Optimise BIM Modelling in Revit for Complex Construction Projects, Reduce Time Spent on Measurements and As-Builts
Aryn Bergman, lead engineer and founder of TL Circle, is frequently confronted with complex engineering projects requiring days of laborious, time-consuming BIM modelling in Revit, usually from hand-drawn measurements, notes and photographs. One project he recently completed involved a complicated MEP room of a high-rise apartment building in which the domestic water heater needed to be retrofit.
(This story is brought to you by Matterport)
Hand measuring the boiler room - from wall to pipe, from pipe to pipe, floor to ceiling, floor to pipe, etc. - would take 4-5 hours for a contractor to accomplish, and 4-5 days for an engineer to model in Revit, as well as potential additional follow on site-visits.
Bergman also knew traditional laser scanning (e.g. with a Leica system) in such a complicated and tight space would require a large number of scan positions. Laser scanning would also take longer than manual measurements in addition to the time required to register the point clouds and still require hand modelling. Finally, acquiring a laser scanner would add a significant unnecessary, expense to his budget.
So he began searching for new tools which would make modeling the MEP room more efficient, without adding additional time or cost to the budget.
Manual measurements for modelling as builts represent a significant time expense per project
New tool needed to simplify and expedite the modelling process
New tool required to sync with current Revit/ReCap workflow
Matterport Scanning 60% Faster than Hand Measuring, Point Clouds Speed Revit Modeling by 40%
With the Matterport Pro 3D Camera, Bergman was able to capture a medium-density single registered point cloud (200 MB) of the MEP room in under an hour. Because he wanted to double check the accuracy of the point cloud, he still took the time to manually hand-measure the MEP room, which took him approximately 2-3 hours to accomplish. Once he had the point cloud, he was able to upload and index it in ReCap to view 3D model. He did this to retain the colorization of the point clouds, as importing Matterport point clouds directly into Revit can remove the colorization of the points.
From there, Bergman saved the ReCap file and imported it directly into Revit to model on top of the 3D structure. When he compared the point cloud data to his hand-taken measurements, he found they were accurate up to the centimetre. With similar projects, modelling in Revit with hand measurements would have taken 4-5 days to trace over the as built. With the point cloud, it took him 2. Due to the accuracy of the point cloud, measuring in the future will be minimized, cutting his time spent measuring by 60%. And since the point cloud is delivered from Matterport as a single, fully registered point cloud there is no time spent manually registering the data to get a final point cloud.
Modelling time in Revit was decreased by 40% which was also expedited by his 24/7 access to perfect, immersive visual references to the job site. This reference material is immediately at hand without having to manage any image files. And modelling with both an accurate point cloud and visuals is the key to modelling efficiency.
Bergman frequently will go back to the job site to visually confirm that the Revit model matches reality. With the Matterport 3D Showcase he no longer has a need to go back on site to confirm that the existing conditions were modelled correctly, removing as much as a day’s travel from his schedule.
Scan space using the Matterport Pro 3D Camera (30-60min/room)
Download the pre-registered Matterport point cloud (200 MB)
Import the point cloud into ReCap (to retain colorization)
Import ReCap file into Revit
Trace over 3D point cloud to create BIM model
Field-to-Finish in 50% of the Time Enables TL Circle to Offer Competitive Pricing 30%-40% Below Previous Rate
The typical price for building a BIM model of a MEP boiler project like this would be approximately US$10k, but with the optimized modelling process, the price drops to US$5K-6K. Bergman estimates that the field to finish time for projects is cut in half when compared to projects done with manual or laser system. By expediting the process, Bergman is able to offer more competitive prices, which has opened him up to new markets as well as increase wages for his employees.
Other projects completed without the Matterport system had approximately 70% more inconsistencies with their as builts. With Matterport, that number was reduced to nearly 0.
“Matterport has helped cut my field to finish time in half. I’m excited to leverage this new tool to improve the BIM modeling process.”
