Open access peer-reviewed chapter - ONLINE FIRST
Abstract
Building information modelling for infrastructure (I-BIM) is used for creating and managing data during the design, construction, and operations process of roads, railways, and airports. I-BIM integrates multi-disciplinary data to create detailed digital representations that are managed in an open cloud platform for real-time collaboration. The newly founded university spin-off VIASTRATA aims to develop digital information management methods and tools for the design and construction of roads, railways, airports and hydraulic works. Its scope also includes interventions on existing constructions and ultimately aims to the creation and products with high technological value and innovative BIM services. The chapter covers structured and innovative methodologies for designing, modelling and managing transport and hydraulic infrastructures leveraging BIM. To this end, the operation of an algorithm developed in visual programming language that realises the parametric model of a road dynamically from data contained in a spreadsheet is described.
Keywords
- I-BIM
- computational design
- asset management
- interoperability
- VIASTRATA spin-off
1. Introduction
In the broad context of smart infrastructures, there are several technologies driving the digital revolution in the architecture engineering construction operation (AECO) field, such as building information modelling (BIM) and geographic information system (GIS). BIM for transport infrastructure is based on the realisation of 3D geometric models enriched with additional dimensions of information: 4D-time (scheduling), 5D-cost (economic project management), 6D-sustainability (environmental focus) and 7D-FM (facility management). Three further dimensions are now being discussed: 8D safety (on site and in operation), 9D lean construction (resource optimisation) and 10D industrialisation (process staging and optimisation) [1].
GIS is a cartography and spatial planning tool that allows different types of analysis depending on the data contained in intelligent geographical maps in SHP (Shapefile) format.
GIS and BIM can be integrated in a methodology that can be defined as geo-BIM, based on the use of geometric/geographic survey technologies, such as light detection and ranging or laser imaging detection and ranging (LIDAR), which records the position of millions of points in few seconds and 360° in space using laser beams, and unmanned aerial vehicle (UAV) drones that can carry both LIDAR and ultra-high-definition cameras whose photographic data can be processed into point clouds using the technique of photogrammetry. Geo-BIM applications are very popular for several scopes, including data survey for new infrastructure planning, as well as reverse-engineering workflows, heritage and monitoring [2, 3, 4, 5, 6, 7, 8].
Tools, such as GIS and BIM, open to many possibilities in terms of efficiency production enhancement, under every aspect. In fact, these digital tools are proven to improve planning, design and building operations coordination, leading to resource optimisation and saving time and money [9].
BIM is a methodology that positively affects all phases composing the lifecycle of the asset. To do so, it is very important that the information flow is continuous and consistent within planning, design, construction and facility management. For this reason, it is very important to define a common data format being a standard for interoperability. That is IFC, standing for industry foundation classes, encoding the description of knowledge, data and shapes, of a building information model [10].
At the core of BIM, there is the concept of collaboration enabled by a central intelligent digital model shared on a cloud platform with all the designers, professionals and other stakeholders involved in the project.
This approach enables to explore and validate different design ideas and what-if scenarios: for instance, in 2021, the implementation of cost-benefit analysis has been integrated into a BIM workflow to prove the visual impact on the design alternatives of a high-speed rail in southern Italy [11].
Another core principle of BIM is that the model is based on parameters. By changing the value assumed by a certain parameter, it is possible to modify a geometric feature of the model such as the width of a road lane, the pitch between the piers of a bridge, the distance between two successive lampposts, etc.
Furthermore, another enormous advantage of modelling in BIM is the possibility of correlating all the elements of the model in such a way that by changing one of them, the others adapt [12].
The BIM model, therefore, is not only a geometric representation of an infrastructure asset, but is above all an intelligent object, capable of adapting based on rules and parameters, of complying with regulatory constraints, of offering design alternatives by constituting a useful decision-support tool, of providing precise data on quantities and materials, providing a real accounting contribution, and much more [13].
Many academic publications focus on how the use of BIM can positively affect road facilities and infrastructures for their entire service life.
Researchers have been concentrating their efforts in the past few years on evaluating the advantages of employing digital tools and procedures to support road infrastructures and transport facilities throughout their whole life from performance management and maintenance to strategic planning, design and construction [13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25].
Facility management is a natural vocation for BIM as a data management tool to maximise the results of companies under budget and time constraints [26].
