TLS Survey for Heritage Building Information Modeling

Version	Summary	Created by	Modification	Content Size	Created at	Operation
1		Salman Azhar	--	2557	2023-04-24 15:34:53	\|
2	April 24th, 2023	Junshan Liu	Meta information modification	2557	2023-04-24 15:45:34	\| \|
3	format correction	Dean Liu	-18 word(s)	2539	2023-04-25 02:52:39	\|

This entry is adapted from the peer-reviewed paper 10.3390/virtualworlds2020006

Heritage Building Information Modeling (HBIM) is an essential technology for heritage documentation, conservation, and management. It enables people to understand, archive, advertise, and virtually reconstruct their built heritage. Creating highly accurate HBIM models requires the use of several reality capture tools, such as terrestrial laser scanning (TLS), photogrammetry, unmanned aerial vehicles (UAV), etc.

BIM HBIM reality capture

1. Terrestrial Laser Scanning (TLS) for Data Acquisition for HBIM

The literature points out that TLS is one of the most important techniques for the research of heritage assets and has become the mandatory technique for HBIM development ^[1]. The biggest advantages of TLS for HBIM over other techniques include its high accuracy ^[2]^[3], fast speed for the amount of data captured over the time spent ^[4], and thoroughness of non-invasive object capture ^[5]^[6] that does not require a returned field survey ^[7]. As mentioned in Al-Bayari and Shatnawi’s study ^[8], 25–30% of the time for field surveys could be reduced using TLS to capture and document heritage.

2. Challenges and Limitations of TLS for HBIM

On the other hand, TLS has several drawbacks when being used for data acquisition for HBIM. It is expensive. A new laser scanner can easily cost USD tens of thousands ^[7]. It requires specialty software, substantial hardware, and highly trained professionals to process and handle the scan data ^[2]^[9]. A TLS survey can also be time-consuming ^[10], especially when it is used to capture large and complex heritage sites and is sensitive to the environment such as lighting conditions, dust, fog, rain, etc. ^[7]. The time required for a TLS survey varies depending on the complexity of the area of interest, the accuracy required, and the processing software used. Factors such as data acquisition time, environmental conditions, and data processing time can all contribute to the overall time required for the survey:

Data acquisition time: TLS involves capturing a large number of high-density point clouds from various viewpoints to cover the entire area of interest. This process can take a considerable amount of time, especially for large or complex areas where multiple scanning setups are needed;
Environmental conditions: Environmental conditions can also impact the survey time. Factors such as weather conditions, sunlight, and shadowing can affect the quality of the data collected, which may require additional scanning time to compensate;
Data processing time: Once the data is acquired, it needs to be processed to generate a usable point cloud. Large datasets with high point densities can take a long time to process, which can contribute to the overall time required for the survey.

Shiny/reflective, black, and transparent surfaces can also be troublesome. TLS is unable to capture objects that are hidden or not in line-of-sight ^[11]. Capturing accurate color is another challenge for the majority of the LiDAR scanners that are used for studying heritage ^[12].

Some of the articles selected revealed specific limitations of using TLS for data acquisition and challenges of processing the TLS scan data for HBIM development, as shown in Table 1.

Table 1. Limitations and challenges of TLS.

Category	Limitations or Challenges of TLS	Articles
Data Acquisition	Limited range of TLS: most roofs and upper building facades were out of range of TLS. Other RC technologies, such as UAVs, had to be used to compensate.	^[5]^[12]^[13]^[14]^[15]^[16]^[17]^[18]^[19]
	Poor results of capturing colors with the camera mounted on the scanner in varying lighting conditions are due to time gaps between the successive scans.	^[12]^[20]
	The distance from the scanner to the scanned object greatly influences the quality of the captured point cloud.	^[20]
	Scanners were used in dangerous areas.	^[21]
	Maintain the stability of the scaffolding that was set up for scanning.	^[21]
	It is impossible to perform a static laser scanning acquisition in some areas due to safety issues. Therefore, a handheld Mobile Mapping System (MMS) was used to capture those areas.	^[21]
	Lack of capability to capture images using the scanner.	^[22]
	Had to dismantle the building to scan the bracket set with hidden geometry.	^[23]
Data Processing	Combine multiple point cloud datasets created from different approaches (e.g., TLS and aerial photogrammetry) into one single point cloud.	^[5]^[7]^[9]^[12]^[14]^[15]^[16]^[19]
	Number of software applications required to process the scan data	^[11]^[19]^[24]^[25]^[26]^[27]
	Processing TLS scans is time-consuming.	^[24]^[28]
	Incorporation of TLS scans from two separate campaigns that were 10 years apart.	^[29]

To overcome these limitations, HBIM practitioners usually utilize other RC techniques to supplement TLS and acquire data for HBIM development. These techniques include analog survey, manual measurement, mobile scanners, and Structure from Motion (SfM) or photogrammetry ^[12].