Aryn Bergman, Owner of TL Circle
On demand webinar
Fast, Affordable Reality Capture for the Built Environment
AEC professionals from Gilbane, Hensel Phelps, Mortenson, and more are using Matterport’s Pro2 3D Camera and Cloud platform to streamline workflows, minimize labour costs, reduce site visits, and to mitigate risk. Sign up for our live webinar to learn how you can use Matterport’s 3D reality capture solution to make your projects more efficient and cost-effective.
http://www.GIM-INTERNATIONAL.com Miércoles 16 de Mayo del 2018
High-end technology-driven solutions often create serious implementation constraints in land administration. Furthermore, despite the developments and advances in geo-ICT, there is still a gap in the development of tools that model people-to-land relationships independently from the legality of those relationships. This article explains why the Social Tenure Domain Model (STDM) is a powerful and effective land information system to arrive at locally engineered solutions for improving tenure security.
While land administration plays a crucial macroeconomic role in the collection, management and dissemination of information about land tenure rights, land use and value, high-end technology-driven solutions often create serious implementation constraints where issues such as those related to licence costs of proprietary software have been reported to land administration programmes. In addition, despite the developments and advances in geo-ICT, there still exists a gap in the development of tools that model people-to-land relationships independently from the level of formalisation, or legality, of these relationships. The Social Tenure Domain Model (STDM) was designed to specifically address these challenges.
The STDM is a pro-poor, participatory, flexible and affordable land tool for representing people-to-land relationships along the ‘continuum of land rights’, independently of the level of formalisation and legality of those relationships. The basic way of defining any form of land right represented through the STDM is that a party has a social tenure relationship with a spatial unit supported by evidence-based source documents (Figure 1), and this also applies to related restrictions and responsibilities. A party can be a person, household or a cooperative society, whereas a parcel, informal structure, natural resources or building can be used to represent a spatial unit. The STDM is an initiative of the Global Land Tool Network (GLTN) in collaboration with its partners to support pro-poor land administration.
Its practical implementation involves participatory enumeration – a survey method which aims to gain better knowledge of the needs and priorities of a community through the administration of appropriate data collection tools. The STDM offers a practical way to address land administration problems related to recognising and recording land rights on the continuum.
The STDM tool is a desktop-based application which is designed to fully comply with the conceptual model in Figure 1. It brings together mature and stable open-source software projects through a consistent, easy-to-use interface, which allows non-specialised users to define and manage tenure information, visualise spatial units as well as support the creation of reports.
Customisation of Tenure Context
The STDM tool provides a data management interface for designing tables and corresponding attributes to meet the data requirements of different application contexts. The current release, version 1.7, enables the definition of compound tenure relationships such as different party types having tenure relationships with a given spatial unit type, or a party type having separate tenure relationships with different spatial units – e.g. a household having different sets of tenure relations with a house and farm respectively (Figure 2). Figure 3 shows this implementation in the STDM tool.
By default, every tenure relation between a party and spatial unit includes a percentage share of the right as well as information on start and end dates representing the duration for which the relation is valid. In addition, custom tenure-specific attributes, such as the identification number of tenure supporting documents, can be defined depending on the tenure context.
All the spatial and attribute information in the STDM is stored in a PostgreSQL/PostGIS database, with the user interface hosted as a QGIS extension, also known as a plug-in. The use of PostgreSQL and PostGIS software components enables the STDM to be deployed as a stand-alone installation or in a client-server environment. The former is mostly applicable in those areas where internet infrastructure does not exist or might be too costly to set up for the given programme, such as in urban informal settlements or rural areas.
Evidence-based Supporting Documents
The STDM provides an intuitive interface which enables the attachment of supporting documents for every record stored in the data repository (Figure 4). These documents provide proof or support claims within the given context. Such evidence from the field can be photographs or scanned documents which are subsequently uploaded into the software when defining the primary textual information. Examples of these can be photos of people or households, scanned copies of utility bills or even hand-written tenancy agreements in urban informal settlements.
The types of source documents that are applicable for each entity can be specified using the data management interface during the initial stage of defining the structure of the data profile.
Extension by Developers
The STDM is designed to be a flexible platform which enables software developers to extend, modify or integrate with existing enterprise systems in order to meet the functional requirements of their own applications. Its application programming interface (API) provides various extensibility points from definition of custom data types, data storage back ends, widgets for data types, document management back ends and data importation formatting to report design items and production.
The STDM is written in Python programming language and the developer community can participate in the open-source project on GitHub.
The Implementation Process
The application of the STDM promotes inclusiveness and continuous capacity development amongst all key stakeholders, from the initial inception up to the deployment and rollout stages. At the core of the implementation is the active participation of the local community leaders and members, who must be continuously engaged throughout the whole process.