A digital information model can also provide a basis for complex studies and simulations in a variety of scientific fields: interaction of infrastructure with winds, noise exposure, lighting engineering, energy, environmental and social impacts and structural analyses of modelled elements [27, 28, 29, 30, 31, 32].
In addition, semi-automated workflows based on the use of algorithms that are not written textually but through visual programming languages (VPL) are in full development in these years [33].
This very direction offers very interesting prospects in terms of methodology development. In fact, the adoption of BIM in business practices is an expensive and time-consuming step, considering the need to update staff, purchase materials, software, etc. The economic impact of this methodology, already non-negligible, can further emerge by benefiting from process automation techniques. In this way, BIM could be more user-friendly and even more effective, in terms of speed and accuracy.
This study provides an overview of a generic BIM workflow, enriched by the algorithms proposed by VIASTRATA, an academic spin-off affiliated with the University of Naples Federico II.
2. BIM workflow theoretical background
In recent years, several possible workflows have emerged to produce information models of linear infrastructures such as roads, railways, airport runways and hydraulic pipelines. In the following, the framework of the methodological approach to be used for the effective application of BIM to the design of linear works will be described.
A BIM workflow is based on the use of a multiplicity of software. Firstly, one must be able to process the terrain survey data. There is a wide range of software dedicated to the processing of topographic data. Next, the work shifts to what is called BIM-authoring software. In this phase, the infrastructure is modelled in its main components: planimetric axis, vertical profile, cross section, 3D solid, and information. BIM-authoring software exports the final file in various formats, also in IFC, the most important standard for BIM interoperability. There are several software packages, called BIM viewers, that allow the exploration of the digital information model but not its editing. They are, therefore, very lightweight software, mainly used for verification.
A core concept of BIM is transparency and sharing the project with all participants. There are several cloud-based platforms that allow the creation and management of a Common Data Environment (CDE), that is, an environment in which several sectoral models (architectural, structural, plant, etc.) are associated into the so-called ‘federated’ model. In an ideal workflow, sharing in the cloud of the project should be transversal to all work phases.
To sum it up, the work phases are:
Preliminary activities as analysis of the state of the sites and parameters set definition;
Digital terrain model (DTM);
Planimetric layout;
Vertical profile;
Parametric section;
3D solid model;
Information management.
2.1 Digital terrain model
The surface that covers the terrain, without any object on it, whether natural or artificial, is called digital terrain model. The DTM is fundamental because it constitutes the starting point for the design of the horizontal and vertical components that once coordinated constitute the three-dimensional axis of the infrastructure.
It serves as the foundational framework for computational aspects, concerning elevation or slope computations, encompassing profiles, cross sections, grading and volume calculations. The DTM process entails the construction of a data structure accessible by software to promptly retrieve elevations or slopes, depicting extant or proposed conditions.
While alternative data forms can generate surfaces, three principal data types contribute to DTM construction: point data, breakline data and contour data. Point data for DTM comprise isolated X, Y and Z coordinates devoid of interconnecting features, typically representing spot elevations in contour drawings or the mass points themselves in mass points and breaklines drawings. Notably, point data necessitate an elevation or Z component amenable to processing for elevation model construction.
Breakline data delineate linear edges of site features where conspicuous grade changes occur. Effectively employed, breaklines induce contour deflections, exemplified by pavement edges, shoulders, slope tops, wall tops and water features.
Contour data constitute interconnected strings of point data within intricate objects, represented in computer-aided design (CAD) as polylines. These polylines must possess accurate Z values, either as a constant 2D polyline or a variable 3D polyline.
Most applications in civil engineering and surveying amalgamate diverse data types to formulate a comprehensive terrain model. Triangular irregular networks (TINs) emerge as a representation of a continuous surface comprising triangular facets, primarily employed as a discrete global grid for primary elevation modelling.
TINs can be generated using three categories of vector information: altitude measurements (mass points), surface continuity breaklines and surface continuity break polygons (polygon surfaces). These points encompass X, Y coordinates and Z values, serving to establish connections with the two nearest points for triangle creation. The triangulation process is underpinned by the Delaunay algorithm, ensuring that no points reside within the circumferential boundary of a triangle.
2.2 Horizontal alignment
Based on a georeferenced DTM, it is possible to start modelling the infrastructure. The planimetric layout containing information on straight roads, fixed curves and variable curves forms the fundamental basis for joining two points on the territory. Since the DTM has elevations, it will be possible to associate a vertical profile with the alignment and finish the 3D modelling with the extrusion of the cross section along the coordinate axis.