3. Planning for the TLS Survey

A successful TLS survey begins with a good plan to guarantee the maximum coverage of the relevant surfaces, reduce self-occlusions, and ensure a sufficient density of the point cloud with the least amount of scans ^[13]. The quality of the data obtained from laser scanning might be influenced by several different factors. including:

The expectation of the data to be collected, e.g., accuracy and density of the point cloud, coverage of the structure ^[30];
The characteristics of the heritage to be captured, e.g., location, size, accessibility, complexity, occlusions and inaccessible areas; and the time constraints of the survey ^[31];
The atmosphere during the TLS survey, e.g., weather conditions such as humidity, temperature, and visibility ^[32];
The specification and limitation of the TLS equipment, i.e., scanning range, speed, scan-station setting up requirements, and the requirements for scanning targets ^[13].

One of the common mistakes for TLS data acquisition is “over scanning”, which can lead to long hours in the field and unnecessary extension of the registration process of scans in the postprocessing phase ^[7]. A scan position layout plan developed during site visits prior to scanning is a very efficient way to optimize the number of scan positions for saving time and obtaining good coverage ^[8]^[26]^[33]. Another important factor for determining scan positions is to ensure proper connections between each of the rooms, spaces, floors, outdoor, and indoor environments. These precautions are vital to guarantee that when the raw scan files are processed, the scan processing software can recognize corresponding features as well as properly align and connect each of the scans ^[31]. Project documentation, such as a floor plan ^[7] or Google Maps ^[26], can be used to draw the scan layout plan. To obtain data with higher accuracy, ground control points (GCPs) or scan targets (e.g., spheres or checkboards) are often used for TLS surveys, so they need to be included in the scan plans as well ^[3]^[16]^[34]. Figure 1 shows a proposed scan position plan for a project to capture a historic museum in Alabama, USA, using a Leica ScanStation C10 scanner.

Figure 1. A sample “Scan Position Layout Plan”. Red circles represent proposed scan locations, and yellow circles represent scan target locations.

Other aspects to be considered during TLS survey planning include the expected coverage of the survey (i.e., interior, exterior, or both), other data acquisition technologies and techniques that may be involved ^[14]^[35], the capture of color ^[1], and safety ^[21]. Column (b) in Table 1 shows the coverage (e.g., exterior façade only, interior façade only, or both exterior and interior) of the TLS scans for the studies included in the 58 selected publications.

Table 2. Information on data acquisition techniques and scan data processing of the HBIM projects in the included publications.