The key activities include planning and consultations amongst the stakeholders where key issues pertaining to tenure security are discussed; this process also includes identifying the main areas where GLTN tools (i.e. continuum of land rights approach, gender evaluation criteria, STDM, participatory enumerations) can be applied. An implementation plan is developed which incorporates agreement on the roles and responsibilities of the different stakeholders (Figure 5). This is followed by an extensive mobilisation and sensitisation process which involves local government authorities as well as community leaders and members.
The data requirements of the project are identified and customised in the STDM to fit the local context. This is an iterative process which is considered final once all the stakeholders have reached agreement on the data attribution to be captured, using either digital or paper-based surveys. Data collection involves conducting interviews and plot or structure mapping, and the enumeration teams are usually accompanied by local leaders and local government officials. The mapping can be done using handheld GPS receivers (Figure 6) and/or high-resolution satellite images depending on the context. All the collected data, documents and photographs are entered into the STDM tool. In some instances, the initial digital maps are also updated at this stage. The enumerators are also trained to analyse the data and produce reports using the tool.
In the final stage, community members validate the collected data before it is subsequently updated in the STDM database. This validation process is critical as it improves the credibility of the overall process. Afterwards, the community members and local government officials agree on modalities of how to continue updating the data and sustaining the process, with backstopping provided by GLTN. Manuals, guidelines and process documents are produced, and translated if applicable, as part of standard outputs from these interventions.
As the STDM continues to evolve technically based on the convergence of new technologies with emerging country needs, GLTN is exploring new opportunities based on the strategic guidance provided by the STDM Advisory Committee. Examples of such opportunities include: establishing data-sharing mechanisms from community level to national level in line with fit-for-purpose principles of ‘incremental upgrading’; developing a sustainable business model; submitting the tool to the OSGeo Incubator programme; promoting the development of Open Geospatial Consortium (OGC) standards related to land administration through the LandAdmin Domain Working Group (DWG) and supporting the implementation of the Sustainable Development Goals (SDGs) – particularly 1, 2, 5 and 11) – in land tenure projects at country level. The use and application of the STDM in these strategic areas of interest will be closely aligned with GLTN’s Phase III strategy which is currently under development.
The capability of the STDM to further incorporate participatory approaches makes it a powerful and effective land information system to arrive at locally engineered solutions for improving tenure security. The uptake of the STDM has in some instances led to inclusion of the STDM-generated information in government initiatives such as the Transforming Settlements of the Urban Poor in Uganda project (TSUPU), establishment of a land information system to manage urban and customary land in Turkana County (Figure 7), and in the Kenya Informal Settlement Improvement Project (KISIP) in Kenya. The STDM is now seen as a significant tool that local governments can adopt for development objectives like inclusive planning, tenure security improvement, provision of basic services and infrastructure. A future article will focus on experiences and lessons learnt from implementations.
http://www.GIM-INTERNATIONAL.com Miércoles 09 de Mayo del 2018
Over the past decade, the world has witnessed a steady increase in the number of people forced away from their homes by natural disasters or political unrest. They often end up in camps that hold tens of thousands of refugees. The camps are supported by non-governmental organisations (NGOs) that provide food, shelter and medical assistance. To manage their operations, NGOs and other agencies need to know how many people are in a given camp. This article explains how a combination of high-resolution satellite Earth observation data and image processing software provides an efficient approach for estimating camp populations.
The refugee population is growing rapidly. Recent estimates from the United Nations indicate that nearly 68 million people fled their homes in 2016, up from 64 million in the previous year. Often located near war zones, the largest camps hold more than 200,000 people. When essential services such as food, clean water and sanitation are in short supply, malnutrition and sickness can take hold. Financial and material support is limited, though, and a camp’s infrastructure is often overtaxed. Because of their limited funding, NGOs have become adept at planning and resource allocation. Information on population size and trends for a given camp helps the NGOs to optimise the return on their spending. Producing good data, however, is not easy. Aircraft – including unmanned aerial vehicles (UAVs or ‘drones’) – and satellites can capture aerial images of the camps, but manually extracting reliable population data from these photos can be painfully slow and expensive. Médecins Sans Frontières (MSF), an NGO supporting refugee camps, teamed up with experts at the University of Salzburg’s Department of Geoinformatics (Z_GIS) to find a solution. Z_GIS conducts research into the use of satellite imagery with advanced image processing techniques to monitor populations. One of its key objectives is developing automated approaches to extract population information from satellite images of refugee camps.
