Representing a Digital Twin City Model Using Open Source Tools and Integrating It With Dynamic Sensor Data

2021 ◽  
pp. 295-319
Author(s):  
Fabrizio Massara ◽  
Tatsiana Hubina ◽  
Sara Mannoni ◽  
Adelaide Ramassotto ◽  
Fabrizio Barbero
Keyword(s):  
Real Time ◽  
3D Models ◽  
Sensor Data ◽  
Time Data ◽  
Building Information Modelling ◽  
Twin City ◽  
Building Information ◽  
City Model ◽  
High Level ◽  
The City

This work presents the developments of representing a part of the city districts of Manchester, UK and Turin, IT initiated within the FP7 DIMMER project completed in 2016 and continued in the last years by the Center of Excellence GIS of CSI Piemonte. The DIMMER system integrates BIM (building information modelling) and district level 3D models with real-time data from sensors and user feedback to analyze and correlate buildings utilization and provide real-time feedback about energy-related behaviors. The emerging standard 3D Tiles endorsed by the OGC was applied to represent CityGML data of two districts of Turin, Italy and Manchester, UK. 3D Tiles allows for a high level of detail (LOD) visualization that displays increasing detail of geometric features and their alphanumeric properties. Currently, the OGC 3D Tiles technology is mature, and the OGC CityGML specification will be soon updated to version three, with the adoption of exciting innovations like the support of time-dependent properties defined Dynamizers.

scholarly journals CULTURAL HERITAGE MONUMENT DOCUMENTATION THROUGH ADAPTIVE PARAMETRIC DESIGN PROCESS

2021 ◽  
Vol XLVI-M-1-2021 ◽  
pp. 769-776
Author(s):  
G. Tryfonos ◽  
M. Ioannides ◽  
A. G. Anastasi ◽  
V. A. Apostolou ◽  
P. P. Pieri ◽  
...  
Keyword(s):  
Cultural Heritage ◽  
Parametric Design ◽  
3D Models ◽  
Building Information Modelling ◽  
3D Data ◽  
Time Period ◽  
Building Information ◽  
Game Developers ◽  
The Given ◽  
The City

Abstract. The paper presents a novel adaptive parametric documentation, modelling and sharing methodology, which aims to achieve a continuous holistic documentation, data processing and sharing process for cultural heritage community, such as architects, engineers, archaeologists, conservators, programmers, fabricators, contest creators, game developers, scholars and common citizens. Thus, the use of advance parametric and building information modelling software allows the processing and specification of all data by creating the 3D models needed for the multidisciplinary experts. Two Cypriot case studies from the medieval time period have been chosen for the development, and evaluation of our proposed methodology in order to investigate the process of modelling and sharing all the given metadata and 3D data. The first one is the Asinou Church, a UNESCO Heritage stone monument in the Troodos Mountains with a unique interior and the Kolossi Castle, a former Crusader stronghold on the west of the city of Limassol on the island of Cyprus.

Innovative real-time sensing of flow dynamics in groundwater and sediments to map contaminant spreading

2021 ◽  
Author(s):  
Goedele Verreydt ◽  
Niels Van Putte ◽  
Timothy De Kleyn ◽  
Joris Cool ◽  
Bino Maiheu
Keyword(s):  
Surface Water ◽  
Groundwater Flow ◽  
Real Time ◽  
Flow Direction ◽  
Flow Dynamics ◽  
Sensor Data ◽  
Time Data ◽  
Mass Fluxes ◽  
First Case