The creation of alignments encompasses various methodologies, including the generation from polylines, pipe networks and LandXML data. Alignments are fashioned through fixed, floating and free elements. Fixed elements, though seemingly static, can adapt based on dependencies on other elements, thereby maintaining a degree of flexibility. Floating elements possess one indeterminate attribute, while free elements lack constraints and derive definition from adjoining elements. The choice of element type is contingent upon design context and available data such as through points, lengths or radii. Criteria-based design features can be employed during alignment creation to ensure adherence to local standards and facilitate the identification and reporting of any violations.
Thus, alignment can be created from other objects such as polylines, reference, pipe network, LandXML file, dgn, or directly as an alignment object with the drawing tools of the chosen software. You can proceed either by initially imposing tangents and only later adding curves as an alignment edit operation, or you can proceed by drawing tangents and curves sequentially. A core feature of BIM is that geometric models are parametric, thus driven by the values that parameters take. Therefore, it is possible to edit the elements that constitute the alignment by numerically modifying the parameters, or one can use a point-and-click approach by using grip points with immediate visual feedback of the geometry. The last operations are related to the addition of labels, for example, for progressives, the dimension of significant points, radii of curves, the length of straight lines.
2.3 Vertical profile
Profiles facilitate the examination of elevation changes along a horizontal alignment, offering insights into the topographical variations.
In much the same way of horizontal alignments, the vertical profile also consists of tangents and curves, of fixed, floating and free elements. In the same graph, it will be possible to compare a multiplicity of profiles, usually the terrain profile and the design profile (Figure 1). Beyond the central profile, offset profiles can be established for specific features, such as waterways or ditch banks. Profile views enable the overlay of alternative horizontal alignment profiles within the same region. It is imperative for the horizontal and vertical alignments to precisely match in length for the accurate creation of the corridor.
Figure 1.
DTM, road alignment and its vertical profile.
2.4 Parametric cross section
3D modelling of linear infrastructure is achieved by extruding a plane figure along an axis. It happens in the same way for roads, bridges, railways, water pipes, sewers, etc. The modelling of the cross section takes place on a plane whose intersection with the extrusion axis is the origin of the cross-sectional diagram. One or more baselines are often associated with this point. Each software is provided with a library of objects that the user can select and drag into the diagram to compose the cross section.
Although these default libraries are well-stocked, and there are libraries dedicated to modelling objects according to local standards; in a normal workflow, there will almost always be a need to customise the cross sections by creating some component from scratch (especially in reverse-engineering operations, where the built heritage is often not very standardised). Almost, all BIM software provides the functionality to model the objects to be assembled in the cross section.
The objects constituting the individual cross-sectional components are parametric, which means that the user can modify their width, thickness, radii, distances between points, slopes, etc.
In addition, certain parameters allow to create more advanced objects. For example, targets can be associated so that certain points of the cross section can follow the course of other objects in the model, such as the DTM or breaklines.
It is also possible to programme the objects in a way that based on specific rules, they will apply to the cross section when specified conditions at a given station are met.
2.5 Corridor solid 3D geometry
The corridor is the 3D geometry representing the space occupied by the planned linear infrastructure.
At the same time as creating the corridor, it is possible to proceed with the calculation of superelevation, verification of regulations, calculation of excavation and backfill volumes, creation of print layouts and data tables, etc.
Many software packages also provide water runoff analysis tools and integrate functionality for soil analysis.
The result of the workflow is, on the one hand, the production of the project drawings ready for printing, and on the other hand, the model in IFC format ready to be shared with all project stakeholders (Figure 2).
Figure 2.
Road corridor and solid representation in IFC.
3. VIASTRATA university spin-off
Research on digital tools at the service of the innovative transformation of transport infrastructure engineering is in full swing. Technology transfer from the academic to the industrial and professional environment is a very important mission and a driver of scientific and economic development.
The VIASTRATA academic spin-off (Figure 3), affiliated with the University of Naples Federico II, has the mission to produce high-tech products and BIM services for digital construction information management and the design and construction of roads, railways, airports and hydraulic works, for new construction and for interventions on existing buildings. VIASTRATA aims to be a leader in technological innovation, introducing digital solutions that optimise the efficiency, sustainability and quality of construction worldwide [34].
Figure 3.