To measure the populations, Z_GIS obtains very-high-resolution (VHR) imagery (less than 1 metre per pixel) from commercial suppliers such as DigitalGlobe and Airbus (Pleiades). The images are processed using Trimble’s eCognition software to produce estimates on the number of physical dwelling units in a camp. The results can be displayed using tools such as ArcGIS or Google Earth to provide geographic context. Teams can then add ground data about the average number of residents per dwelling, enabling them to estimate the size and distribution of the population. The process can be repeated to quantify population change and movement within a camp. The ability to repeat the population analysis quickly is important, since rapid changes can occur in a camp during a crisis.
Object-based image analysis (OBIA), a method available in eCognition, is used to identify and classify features in an image. According to Dr Dirk Tiede of Z_GIS, OBIA offers greater flexibility and efficiency than pixel-based analysis techniques. Tiede develops customised processes (known as ‘rule sets’) that effectively train eCognition to recognise and classify individual features within an image. The objective is to identify man-made structures and to differentiate dwellings from other camp buildings such as food stations and medical facilities.
The rule sets use edge detection algorithms to delineate camp margins and to classify man-made features. Spatial characteristics of different dwelling types, along with relative spectral differences between objects, enable the rule sets to distinguish light-coloured dwelling structures from darker buildings and fences. The system determines the spectral values in order to separate dwelling types as well as areas with and without vegetation. The comparison is based on specific spectral ranges or vegetation indices such as the Normalised Difference Vegetation Index (NDVI). These values are saved as variables within the rule set and can be combined with other independent spectral parameters. Once a rule set is constructed, it can be transferred and adapted to work in different camps, or at different times in the same camp when the environment has changed.
The initial rule set was developed using archived QuickBird imagery of the Zam Zam camp in Darfur, Sudan. To test transferability, the rule set was applied to a series of QuickBird images taken in the years prior to and following the original dataset. Additional tests used GeoEye-1 imagery at camps in Darfur. During these tests, Z_GIS found that the primary challenges came from varying vegetation at new sites as well as differences in characteristics of various satellite sensors.
Complex situations occur when camps show a very diverse set of structures or several development phases. In addition, local conditions may change the appearance of structures over time. For example, in some camps brightly coloured tents are used, which are relatively easily recognisable. However, when a sandstorm passes over and covers everything in a brownish dust, the given rule set may need to be adapted quickly. Therefore, a hybrid approach was applied that combines the automated solution with manual image interpretation for analysis and quality checking. The tests performed by Z_GIS showed that the rule sets could be adapted successfully using visual inspection on the computer screen and redefining appropriate spectral thresholds for structures and the NVDI for vegetation.
Effective population monitoring relies on rapid acquisition and processing of satellite data. Once imagery is acquired, the main bottleneck occurs in image processing. Using more efficient algorithms and faster computing technology (including the distributed computing ability of eCognition Server), it is possible to analyse an entire VHR satellite scene in just a few minutes.
To further reduce the time and staffing level required for analysis, Tiede and his colleagues are working to improve workflows around the automated processes. They are developing applications and solutions with the goal of producing initial data for a camp very quickly. From there, the team can build on the results to produce temporal information on population dynamics.
Satellite imagery supports NGOs in more than just population estimates. For example, organisations want to avoid the expensive and risky practice of trucking water to the camps. Using lower-resolution imagery from Sentinel or Landsat satellites, Z_GIS can develop an overview map of a camp’s geohydrological situation to identify possible well sites. Natural disaster aid efforts also benefit from satellite imagery in analysing the extent and nature of damage for first responders and longer-term recovery. After the devastating 2010 earthquake in Haiti, Z_GIS produced a damage map within two days of the event. In emergency situations, the turnaround time is critical. The urgency is usually not as great for refugee camps, but timeliness does matter for highly dynamic situations such as the Rohingya crisis in Bangladesh.
The humanitarian team at Z_GIS continues to work closely with MSF and other NGOs, including the Red Cross movement, SOS Children’s Villages and Action Against Hunger.