<p>Groundwater dynamics play a crucial role in the spreading of a soil and groundwater contamination. However, there is still a big gap in the understanding of the groundwater flow dynamics. Heterogeneities and dynamics are often underestimated and therefore not taken into account. They are of crucial input for successful management and remediation measures. The bulk of the mass of mass often is transported through only a small layer or section within the aquifer and is in cases of seepage into surface water very dependent to rainfall and occurring tidal effects.</p><p> </p><p>This study contains the use of novel real-time iFLUX sensors to map the groundwater flow dynamics over time. The sensors provide real-time data on groundwater flow rate and flow direction. The sensor probes consist of multiple bidirectional flow sensors that are superimposed. The probes can be installed directly in the subsoil, riverbed or monitoring well. The measurement setup is unique as it can perform measurements every second, ideal to map rapid changing flow conditions. The measurement range is between 0,5 and 500 cm per day.</p><p> </p><p>We will present the measurement principles and technical aspects of the sensor, together with two case studies.</p><p> </p><p>The first case study comprises the installation of iFLUX sensors in 4 different monitoring wells in a chlorinated solvent plume to map on the one hand the flow patterns in the plume, and on the other hand the flow dynamics that are influenced by the nearby popular trees. The foreseen remediation concept here is phytoremediation. The sensors were installed for a period of in total 4 weeks. Measurement frequency was 5 minutes. The flow profiles and time series will be presented together with the determined mass fluxes.</p><p> </p><p>A second case study was performed on behalf of the remediation of a canal riverbed. Due to industrial production of tar and carbon black in the past, the soil and groundwater next to the small canal ‘De Lieve’ in Ghent, Belgium, got contaminated with aliphatic and (poly)aromatic hydrocarbons. The groundwater contaminants migrate to the canal, impact the surface water quality and cause an ecological risk. The seepage flow and mass fluxes of contaminants into the surface water were measured with the novel iFLUX streambed sensors, installed directly in the river sediment. A site conceptual model was drawn and dimensioned based on the sensor data. The remediation concept to tackle the inflowing pollution: a hydraulic conductive reactive mat on the riverbed that makes use of the natural draining function of the waterbody, the adsorption capacity of a natural or secondary adsorbent and a future habitat for micro-organisms that biodegrade contaminants. The reactive mats were successfully installed and based on the mass flux calculations a lifespan of at least 10 years is expected for the adsorption material.  </p>

scholarly journals Assessment of BIM Implementation among MEP Firms in Nigeria

2015 ◽  
Vol 4 (3) ◽  
pp. 73 ◽  
Author(s):  
Chen Wang ◽  
Shittu Hammed Adetola ◽  
Hamzah Abdul-Rahman
Keyword(s):  
Process Change ◽  
Chi Square ◽  
Building Information Modelling ◽  
Significant Difference ◽  
Investment Cost ◽  
Building Information ◽  
High Investment ◽  
Chi Square Test ◽  
High Level ◽  
One Way Anova

<p>The purpose of this study is to assess the awareness level and potential challenges for Building Information Modelling (BIM) adoption among the Mechanical, Electrical and Plumbing (MEP) design consultants and contractors in Nigeria, and to get the perspective of BIM in the industry. The research method used in this study was a fieldwork survey using structured questionnaires. The results from the responses were analysed using descriptive statistics, one-way ANOVA test for statistical significant difference, Chi-Square test, and Cross Tab analysis. The findings from the survey show that Nigeria MEP firms have a relatively high level of awareness toward BIM technology. The Most important challenges identified as barriers for BIM adoption are lack of technical expertise on BIM tools utilisation, lack of awareness of BIM technology, and high investment cost in training staff, process change, and software/hardware upgrade.</p>

BOT: The building topology ontology of the W3C linked building data group

Semantic Web ◽  
2020 ◽  
Vol 12 (1) ◽  
pp. 143-161 ◽  
Author(s):  
Mads Holten Rasmussen ◽  
Maxime Lefrançois ◽  
Georg Ferdinand Schneider ◽  
Pieter Pauwels
Keyword(s):  
Information Exchange ◽  
Real Life ◽  
3D Models ◽  
Maturity Level ◽  
Community Group ◽  
Web Based ◽  
Building Models ◽  
Building Information ◽  
Level 3 ◽  
High Level