VIASTRATA® logo.
The co-founders are:
Gianluca Dell’Acqua, Full Professor in Roads, Railways, and Airports and VIASTRATA Product Innovation Manager;
Salvatore Antonio Biancardo, Assistant Professor in Roads, Railways, and Airports and VIASTRATA Research and Development Manager for Transportation;
Mattia Intignano, PhD Candidate and VIASTRATA Information Technology Manager;
Francesco De Paola, Associate Professor in Hydraulics and VIASTRATA Research and Development Manager for Hydraulics;
Gerardo Attianese, Accountant and VIASTRATA Chief Executive Officer.
4. Road-BIM automatic modelling tool
The workflow described in the previous paragraphs is a proven state-of-the-art workflow to produce information models for linear infrastructures. An experienced user, depending on the complexity of the project, can still take days to complete all tasks. The proposal put forward by VIASTRATA is to speed up the entire process by using algorithms that automate individual tasks by executing them in succession and within seconds.
This is made possible by algorithms developed in a visual programming language environment (VPLE). The leading commercial software in the BIM industry provides platforms through which the average user can implement their own algorithms. The advantage of using the VPL is that no programming experience is needed to develop the algorithms. Obviously, the more complex the algorithm, the more time the developer will have to devote to it, but the more general and robust the algorithm, the wider the range of operations it can automate, and the greater the savings in time, and therefore money.
The example shown in this study is based on the use of Dynamo, an extension of Civil 3D, from Autodesk (Figure 4).
Figure 4.
Algorithm for automatic modelling of a road infrastructure.
Figure 4 shows the diagram behind the algorithm developed by VIASTRATA for modelling a road in all its parts from the data contained in a spreadsheet. The algorithm extracts all the necessary data from an excel file, read in either CSV or XLS format, and reorganises it into editable lists.
The spreadsheet is organised in such a way that it contains data on topography, alignment, vertical profile and parametric cross-sectional geometry. These are necessary for the completion of the geometric modelling. Further, data can be entered for semantic enrichment of the model such as those relating to maintenance planning, quality, characteristics and origin, of materials, costs, structural data, laboratory test results.
The topography is summarised in a cell that provides the reference system and in a list of coordinate points with latitude, longitude and elevation, provided in separate columns.
The algorithm can read the code relating to the geographical reference system and assign it to the Civil 3D project sheet. It then draws all points according to the assigned coordinates and finally joins them by triangulation according to Delaunay.
The triangulated irregular network (TIN) surface resulting from these operations is the DTM of the BIM model. Thus, sequentially, the algorithm reads the coordinates of the points that mark the path of the alignment and the vertical profile.
Given a sequence of georeferenced points, it is possible to create a 3D polyline, resulting in a tangent break. At this point, fillet curves are defined between the tangents by specifying radii and/or length. Secondly, spirals can also be added if provided. The approach changes if the reference points are not the vertices of the project plan elements, that is, the tangents, but are surveyed points belonging to an existing road. In this second case, it is a matter of reverse engineering and making an as-built model. In this situation, if the design data are not available, one can proceed by interpolation of the points using a Non-Uniform Rational B-Spline (NURBS) curve. NURBS curves can either pass through the points entered by the user, which are called end points and are, therefore, referred to as NURBS EP or interpolate the distances between points, which are called control vertices, therefore approach the points without passing through them.
The algorithm presented can perform both procedures, depending on the input given by the user.
Once the alignment and vertical profile are defined, the algorithm creates the ‘corridor’ element and derives its founding elements such as baselines and regions, that is, parts of the corridor that are defined by precise chainage intervals and characterised by having the same parametric cross section, the same baselines, and generally the same representation rules.
At this point, the geometry data are read and used to create a proxy representation of the cross section within the Dynamo viewer. This geometry is then vectorised in C3D and extruded along the corridor baselines with the eventual targets assigned. This produces the 3D geometry of the road in all its components. From this geometry, the algorithm extracts ‘solid’ objects to which it can associate ‘property sets,’ that is lists of properties that can take different formats (textual, numerical, formulas, images, etc.) and that constitute an enrichment to the semantic content of the digital information model.
Although Dynamo’s default node library is well-stocked with functions and instructions, in some steps, such as creating property sets from scratch and assigning attributes to geometries, it was necessary to refer to libraries published by experienced users and the creation of custom nodes. This is possible using programming codes, such as Python and C#. The main challenge is related to the complexity of the Civil 3D API (Application Programming Interface) with the difficulty of having to navigate through hundreds of possible functions, instructions, constructors and methods.
