dataesr/scientific-paragraphs-categorization huggingface dataset:
Also, an attempt is made to obtain the name of the street for the current coordinate. The client end of the application analyses the received information and calculates parameters like time, speed, coordinates on GPS basis. User data are sent to the cloud server Parse. com for further storage and exported in the required format. The Parse. com library was used for processing data for Parse. com. The implementation of the client end is represented by a mobile software system for data collection. The system was developed for the Android platform using the Java programming language. The user interface of the system was developed next to the requirements set by Google for the interface of Android applications. The server end of the system is a web application used for • loading tracks from a file; • processing tracks in line to the Herman-Prigogine model; • visualising the obtained results with an overlay on the Google Maps cartographic service (Google Maps Javascript API); • clustering the sections of the street and road network in consonance to parameters for the Herman-Prigogine model.
Following the preliminary processing and storage in the cloud service, data are uploaded as a file for further analysis. The file in the CSV format is sent to the server end of the system that receives all tracks from the file at this stage, processes and verifies them. Then, the file in the CSV format containing tracks is sent to the input for analysis. After receiving all tracks for a particular street, the number of tracks, standing time and the average speed for this section of the road are determined. Every parameter is assigned a threshold value in advance and assists in determining whether a track belongs to the group of unreliable tracks. After passing verification, data are sent to the Herman-Prigogine module in calculating parameters. The following algorithm is used for implementing the Herman-Prigogine model.
All tracks for a particular street are selected from the database of heavy vehicles. Information on time and speed at each point in time-related to obtaining the location is extracted from them. Information on time allows calculating the total travel time and speed allows determining standing time. Thus, the two components necessary for the Herman-Prigogine model are obtained. Next to receiving data, linear regression is constructed using the total and motion time to solve equation y = kx + b. After solving linear regression, parameters k and b are calculated and used for estimating parameters for the Herman-Prigogine model. The data are transferred to the clustering module input, succeeding the calculation of parameters for the Herman-Prigogine model. The FOREL algorithm (Algoritmy semeystva FOREL) is used for clustering the obtained results.
The algorithm is based on the idea of combining objects into a single cluster in the areas of the highest concentration and has been chosen due to low demand for specifying the number of clusters in advance. Clustering the entire sample results in the application displaying the charts and routes on the map and then compiling a report in the TXT format. The implementation of the server end is represented by a unique system created for the web platform (jQuery API Documentation; Matplotlib; SQLite Documentation). Python was used for developing the primary programming language. The Flask framework was employed for displaying in the web environment. Cartographic service Google Maps v3 (Location and Sensors APIs) was applied in this system. The JavaScript programming language (jQuery API Documentation) was used for manipulating the map: constructing points and routes.
ORM SqlAlchemy and the SQLite database management system (SQLite Documentation) were utilised for work on the database. The use of this Database Management System (DBMS) is rational within this paper because of the mobility of deployment and maintenance. For developing the introduced system in the future, using a more powerful DBMS is required, as the increased amount of data leads to limitations to the existing solution. Thus, the hardware of the software package for assessing traffic flow parameters includes • a mobile device for collecting traffic information -a navigation module integrated into the vehicle or a mobile device with a GPS receiver (Android OS 4. 0 and higher) having the enabled function of data transfer; • software products of transportation companies providing navigation data in the CSV format; • the automated workstation (AWS) of the operator, including a personal computer running the Linux family in the operating system.
The advanced mathematical tools assist in finding solutions for the encountered problems of transport simulation and in assessing traffic flow parameters. 4. Thus, to successfully apply mathematical modelling methods of the transport system, recommendations for transport simulation and the programs optimising road network capacity need to be developed. 5. As part of this paper, a software system has been designed. It includes modules in collecting navigation data, storing and verifying track data, managing street and road data, calculating indicators for the Herman-Prigogine model and classifying streets in line to the parameters based on passive processing of navigation data on vehicle traffic on highways as well as on the streets and road networks of cities. 6. The main advantages and novelty of the developed system include: • cloud computing (large amounts of cloud storage data); • availability of a mobile client application for the Android OS for collecting data in addition to the ability to work on the navigation data already collected from the navigation and information centre or other sources; • support for the formats of modern Global Positioning System trackers; • use for calculating and processing data on modern web technologies; • the scalability and customizability of the system.