Actors in the Architecture, Engineering, Construction, Owner and Operation (AECOO) industry traditionally exchange building models as files. The Building Information Modelling (BIM) methodology advocates the seamless exchange of all information between related stakeholders using digital technologies. The ultimate evolution of the methodology, BIM Maturity Level 3, envisions interoperable, distributed, web-based, interdisciplinary information exchange among stakeholders across the life-cycle of buildings. The World Wide Web Consortium Linked Building Data Community Group (W3C LBD-CG) hypothesises that the Linked Data models and best practices can be leveraged to achieve this vision in modern web-based applications. In this paper, we introduce the Building Topology Ontology (BOT) as a core vocabulary to this approach. It provides a high-level description of the topology of buildings including storeys and spaces, the building elements they contain, and their web-friendly 3D models. We describe how existing applications produce and consume datasets combining BOT with other ontologies that describe product catalogues, sensor observations, or Internet of Things (IoT) devices effectively implementing BIM Maturity Level 3. We evaluate our approach by exporting and querying three real-life large building models.

scholarly journals Fossel: Efficient Latency Reduction in Approximating Streaming Sensor Data

2020 ◽  
Vol 12 (23) ◽  
pp. 10175
Author(s):  
Fatima Abdullah ◽  
Limei Peng ◽  
Byungchul Tak
Keyword(s):  
Real Time ◽  
Data Analytics ◽  
Streaming Data ◽  
Sensor Data ◽  
Simple Type ◽  
Data Sampling ◽  
Time Data ◽  
Latency Reduction ◽  
Network Delays ◽  
Real Time Data

The volume of streaming sensor data from various environmental sensors continues to increase rapidly due to wider deployments of IoT devices at much greater scales than ever before. This, in turn, causes massive increase in the fog, cloud network traffic which leads to heavily delayed network operations. In streaming data analytics, the ability to obtain real time data insight is crucial for computational sustainability for many IoT enabled applications such as environmental monitors, pollution and climate surveillance, traffic control or even E-commerce applications. However, such network delays prevent us from achieving high quality real-time data analytics of environmental information. In order to address this challenge, we propose the Fog Sampling Node Selector (Fossel) technique that can significantly reduce the IoT network and processing delays by algorithmically selecting an optimal subset of fog nodes to perform the sensor data sampling. In addition, our technique performs a simple type of query executions within the fog nodes in order to further reduce the network delays by processing the data near the data producing devices. Our extensive evaluations show that Fossel technique outperforms the state-of-the-art in terms of latency reduction as well as in bandwidth consumption, network usage and energy consumption.

scholarly journals Bifrost: A Python/C++ Framework for High-Throughput Stream Processing in Astronomy

2017 ◽  
Vol 06 (04) ◽  
pp. 1750007 ◽  
Author(s):  
Miles D. Cranmer ◽  
Benjamin R. Barsdell ◽  
Danny C. Price ◽  
Jayce Dowell ◽  
Hugh Garsden ◽  
...  
Keyword(s):  
Real Time ◽  
High Throughput ◽  
Radio Astronomy ◽  
Scientific Productivity ◽  
Data Transport ◽  
Time Data ◽  
Process Data ◽  
Real Time Processing ◽  
Real Time Data Processing ◽  
High Level

Radio astronomy observatories with high throughput back end instruments require real-time data processing. While computing hardware continues to advance rapidly, development of real-time processing pipelines remains difficult and time-consuming, which can limit scientific productivity. Motivated by this, we have developed Bifrost: an open-source software framework for rapid pipeline development. (a) Bifrost combines a high-level Python interface with highly efficient reconfigurable data transport and a library of computing blocks for CPU and GPU processing. The framework is generalizable, but initially it emphasizes the needs of high-throughput radio astronomy pipelines, such as the ability to process data buffers as if they were continuous streams, the capacity to partition processing into distinct data sequences (e.g. separate observations), and the ability to extract specific intervals from buffered data. Computing blocks in the library are designed for applications such as interferometry, pulsar dedispersion and timing, and transient search pipelines. We describe the design and implementation of the Bifrost framework and demonstrate its use as the backbone in the correlation and beamforming back end of the Long Wavelength Array (LWA) station in the Sevilleta National Wildlife Refuge, NM.