5. Hydraulic-BIM modelling workflow
First, the design of hydraulic infrastructure in the BIM environment is evaluated for the authoring software Infraworks; it integrates BIM and GIS methodology, generating a workflow for moving data from one system to another without issues. This software tool allows for achieving almost definitive preliminary-level projects. The resulting files are interoperable with several software tools produced by the same manufacturer without data loss. A software tool that integrates GIS data from the manufacturer Esri with BIM data from the Autodesk manufacturer has a high potential. The knowledge of the natural and built environments of the context where to carry out a civil infrastructure project allows for timely and more informed decision-making, greater stakeholder involvement and faster approval processes. The second step is sizing the pipeline network; the third is verifying and controlling the pressure network. Both phases have been simplified by an innovative procedure within the I-BIM practice applied to the hydraulic infrastructures of the network under pressure; in particular, it has been combined with the software that allows the data population of the pipelines, a specific hydraulic calculation algorithm for network planning, verification and management. This tool procedure has been implemented because the Civil 3D software is not equipped with an integrated pressure network solver and requires other tools, usually available with additional payments. It was preferred to combine the Civil 3D software only with free tools, widely known in the sector; therefore, Civil 3D was integrated with EPANET and EpaCAD. Figure 5 shows an innovative procedure for pressure water distribution networks. AutoCAD Civil 3D allows managing the horizontal-vertical and infrastructural information of an urban area. It allows automatically generating the 3D surfaces to represent the elevation profile of linear infrastructures. More specifically, using the 3DPOLY command, a closed polyline representing the water network (in this specific case study, two loops) can be drawn. In the 3D model, the network nodes are characterised by heights, defined by choosing the base surface of the vector cartography as the zero level. The preliminary network can be transformed using open-source software, such as EpaCAD (2009), to generate files with the .inp extension, readable through EPANET software. The .inp file can be edited by using HS network optimiser models. The objective function is minimised, accounting for costs and installation while respecting hydraulic and network constraints. The software tool is based on the Harmony search optimisation algorithm and interacts with EPANET to calculate pressures and flows in the water network. Finally, through AutoCAD Civil 3D, the final project of the water network is obtained by defining first the horizontal and then the vertical layout.
Figure 5.
Algorithm for automatic modelling of a pipeline network.
6. Conclusions
This research focuses on how design development and modelling of linear infrastructures can be carried out in a BIM environment. In particular, starting from the state of the art on BIM, a generally valid and proven workflow for the realisation of models with advanced software was described. The possibility of automating some if not all of the operations required for the realisation of a road BIM is a strong innovative element. The workflow described at the outset, while drastically reducing processing times compared to more traditional methods, is still time-consuming. The algorithm proposed by VIASTRATA reduces processing time to a few seconds, instead shifting the effort and commitment of professionals to the proper compilation of a specially designed spreadsheet. In fact, the main result of this work is precisely that of having found a methodology capable of drastically reducing modelling time in relation to a well-established framework for processing infrastructure BIM models. This opens up the possibility of expanding this approach to all transportation-related works and beyond. The algorithm was developed specifically for road infrastructure, but the principle is applicable to all linear infrastructure (pipelines, sewers, oil pipelines, railways, airport runways, etc.). In fact, with a few appropriate modifications, the level of generalisation of the algorithm would be such that it could be applied to all fields of linear infrastructure.
Research in the area of digitisation of infrastructure projects is in full swing. The need to adopt methodological approaches based on advanced digital technologies, such as BIM, is also supported by policy decisions, resulting from several years of experimentation and data collection regarding their effectiveness in technical-economic terms.
The potential of these tools is such that they can transform the way infrastructure projects are conceived and implemented. Future research directions based on the evolution of the algorithm presented in this chapter could involve integrating it with artificial intelligence (AI) systems for the evolution of generative modelling processes. In fact, the algorithm produces the model of a road infrastructure based on a rigid pattern of input data, while with AI it could autonomously learn how to respond to a wide variety of design needs.
Therefore, it is of paramount importance to continue exploring the possibilities offered by BIM and to increase its performance. Nevertheless, the algorithm developed in VPL described in this paper represents a possible application of a very important tool that has all the potential to expand the frontiers of BIM as a scientific methodology and as a technical-economic tool for the recovery of the construction and linear infrastructure sector.