Hoxha et al. [6]  discussed on how to address data uncertainties through contribution and sensitivity analysis and draws attention to the implications for LCA. At the same time, Najjar et al. [38]  highlighted several gaps, such as incomplete databases or insufficient data at the early design phases that reduce the potential for LCA applications. Most studies present simplified hypotheses due to lack of incoming data, especially for EOL scenarios in which "only demolition and disposal to landfill sites will be considered"  [39]. Although most of the papers have focused on LCA methodology, some provide results allowing to give impact results, especially outside the most widespread climate change. Thus, according to Marzouk et al. [26]  study, "manufacturing and transportation off-site phase represents the highest weight of ozone-depleting particles with 92.
This score emphasizes criteria to facilitate deconstruction ability but does not guide designers with methodology. The residual performances of building elements are still insufficiently assessed and Akanbi et al. [45]  provide additional elements with a BIM-based whole-life performance estimator (BWPE), which takes into account the aging criteria of the materials to allow their reuse. A study addressed the design for deconstruction (DfD) design criteria  [46]  based on professionals' feedbacks and the need to implement Industry Foundation Classes (IFC) models with DfD-BIM software data. The IFC interchange format for the BIM model is approached by several studies  [47, 48]  to integrate data relating to sustainable development but experiences difficulties due to generated file errors during export and import  [49].
BIM and LCA Integration Several papers develop the relation between BIM, LODs and LCA  [53, 54]  and ditto for 6D BIM (covering environmental data relating to sustainable development)  [52]  with repercussions on the way to structure Life Cycle Inventory (LCI) and to use LCA datasets. By understanding the limitations of these BIM data streams to perform an LCA, we can divide current practices into two main parts. On the one hand, there is geometrical related information extracted from the model (bill of quantities, type of material) which induces a specialized LCA software secondly, and on the other hand, specific LCA data contained directly in the model with a direct LCA plugin in a BIM software  [37]. Workflows enabling BIM to export input data for LCA software are linking BIM and LCA. It is recognized that the BIM-LCA connection is not yet optimal  [55].
The literature highlights the complexity to manage between precise data requirements and simplified estimations needed in early stages  [9, 25, 56]. Some studies try to address these issues providing methods as a first approach to obtain LCA results with few data  [25]. Scarce papers have worked to link 6D BIM and LCA and to refine the environmental data attached to BIM objects to facilitate the extraction of data feeding the LCA software  [55]. 6D BIM can help establish the framework to guide the practitioner to fill in correctly the digital model and to save time when performing the LCA. However, most BIM-based LCA methods papers do not precise the LOD for the LCA  [25, 56]. To achieve reuse, we should provide and integrate necessary BIM-attached information to simulate the assembly and disassembly process, such as a deconstruction plan  [57] , material properties enabling LCA performing  [38].
Indeed, LCA requires rigor and comprehensive data obtained at an advanced stage of the project, whereas engineers need a simplified LCA in the early stages when we can still change the design choices influencing the environmental impact with a low LOD in the BIM model. This difference of accuracy may affect the results obtained with often higher quantities overestimated in early stages (low LOD 200) and a general reduction of impacts obtained with refined quantities in the advanced phase of the project (higher LOD 350 and above),  [14, 56]. It is preferable to carry out comparative LCA for a given LOD and phase, as the final result is questionable. Three main strategies have been developed in the literature: perform an LCA from a dedicated software (enter data), from a BIM software (integrated LCA plugin, automatic data extraction), from an external facilitator as a web platform (automatic extraction of BIM data and automatic import of LCA data)  [58].
Material Bank, from the Project Database to the Big Data Once the BIM model has been correctly established and filled in, we must capitalize data it contains on a database. This database makes it possible to build a vast catalog of elements constituting a building that could be made available for future reuse. Mainly unique recent research explored the establishment of a material bank  [18]  to stock material properties; several papers present the interest of such a development  [48]  to achieve reuse. In an existing old structure context, a material or component bank could manage more effectively their direct reuse  [18]. Brütting et al. [4]  also claimed that in the future, effective collaboration between element stocks, databases, and a market for reused elements would facilitate the reuse process.
Cai et al. [18]  recommends that the bank could help to store and to sell materials and components in a factory or center shop of the bank, and certification of materials and components. Urban mining and building stock can feed a generalized database with the help of drones, photogrammetry, laser scanning and greatly facilitate designers' access to a wide choice of reusable materials. We propose in this paper a framework for building up this material bank for the reuse of load-bearing elements. Concurrently, the problem of the impact of the massive datasets produced via these material banks and during the service-life appears. More and more complex BIM models will require higher information storage capacity as we generate huge amounts of data, especially as the monitoring of components is set up  [30].