scholarly journals Semantic Traffic Sensor Data: The TRAFAIR Experience

2020 ◽  
Vol 10 (17) ◽  
pp. 5882
Author(s):  
Federico Desimoni ◽  
Sergio Ilarri ◽  
Laura Po ◽  
Federica Rollo ◽  
Raquel Trillo-Lado
Keyword(s):  
Air Quality ◽  
Real Time ◽  
Smart Cities ◽  
Low Cost ◽  
Semantic Annotation ◽  
Open Data ◽  
Transportation Systems ◽  
Sensor Data ◽  
Time Data ◽  
Future Evolution

Modern cities face pressing problems with transportation systems including, but not limited to, traffic congestion, safety, health, and pollution. To tackle them, public administrations have implemented roadside infrastructures such as cameras and sensors to collect data about environmental and traffic conditions. In the case of traffic sensor data not only the real-time data are essential, but also historical values need to be preserved and published. When real-time and historical data of smart cities become available, everyone can join an evidence-based debate on the city’s future evolution. The TRAFAIR (Understanding Traffic Flows to Improve Air Quality) project seeks to understand how traffic affects urban air quality. The project develops a platform to provide real-time and predicted values on air quality in several cities in Europe, encompassing tasks such as the deployment of low-cost air quality sensors, data collection and integration, modeling and prediction, the publication of open data, and the development of applications for end-users and public administrations. This paper explicitly focuses on the modeling and semantic annotation of traffic data. We present the tools and techniques used in the project and validate our strategies for data modeling and its semantic enrichment over two cities: Modena (Italy) and Zaragoza (Spain). An experimental evaluation shows that our approach to publish Linked Data is effective.

Real-Time Bus-Holding Control Strategy to Reduce Passenger Waiting Time

2017 ◽  
Vol 2647 (1) ◽  
pp. 9-16 ◽  
Author(s):  
Mohamadamin Asgharzadeh ◽  
Yousef Shafahi
Keyword(s):  
Real Time ◽  
Waiting Time ◽  
Control Strategy ◽  
Gradient Descent ◽  
Waiting Times ◽  
Heuristic Method ◽  
Bus Rapid Transit ◽  
Time Data ◽  
Using Data ◽  
The City

This paper presents research on a real-time bus-holding control strategy that minimizes passenger waiting time. This bus-holding strategy forces buses to hold at stations for a while after a passenger exchange is finished. A mathematical model is proposed to determine the optimal holding time. Both onboard and on-station passenger waiting times have been taken into account. Given the real-time nature of the problem, a heuristic method based on gradient descent algorithms was developed. The proposed control strategy was evaluated by using data derived from a shuttle bus rapid transit (BRT) line in the city of Mashhad, Iran. The BRT line was simulated and calibrated by available empirical and real-time data from the automatic vehicle location and automatic passenger counting systems. The results indicate that the proposed bus-holding strategy reduces total passenger waiting time by 8.65%.

scholarly journals A Framework for Real Time Processing of Sensor Data in the Cloud

2015 ◽  
Vol 2015 ◽  
pp. 1-11 ◽  
Author(s):  
Supun Kamburugamuve ◽  
Leif Christiansen ◽  
Geoffrey Fox
Keyword(s):  
Data Processing ◽  
Real Time ◽  
Cloud Services ◽  
Sensor Data ◽  
Smart Devices ◽  
Time Data ◽  
Real Time Processing ◽  
Real Time Data ◽  
Autonomous Robot Navigation ◽  
Real Time Data Processing

We describe IoTCloud, a platform to connect smart devices to cloud services for real time data processing and control. A device connected to IoTCloud can communicate with real time data analysis frameworks deployed in the cloud via messaging. The platform design is scalable in connecting devices as well as transferring and processing data. With IoTCloud, a user can develop real time data processing algorithms in an abstract framework without concern for the underlying details of how the data is distributed and transferred. For this platform, we primarily consider real time robotics applications such as autonomous robot navigation, where there are strict requirements on processing latency and demand for scalable processing. To demonstrate the effectiveness of the system, a robotic application is developed on top of the framework. The system and the robotics application characteristics are measured to show that data processing in central servers is feasible for real time sensor applications.

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