Their work has produced a stable, operational service to support humanitarian organisations. It is an illustrative example of the value of Earth observation and image analysis in humanitarian aid. Natural disasters and other crises will continue. Putting advanced tools in the hands of dedicated specialists helps to provide better and faster relief to those in need.
http://www.GIM-INTERNATIONAL.com Miércoles 09 de Mayo del 2018
When people think about landslides, they usually imagine large mud streams which cause considerable loss of life. Whereas such large-scale disasters are rare, smaller landslides are a much more frequent occurrence and pose a danger for traffic and housing in thousands of cities worldwide. In Italy, Google News is used to calibrate the 24-hour prediction models and slope-instability risk maps are produced every six days based on Sentinel-1 radar images. In fact, Prof Nicola Casagli from Florence University has high hopes for a geostationary European InSAR satellite which will enable the daily production of a map for assessing ground displacements of 1mm.
Can one predict with certainty where and when a landslide will occur? Prof Nicola Casagli, from Italy’s Florence University, has no doubt about the answer: “Landslides caused by earthquakes are impossible to predict, but all other types of landslides are predictable. You can forecast the time of failure even using simple models. You do need to have a continuous series of accurate monitoring data.” He adds, laughing: “But it is quite common to do the prediction after the event.” The art and science of predicting is never easy, but climate change is making things even more complicated nowadays. Casagli can handle it, though; his renowned Centre of Competence for geohydrological hazards is supported by 60 multi-disciplinary experts.
In the south of Europe, climate change is causing rising temperatures and a gradual reduction in the average yearly rainfall. Besides that, the precipitation increasingly occurs in more concentrated periods of heavy rainfall, triggering debris flows and shallow landslides – small, but very fast and dangerous. These landslides are increasingly being accompanied by flash floods: a large volume of water building up in one place in a very short space of time. This mixture of geological and hydrological phenomena is difficult to predict. Casagli: “You can monitor slow-moving, large landslides with satellites combined with ground based instrumentation for measurement of the exact displacements. Those are very easy to predict. For very fast landslides – debris flows, debris avalanches – triggered by intense rainfall, it is more difficult. The movement before failure is zero, but when heavy rain occurs they start to accelerate in seconds. The only way to predict them is to predict the rainfall.”
Besides the normal 24-hour weather forecast, data is also collected from rain gauges. These are in place throughout the whole country, with different defined thresholds per zone based on normal rainfall characteristics, bedrock lithology and landscape morphology. When a threshold is exceeded, the regional landslide early-warning system issues a moderate or high alert, spatially limited to the authorities of that zone. Of course, there must be certainty that those thresholds are correct so that no false alarms are raised or the alert fails to be issued when it should be. To ensure this, Casagli uses spatial data mining techniques. A semantic engine analyses Google News for reports of landslides that occurred in the previous week. By comparing the reported events with the rain-gauge data – all rainfall parameters (intensity, duration, amount, etc.) are stored in a database management system (DBMS) – the thresholds are kept up to date and the alerts provide trusted input for the landslide prediction model.
Two thousand landslides reported last year
Casagli explains the importance of Google News. “To predict landslides caused by precipitation, you need information on landslides as well as weather predictions and data from rain gauges. If I predict a landslide will occur tomorrow based upon precipitation, I subsequently need to know whether a landslide did actually occur on that day. We match the data with rainfall data and refine the prediction. The more data we have on landslides, the better our predictions become.”
The semantic engine analyses all the news reports about landslides (and about the 34 synonyms for ‘landslide’ that exist in the Italian language) that really occurred. Last year there were more than 2,000 such news items. In fact, this manner of calibrating landslide prediction models is a digital follow-up. In the 1980s, scientists in Italy started to compile large databases of newspaper data on floods and landslides, until the government cut all funding in 2002. But since 2010 the scientists have been using spatial data mining techniques. The semantic engine now analyses all the news in Google News and in the database of ANSA, the national press agency. Many reports of minor landslides go to local or regional newspapers rather than to ANSA, but they are all on Google News, as are most of the digital-only local newspapers.
Warnings every 24 hours
To actually predict landslides, all the data is input into several models that are used simultaneously. Experts within the National Civil Protection service and in the regions evaluate the outcomes every day and make a decision. That is then translated into a map showing expected landslides and floods, which is made publicly available on the www.protezionecivile.gov.it website.
