air pollution will continue to be a leading cause of harmful health effects in European cities as urban populations continue to grow. In recent years, the technology of low-cost sensors (LCS) has been adapted for use in expanding air pollution measurements at higher spatial resolution in cities across the globe. In a novel application, this exploratory study deploys metal oxide (MOS) and electrochemical (EC) low-cost sensors housed in EarthSense Zephyr sensor systems to measure nitrogen dioxide (NO2) and ozone (O3) concentrations in three street canyons in Berlin in winter, spring, and summer from 2017 to 2020. After calibration with reference instrumentation using the seven-step methodology outlined by Schmitz et al. (2021), we compare the measured concentrations with reference and urban background concentrations and investigate relationships with meteorology. We find that, following proper calibration, LCS capture expected patterns of urban pollution in association with diurnal chemistry and meteorology well. Additionally, EC sensors outperform MOS and allow for greater insights into local patterns of pollution. Furthermore, we measure concentrations of NO2 and O3 in street canyons that match expectations from modelling studies, indicating that high spatial resolution deployment of LCS could successfully yield new insights in urban microenvironments and inform model development. While LCS have a wider range of uncertainty than reference instruments, these results suggest that they can be reliably used for several new applications, such as validating urban street canyon models or measuring air pollution alongside changes to urban infrastructure.

ISO 19650.3 and the digitisation of operations in strata-titled residential apartment developments

The purpose of this paper is to identify shortcomings of the application of ISO 19650.3: Organisation and digitisation of information about buildings and civil engineering works including building information modelling (BIM) Information management using building information modelling Part 3: Operational phase of the assets during the design, documentation, and construction of strata-titled residential developments. The problem is that the original owner/clients of these speculative developments have little interest in the long-term management or operations of these buildings. As a result, these owners are not motivated to ensure that the information required for the development of an asset information model (AIM) is defined, identified, and provided to the future owners of these buildings. This paper compares traditionally accepted development models of most commercial, industrial, and institutional projects to that of apartments sold ‘off-plan’ during construction, using a content analysis of ISO 19650.3 as the theoretical lens. The theoretical application of ISO 19650 to strata-titled residential development identified challenges to the processes identified in this standard. This research made it clear that ISO 19650.3 cannot be applied to speculative development in its current form and that modifications are required if the digitisation of operations of speculative residential development is to be realized.

Geomatic Data Fusion for 3D Tree Modeling: The Case Study of Monumental Chestnut Trees

In recent years, advancements in remote and proximal sensing technology have driven innovation in environmental and land surveys. The integration of various geomatics devices, such as reflex and UAVs equipped with RGB cameras and mobile laser scanners (MLS), allows detailed and precise surveys of monumental trees. With these data fusion method, we reconstructed three monumental 3D tree models, allowing the computation of tree metric variables such as diameter at breast height (DBH), total height (TH), crown basal area (CBA), crown volume (CV) and wood volume (WV), even providing information on the tree shape and its overall conditions. We processed the point clouds in software such as CloudCompare, 3D Forest, R and MATLAB, whereas the photogrammetric processing was conducted with Agisoft Metashape. Three-dimensional tree models enhance accessibility to the data and allow for a wide range of potential applications, including the development of a tree information model (TIM), providing detailed data for monitoring tree health, growth, biomass and carbon sequestration. The encouraging results provide a basis for extending the virtualization of these monumental trees to a larger scale for conservation and monitoring.

Hindcasting to inform the development of bottom-up electricity system models: The cases of endogenous demand and technology learning

Bottom-up, technology-rich electricity system models are commonly used to generate scenarios for policy support at a national or global level. In literature, previous hindcasting studies (also called retrospective modeling or ex-post modeling) evaluated existing models rather than sought to inform model development from the beginning. In this study, we present a hindcasting exercise with D-EXPANSE model for national electricity systems in 31 European countries over the 1990–2019 period. We develop several model versions with or without elastic electricity demand and with or without endogenous technology learning, and use hindcasting to choose the most accurate configuration of the bottom-up model. The hindcasting results show that a model with endogenous elastic demand can capture well the real-world evolution of electricity demand, if elasticity factor is chosen well and if the countries did not undergo severe structural changes. Endogenous technology learning, however, increases the uptake of new emerging technologies in cost-optimal scenarios, but still cannot fully capture the real-world dynamics and at times even introduces further inaccuracies.