References
- 1.
Borrmann A, Konig M, Kock C, Beetz J. Building Information Modeling, Technology Foundations and Industry Practice. Cham: Springer; 2018. p. 584. DOI: 10.1007/978-3-319-92862-3 - 2.
Fröhlich C, Mettenleite M. Terrestrial laser scanning—New perspectives in 3D surveying. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. 2024; 36 :W2 - 3.
Stal C, Verbeurgt J, De Sloover L, De Wulf A. Assessment of handheld mobile terrestrial laser scanning for estimating tree parameters. Journal of Forestry Research. 2021; 32 :1503-1513. DOI: 10.1007/s11676-020-01214-7 - 4.
Achille C, Adami A, Chiarini S, Cremonesi S, Francesco F, Fregonese L, et al. UAV-based photogrammetry and integrated technologies for architectural applications—methodological strategies for the after-quake survey of vertical structures in Mantua (Italy). Sensors. 2015; 2015 (15):15520-15539 - 5.
Banfi F, Brumana R, Stanga C. Extended reality and informative models for the architectural heritage: From scan-to-BIM process to virtual augmented reality. Virtual Archaeology Review. 2019; 10 :14-30. DOI: 10.4995/var.2019.11923 - 6.
Brumana R, Tucci G, Lerma Garcia JL. Special section preface: Informative models and systems for virtual museums. Virtual Archaeology Review. 2019; 10 :ii-iii. DOI: 10.4995/var.2019.12357 - 7.
Baik A. From point cloud to Jeddah heritage BIM Nasit historical house – Case study. Digital Applications in Archaeology and Cultural Heritage. 2017; 4 :1-18. DOI: 10.1016/j.daach.2017.02.001 - 8.
Intignano M, Biancardo SA, Oreto C, Viscione N, Veropalumbo R, Russo F, et al. Stone paved road digital reproduction: A workflow. In: TRANSBALTICA XII: Transportation Science and Technology, Lecture Notes in Intelligent Transportation and Infrastructure. Cham: Springer; 2022. pp. 131-139. DOI: 10.1007/978-3-030-94774-3_13 - 9.
Bilov V, Goi V, Mamonov K, Tregub O, Levchenko O. Advantages of building information modeling (bim) during the operational life. Amazonia Investiga. 2023; 12 (68):346-363 - 10.
Moon HS, Kim CY, Cho GH, Moon JS, Ju KB. Interoperability verification using BIM case models of road project. Journal of KIBIM. 2015; 5 (1):44-53 - 11.
Biancardo SA, Gesualdi M, Savastano D, Intignano M, Henke I, Pagliara F. An innovative framework for integrating cost-benefit analysis (CBA) within building information modeling (BIM). Socio-Economic Planning Sciences. 2023; 85 :101495. DOI: 10.1016/j.seps.2022.101495 - 12.
Biancardo SA, Intignano M, Viscione N, Guerra De Oliveira S, Tibaut A. Procedural modeling-based BIM approach for railway design. Journal of Advanced Transportation. 2021; 2021 :1-17 - 13.
Lee SS, Kim KT, Tanoli WA, Seo JW. Flexible 3D model partitioning system for nD-based BIM implementation of alignment-based civil infrastructure. Journal of Management in Engineering. 2020; 36 (1):04019037 - 14.
Chong H, Lopez R, Wang J, Wang X, Zhao Z. Comparative analysis on the adoption and use of BIM in road infrastructure projects. Journal of Management in Engineering. 2016; 32 :1-13 - 15.
Sankaran B, O’Brien W, Goodrum P, Khwaja N, Leite F, Johnson J. Civil integrated management for highway infrastructure: Case studies and lessons learned. Transportation Research Record: Journal of the Transportation Research Board. 2016; 2573 :10-17 - 16.
Abdelwahab HT. Intelligent design (4D, 5D and beyond) for road design and construction projects: Two case studies. International Road Federation (IRF) Examiner. 2018; 12 :21-25 - 17.
Abbondati F, Biancardo SA, Sicignano G, Guerra de Olivera S, Tibaut A, Dell’Acqua G. BIM parametric modeling of a railway underpass. Ingegneria Ferroviaria. 2020; 6 :443-459 - 18.