Traceability, Monitoring and Predictive Approach One of the shortcomings noticed to facilitate reuse is the need for reliable traceability. The BIM model can inform and store information. Furthermore, the digital model is not sufficient to ensure the sustainability and integrity of the data and must be coupled with physical traceability devices. One of them is the Radio Frequency Identification (RFID), allowing tracking and archiving the properties of structural construction components  [62]. RFID chips can be incorporated inside the components or attached to them  [18]. Quick originality codes or bar codes pasted on the surface of the elements  [18]  are also traceability solutions. Thus, RFID-BIM technology and potential application is still a niche  [62]. Yet, incorporating sensors' data into the BIM model should contribute to this innovative field  [10, 28] , as it could feature GPS and localization ability  [62].
Finally, the literature addresses the construction sector impacts with a desire to reduce the CDW in current demolitions, but only a few studies explore anticipating reuse in buildings designed today. Several design methodologies for reuse are based on principles of easy disassembly, but there are still difficulties in characterizing residual performance, responsibilities and insurance, phasing of deconstruction and structural reassembly. The article we presented helps to overcome these difficulties and provides useful guidelines for practitioners. The BIM is particularly enthusiastic support for compiling and sustaining the expected data for reuse and is a pillar of the methodology we propose. All of this data must then be usable for performing an LCA. Current LCA methods do not take into account all the benefits of reuse (such as the multiplication of use cycles) and need to be improved to scientifically assess the different impacts, benefits or disadvantages, of the reuse process on the environment.
The Need for Data Traceability and Engineer Responsibility The many current rehabilitation projects require recognition of the existing structure. This current method of recognition can be integrated into the method to anticipate reuse in 30, 50, or more years. Thus, the data of the load-bearing elements must be tracked during the design phase. For the reuse of load-bearing components, the physical and mechanical properties of the materials must be known at least. These data to be collected must be specified according to the type of material: concrete, steel, timber, and according to the structural function: column, beam, load-bearing wall, transverse wall, slab. In some cases, the complexity of the structure to be dismantled or the future class of use of the load-bearing elements requires additional studies by structural engineers.
These data are already collected as part of structural health monitoring (SHM) and are adaptable for reuse. The SHM allows the updating of information on the aging of elements. The SHM can be directly coupled to the initial calculation model to update the consequences of events experienced by the structure. Finally, active traceability is, therefore, better suited to data collection to monitor the overall behavior of the (mechanical) structure. • To assess the risk of reusing a structural element and extending its useful life for a "new life", another level of response can be provided via the principle of predictive analysis. The statistical processing of numerical data recorded during the first life must allow reusing a structural element with an understanding of its behavior far superior to that provided by traditional analysis processes.
In the case of an existing structure, a predictive analysis, based on the evaluation and statistical processing of data measured in situ, makes it possible to establish correlations between the data, for example between observed displacements and applied actions and makes it possible to predict the structure's subsequent behavior. We can base the future behavior on knowledge of the actual behavior and the increasingly fine-tuning of the predictive model according to the data measured in response to climatic or other events. The data recorded by the sensors will be analyzed and interpreted to provide a real-time dashboard of the building's mechanical behavior. A precise orchestration allows to: • Store this data according to predefined data architecture and in a secure storage area, • Define the correlations sought between the data collected and build the processes to establish these correlations (data science), • Implement a "Machine Learning" type process to learn and control the behavior of the work overtime in an increasingly fine-tuned manner,.
A LOD/LOI 700 could consist of the data expected to reuse at the EOL and must, therefore, be anticipated from the design phases, then be checked during execution and then be resumed at the EOL, while having the trace of events occurring during maintenance. To this end, we implement the four categories of information described above, integrating previous research into the literature  [5, 17]. For the time being, the 6D BIM information is not complete in the proper description of the LODs, LOI. Therefore, this article identifies the need to specify new LOI 600 and 700. Today, the development of expectations in terms of the information contained in the BIM model is such that we talk about LOX as there can be so many levels of detail  [64]. The LOIN (Level of Information Need) We propose two new definitions of LOD herein to specify the details that the model must include for long-term reuse.