The Effectiveness of Information and Communication Technology-Based Supervision Model Development at SMK Surakarta

<em>The purpose of this study is to test the feasibility of an academic supervision model based on information and communication technology. Feasibility consists of three aspects: validity, practicality, and effectiveness. This research was developed by the R&D method, which was modified into five steps, namely the define stage, design stage, development stage, and dissemination stage. The field test design is the One Group Pretest-Posttest Design. The results show that technology-based, informatics, and communication academic supervision modules with Microsoft Teams are appropriate to be applied in the implementation of technology-based academic supervision, informatics, and communication. The results obtained show that the developed model is valid, practical, and effective in increasing progress in the process of academic supervision based on technology, informatics, and communication. With this model of maximum teacher assessment, all are assessed. The assessment of academic supervision went smoothly; 126 teachers could be assessed all and more optimally. Researchers recommend that school principals implement this supervision model using Microsoft Teams. Further research is needed using different applications using models that have been developed in academic supervision assessments.</em>

Approach to Reference Models for Building Performance Simulation: Establishing Common Understanding

In the fields of business process modeling, logistics, and information model development, Reference Models (RMs) have shown to enhance standardization, support the common understanding of terminology and procedures, reduce the modeling efforts and cost through the paradigm “Design by Reuse”, and enable knowledge transfer. Utilizing RMs in Building Performance Simulation (BPS) shows potential to achieve similar benefits. However, there is no universally agreed understanding of RMs. In a previous scientific publication, we provided a comprehensive overview of the diversely interpreted definitions, benefits, and attributes of RMs and related terms. Additionally, to transfer the approach of RMs to BPS, a definition for RMs applicable to BPS has been provided, and the identified RM qualities were matched with BPS’s challenges. However, a sound evaluation of the success of transferring RMs to BPS is lacking. Therefore, this scientific contribution firstly includes the analysis conducted in the previous scientific contribution constituting a common understanding about RMs and their elements for BPS. Secondly, by conducting expert interviews, the applicability and validity of the developed concept of RMs for BPS are surveyed. In total, ten experts (seven BPS experts and three RM experts) evaluated the quality of creating transparency about the understanding of RMs and the level of success of their transfer toward BPS. The experts consistently see a great benefit of RMs in BPS, but for BPS experts the transfer and possible application of RMs in BPS is not sufficiently clear. Accordingly, the key output of the conducted survey is that a clearer and more detailed application example, e.g., describing at a more easy-to-understand level of detail an exemplary class of the provided example of an RM, is required for a more profound transfer of RMs to BPS.

Asset information requirements for blockchain-based digital twins: a data-driven predictive analytics perspective

PurposeThe purpose of this study is to identify the key data categories and characteristics defined by asset information requirements (AIR) and how this affects the development and maintenance of an asset information model (AIM) for a blockchain-based digital twin (DT).Design/methodology/approachA mixed-method approach involving qualitative and quantitative analysis was used to gather empirical data through semistructured interviews and a digital questionnaire survey with an emphasis on AIR for blockchain-based DTs from a data-driven predictive analytics perspective.FindingsBased on the analysis of results three key data categories were identified, core data, static operation and maintenance (OM) data, and dynamic OM data, along with the data characteristics required to perform data-driven predictive analytics through artificial intelligence (AI) in a blockchain-based DT platform. The findings also include how the creation and maintenance of an AIM is affected in this context.Practical implicationsThe key data categories and characteristics specified through AIR to support predictive data-driven analytics through AI in a blockchain-based DT will contribute to the development and maintenance of an AIM.Originality/valueThe research explores the process of defining, delivering and maintaining the AIM and the potential use of blockchain technology (BCT) as a facilitator for data trust, integrity and security.

Asset Information Model to Increase Traceability in Offshore Wind Projects

Asset Information Model to Increase Traceability in Offshore Wind Projects

Use of Modeling to Inform Decision Making in North Carolina during the COVID-19 Pandemic: A Qualitative Study.

Background. The COVID-19 pandemic has popularized computer-based decision-support models, which are commonly used to inform decision making amidst complexity. Understanding what organizational decision makers prefer from these models is needed to inform model development during this and future crises. Methods. We recruited and interviewed decision makers from North Carolina across 9 sectors to understand organizational decision-making processes during the first year of the COVID-19 pandemic (N = 44). For this study, we identified and analyzed a subset of responses from interviewees (n = 19) who reported using modeling to inform decision making. We used conventional content analysis to analyze themes from this convenience sample with respect to the source of models and their applications, the value of modeling and recommended applications, and hesitancies toward the use of models. Results. Models were used to compare trends in disease spread across localities, estimate the effects of social distancing policies, and allocate scarce resources, with some interviewees depending on multiple models. Decision makers desired more granular models, capable of projecting disease spread within subpopulations and estimating where local outbreaks could occur, and incorporating a broad set of outcomes, such as social well-being. Hesitancies to the use of modeling included doubts that models could reflect nuances of human behavior, concerns about the quality of data used in models, and the limited amount of modeling specific to the local context. Conclusions. Decision makers perceived modeling as valuable for informing organizational decisions yet described varied ability and willingness to use models for this purpose. These data present an opportunity to educate organizational decision makers on the merits of decision-support modeling and to inform modeling teams on how to build more responsive models that address the needs of organizational decision makers.HighlightsOrganizations from a diversity of sectors across North Carolina (including public health, education, business, government, religion, and public safety) have used decision-support modeling to inform decision making during COVID-19.Decision makers wish for models to project the spread of disease, especially at the local level (e.g., individual cities and counties), and to help estimate the outcomes of policies.Some organizational decision makers are hesitant to use modeling to inform their decisions, stemming from doubts that models could reflect nuances of human behavior, concerns about the accuracy and precision of data used in models, and the limited amount of modeling available at the local level.