Salzano A, Intignano M, Mottola C, Biancardo SA, Nicolella M, Dell’Acqua G. Systematic literature review of open infrastructure BIM. Buildings. 2023; 13 (7):1593. DOI: 10.3390/buildings13071593 - 19.
Biancardo SA, Intignano M, Abbondati F, Abramović B, Dell’Acqua G. Horizontal building information modeling: The Croatian railway Gradec-Sveti Ivan Žabno case study. Ingegneria Ferroviaria. 2021; 76 (12):979-994, Cited 3 times - 20.
Biancardo SA, Intignano M, Veropalumbo R, Martinelli R, Calvanese V, Autelitano F, et al. BIM approach for stone pavements in archaeological sites: The case study of Vicolo Dei Balconi of Pompeii. Transportation Research Interdisciplinary Perspectives. 2023; 17 :100755. DOI: 10.1016/j.trip.2023.100755 - 21.
Biancardo SA, Viscione N, Cerbone A, Dessì E Jr. BIM-based design for road infrastructure: A critical focus on modeling guardrails and retaining walls. Infrastructures. 2020; 5 :59 - 22.
Tschickardt T, Krause D. BIM in highway construction using the example project availability model A 10/A 24. Bautechnik. 2019; 96 (3):259-268 - 23.
Bosurgi G, Celauro C, Pellegrino O, Rustica N, Sollazzo G. The BIM (building information modeling)-based approach for road pavement maintenance. Lecture Notes in Civil Engineering. 2020; 48 :480-490 - 24.
Jing W, Hao G, Li C, Wei W, Cheng J. BIM application approach on highway maintenance and management. In: Proceedings of the 19th COTA International Conference of Transportation Professionals; 6-8 July 2019; Nanjing. China. Reston, VA: ASCE; 2016. pp. 776-786 - 25.
Biancardo SA, Intignano M, Pires DM, Abbondati F, Dell'Acqua G. Heritage BIM approach for roman pavements. European Transport - Trasporti Europei. 2023; 91 :8. DOI: 10.48295/ET.2023.91.8 - 26.
Patel K, Ruparathna R. Life cycle sustainability assessment of road infrastructure: A building information modeling-(BIM) based approach. International Journal of Construction Management. 2023; 23 (11):1837-1846. DOI: 10.1080/15623599.2021.2017113 - 27.
Gan VJL, Liu T, Li K. Integrated BIM and VR for interactive aerodynamic design and wind comfort analysis of modular buildings. Buildings. 2022; 12 :333. DOI: 10.3390/buildings12030333 - 28.
Deng Y, Cheng JCP, Anumba C. A framework for 3D traffic noise mapping using data from BIM and GIS integration. Structure and Infrastructure Engineering. 2016; 12 (10):1267-1280. DOI: 10.1080/15732479.2015.1110603 - 29.
Wang J, Hou L, Wu P. BIM-supported tunnel light environment evaluation: A case study on Shanghai Chenxiang road tunnel project. In: Proceedings of the 16th International Conference on Computing in Civil and Building Engineering (ICCCBE 2016). Osaka, JPN: Osaka University; 2016. pp. 1023-1030. Available from: http://www.see.eng.osaka-u.ac.jp/seeit/icccbe2016/pages/conference5.html - 30.
Cantisani G, Correa Panesso JD, Del Serrone G, Di Mascio P, Gentile G, Loprencipe G, et al. Re-design of a road node with 7D BIM: Geometrical, environmental and microsimulation approaches to implement a benefit-cost analysis between alternatives. Automation in Construction. 2022; 135 :104133. DOI: 10.1016/j.autcon.2022.104133 - 31.
Shadram F, Johansson TD, Lu W, Schade J, Olofsson T. An integrated BIM-based framework for minimizing embodied energy during building design. Energy and Buildings. 2016; 128 :592-604. DOI: 10.1016/j.enbuild.2016.07.007 - 32.
Tang F, Ma T, Zhang J, Guan Y, Chen L. Integrating three-dimensional road design and pavement structure analysis based on BIM. Automation in Construction. 2020; 113 :103152. DOI: 10.1016/j.autcon.2020.103152 - 33.
Collao J, Lozano-Galant F, Lozano-Galant J. A., Turmo, J. BIM visual programming tools applications in infrastructure projects: A state-of-the-art review. Applied Sciences. 2021; 11 (18):8343 - 34.
VIASTRATA spin-off. Available from: https://www. viastrata.unina.it/