On a nice sunny day in February, almost the whole of Italy is shown in green on the map with yellow indicating a low probability of landslides or floods in just a few places. “The national warning system gives warnings every 24 hours. That is enough for most problems, but not for all. Sometimes we would need ‘now-casting’ of heavy rainfall (as opposed to the normal 24-hour ‘forecasting’, Ed.), but hourly prediction is still in the research phase; the degree of uncertainty is still to high.”
Uncertainty also plays a role at the local level. Casagli is a member of the National Risk Commission and sees the struggle the mayors face whenever the map turns red or orange in their territory, because this means the mayor has to take action as a local authority of civil protection. Every municipality has a civil-protection action plan and should know what to do in the case of each colour. “Here in Tuscany the colour red occurs two or three times per year. Then, schools are closed, some offices are closed, houses near rivers are evacuated and firefighters get ready. But orange is a problem; it’s the mayor’s responsibility to decide what to do. You might have 10-12 orange alerts per year, and if you evacuate nine times without anything happening… So many mayors of larger cities waited too long and they are being charged with manslaughter. But it is not easy. In many cities, urban planning was non-existent during the time Italy became a richer country and we built whatever we liked. For instance, many rivers were put away under concrete and are now causing serious problems when floods occur. In our research group we now want to add an expert on risk communication.”
Sentinel-1 for monitoring
The Italian authorities are also provided, every six days, with scatter maps on the risk of slope instability, again based on applications developed and maintained by Casagli and his team. Sentinel-1 satellites belonging to the European Space Agency (ESA) have proved crucial. Casagli started using radar satellites to map and monitor landslides in the 1990s. With interferometry in all variations, the measurements of ground displacements over large areas can be extracted from the radar images. “Sentinel-1 is such a big step forward, it is like the change from a photograph to a movie.” With the previously available satellites, the revisiting time was at best 24 days. Now, Sentinel-1A and B revisit each part of the Earth every six days with the same line of sight – plus it is free. This opened up the possibility to use satellites for monitoring as well as mapping. Now, all the authorities can update their risk map with interferometric data. In Italy there are five levels of risk, from R0 (no risk) to R4 (extremely high risk). No building is permitted in the case of R3 or R4 codes. The district authorities decide on the exact boundaries. Changing a boundary or a level has a considerable effect on property value, so there is always great pressure from the owners. And of course, not all the authorities are happy to learn that they have to take action right away to solve a serious problem with a road or a building. But the satellite data provides quantitative information, and the pressure is reduced; it is not only based on an expert’s opinion. Casagli: “We are already doing completely new mapping every six days for two Italian regions. You can see on the scatter web maps which points are red, with significant ground displacements. Most of them correspond to R4 in urban areas.”
European stationary InSAR satellite
There still are limitations to the use of Sentinel-1. The main problem is the resolution of 14x5m. “That resolution is a problem when you want to build a warning system based on satellites only; you cannot detect all the relevant phenomena. We are now applying, with all the major radar-interferometry research institutions in Europe, for the third time to ESA for a geostationary radar satellite. Each day, one map image with an accuracy between 1 and 2mm will be composed out of all the interferograms. With one satellite it would be possible to map a circle with a 2,000km radius: more than half of Europe. We suggest a cooperation with ESA because they have good ground segmentation. By using that, we are certain to get an image of the same area every day. The application is ready. The satellite would cost €500 million. The same satellite can also be used for other applications such telecommunications and television. But in the European institutions, applications for smart cities, oceans, coastlines or desertification are more popular than natural hazards. It is a pity, because it is cheap and it can save lives. As a comparison: our four-satellite Italian system, COSMO-SkyMed, cost €800 million. The data is excellent (3x3m) but it is not free, and the standard revisiting time is 16 days.”
“Europe has been the leader in radar Earth observation since the 1990s; it had the vision to launch the satellites and to build an archive, which was crucial for new business to develop. But if ESA doesn’t take a step forward, Amazon or Tesla will take over its role and provide a new impulse. I would love to talk to Mr Musk. The information we would produce is crucial for traffic safety; if you drive in a self-driving car, you trust the authorities to keep the roads safe.”
http://www.GPSWORLD.com Miércoles 09 de Mayo del 2018
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