In parallel, LOI (the level of information) points out the attributes expected. We can consider that the LOD/LOI 600 corresponds to the information added during the service, and we will not develop this point in this paper, except to anticipate reuse. A LOD/LOI 700 could consist of the data expected to reuse at the EOL and must, therefore, be anticipated from the design phases, then be checked during execution and then be resumed at the EOL, while having the trace of events occurring during maintenance. To this end, we implement the four categories of information described above, integrating previous research into the literature  [5, 17]. For the time being, the 6D BIM information is not complete in the proper description of the LODs, LOI. Therefore, this article identifies the need to specify new LOI 600 and 700.
Bank of Available Materials and Just-In-Time Circular Flow The case study described in this article sets up a materials bank in the form of a database of reuse items. If we start feeding a brand new database from the buildings constructed today, the process described in this article will not be operational for a long time, since these new buildings will have to reach the end of their lives to generate a database with elements available for reuse. It is, therefore, essential to feed this database with data from existing buildings, which means that massive digitization would be necessary. It should be noted that entire cities are currently being digitized, such as Singapore  [67]. In the same way, we can, therefore, hope that the digitization of carrier systems will be possible. A materials bank is essential to generalize reuse and ensure the availability of elements ready for reuse.
Bank of Available Materials and just-in-Time Circular Flow The case study described in this article sets up a materials bank in the form of a database of reuse items. If we start feeding a brand new database from the buildings constructed today, the process described in this article will not be operational for a long time, since these new buildings will have to reach the end of their lives to generate a database with elements available for reuse. It is, therefore, essential to feed this database with data from existing buildings, which means that massive digitization would be necessary. It should be noted that entire cities are currently being digitized, such as Singapore  [67]. In the same way, we can, therefore, hope that the digitization of carrier systems will be possible. A materials bank is essential to generalize reuse and ensure the availability of elements ready for reuse.
The materials bank concerns both existing buildings to deconstruct and future buildings to build that may incorporate reusable materials. As defined in Figure  3 , we developed tools to enable the materials bank to be set up in the form of a database, while at the same time facilitating the implementation of LCA. When the existing building, whose elements are in this database, is to be deconstructed, the elements appear free for reuse in a new project. Structural engineers create a calculation model when designing new projects. From this calculation model, engineers can then send queries to the database to find a load-bearing element that could perform a new function once reused. This database system will operate on a just-in-time basis to avoid storage problems, especially in areas of the territory where the available space is limited, such as metropolises.
New data is added each time a new building is constructed and its elements modeled. However, as demonstrated in the literature, the environmental impact of digital data is not negligible. Moreover, the more data a model contains, the less fluid it will be when opening, managing, and modifying it. This is why the amount of data must be limited to relevant data and optimized. This present article deals with the structural data necessary for the structural engineer to engage his responsibility in case of reuse of the elements. Reducing the amount of data will, among other things, make it possible to save both calculation and database search time, as well as energy on the servers. For this research, we broke down the parameters produced in the BIM model by materials and phase according to the different stages of the project.
The information is related to the objects in the BIM model. The approach involves exporting, at each end of the phase, these parameters on the database. We developed a gateway between the BIM model and the database, and then the software for calculating reused structures. The database described in this article makes it possible to carry out the necessary structural When the existing building, whose elements are in this database, is to be deconstructed, the elements appear free for reuse in a new project. Structural engineers create a calculation model when designing new projects. From this calculation model, engineers can then send queries to the database to find a load-bearing element that could perform a new function once reused. This database system will operate on a just-in-time basis to avoid storage problems, especially in areas of the territory where the available space is limited, such as metropolises.
The information is related to the objects in the BIM model. The approach involves exporting, at each end of the phase, these parameters on the database. We developed a gateway between the BIM model and the database, and then the software for calculating reused structures. The database described in this article makes it possible to carry out the necessary structural calculations, thanks to real-time queries. Depending on the stage of the project, queries are more or less complex, according to the stress field applied to the considered element. For a column submitted to compression, for example, the first issue may be to find a column in the database that enables to bear the sufficient load. For a beam, the request becomes more complicated since, in addition to an admissible load, it is necessary to be able to select an element according to the allowable deflection.
In that case, we must add supplementary information on the ultimate bending moment and sufficient inertia. For this reason, the study presented focused on column dimensions and capacities but ruled out the case of compound bending, which may give rise to future developments. Next, we must thoroughly check the compatibility of the existing reinforcement drawing with the new use. This database query process is iterative as a series of hypothetical uncertainties need to be examined. If the modeling of the new structure requires the use of a 50 × 50 cm section beam and, in the database, the beam best meeting the stresses of the model has a section of 60 × 60 cm, the structure's overall weight will be modified.