			(a) TLS Scanner Brands						(d) Control Reference		(e) Other RC Technologies Used for Data Acquisition					(f) Scan Processing Software				(g) # of Points Captured (million)
Article	Country of Heritage Site	Year of Survey	FARO	Leica	Trimble	Other	(b) TLS Coverage	(c) # of Scans	GCP	Target	SfM	UAV	T.S.	GPS	Other	FARO SCENE	Leica Cyclone	Autodesk ReCap	Other	(g) # of Points Captured (million)
^[5]	Italy	2012 and 2017	✓				Ext. and Int.	58			✓	✓	✓						3DReshaper
^[8]	Jordan				✓			11	✓	✓			✓
^[9] a	Saudi Arabia			✓				123	✓		✓	✓	✓	✓						10,600
^[12] b	Saudi Arabia			✓					✓	✓	✓	✓	✓	✓
^[36]	Spain	2019		✓			Ext. and Int.	7									✓
^[37]	Spain	2016		✓													✓
^[20]	Spain	2021		✓									✓				✓
^[38]	Saudi Arabia			✓			Ext. and Int.	100+	✓	✓			✓				✓
^[18]	Italy		✓															✓
^[25]	Italy		✓													✓		✓
^[21]	Italy	2020	✓				Ext. and Int.	65	✓				✓		Handheld scanner
^[13]	Italy		✓					213			✓	✓	✓					✓
^[24]	Italy	2021	✓				Ext. and Int.		✓		✓	✓	✓			✓		✓
^[28]	Italy	2019	✓													✓
^[39] a	Italy		✓
^[40] b	Italy		✓					182			✓		✓
^[41]
^[14]	Italy	2021					Ext. and Int.	47	✓	✓	✓	✓	✓
^[27]	Italy	2018	✓				Ext.	3			✓	✓							3D Zephyr
^[42]	Italy	2020		✓							✓	✓						✓
^[35]	Brazil	2016		✓							✓						✓
^[15]	Spain	2019	✓				Ext	107		✓	✓	✓	✓	✓		✓		✓		182
^[6]	Spain	2019	✓				Ext. and Int.	108		✓		✓		✓		✓		✓		214
^[32]	Poland	2017		✓			Ext. and Int.	7		✓
^[29]	Portugal	2021	✓					3			✓				Three-dimensional photo, Ground Penerating Radar (GPR); X-ray and SEM + EDX tests					1345
^[43]	Italy	2018	✓				Ext.			✓	✓		✓
^[44]	China	2019			✓		Ext.	35		✓	✓	✓						✓	Trimble Realwoks
^[2]	Spain	2020		✓			Ext.				✓	✓			Thermal images		✓		PolyWorks
^[45]	Egypt	2019				Z + F IMAGER		160					✓					✓
^[16]	Portugal	2019	✓				Ext. and Int.	95		✓			✓	✓		✓
^[10]	Italy	2019		✓			Ext. and Int.	69			✓						✓	✓		1200
^[11]	Italy	2019	✓				Ext. and Int.	64								✓				1014
^[34]	Portugal	2019	✓				Ext. and Int.	8		✓									CloudCompare
^[3] a	Spain
^[46] b	Spain			✓			Ext. and Int.	15	✓				✓	✓			✓			103
^[47]	Spain	2018		✓													✓
^[1] c
^[48]
^[49]	Italy	2018		✓			Ext. and Int.	256			✓				3D photo, wearable laser scanner
^[26]	Vietnam	2021			✓		Ext. and Int.	162										✓	Trimble Realwoks
^[50]	Spain	2018		✓			Ext. and Int.
^[51]	Spain										✓
^[7]	Slovakia		✓				Ext. and Int.	141		✓	✓	✓				✓
^[19]	Italy		✓				Ext.	3										✓	3DF Zephyr
^[33]	Italy	2020		✓				3	✓								✓
^[52]	Spain	2018		✓			Ext. and Int.	99			✓		✓					✓
^[4]
^[31]	Portugal	2019	✓				Ext. and Int.	144	✓		✓	✓		✓	GPS	✓		✓
^[53]	Spain	2016		✓			Ext. and Int.	27		✓			✓	✓	GPS		✓		3DReshaper
^[17]	France	2015	✓				Ext. and Int.	40		✓						✓			3DReshaper	839
^[54]	Italy	2017	✓				Ext. and Int.	14
^[22]	Cyprus					Surphased	Ext. and Int.	73			✓							✓	JRC Reconstructor	2080
^[55]	Portugal							10											CloudCompare
^[56]	Poland	2016				Z + F IMAGER	Ext. and Int.	36		✓							✓
^[57]	China	2021	✓				Ext. and Int.	25			✓	✓	✓					✓
^[58]	Italy	2020					Ext and Int	24	✓							✓		✓
^[23]	S. Korea														Handheld scanner			✓
^[59]	Algeria	2020	✓				Ext. and Int.	188		✓						✓		✓

4. Static TLS Devices

A total of 50 static TLS scanners from five different brands are explicitly mentioned in the selected publications, as shown in Figure 2. FARO^® and Leica^® dominate the list, supplying 24 and 20 of the scanners, respectively. Leica ScanStation C10 was one of the most widely adopted TLS scanners between 2015 and 2018 due to its high accuracy and long range. However, FARO Focus 3D series scanners started taking over in the late 2010s because of their high mobility and affordability. The FARO Focus Premium ^[60], Leica BLK360 ^[61], and Trimble X7 ^[62], as shown in Figure 3, are all considered to be the current state-of-the-art TLS scanners. All are lightweight, extremely fast, and have the capability of on-site registration. The brands of the TLS scanners used for HBIM projects in the included articles are shown in column (a) in Table 1.

Figure 2. Occurrences of TLS scanner brand names listed in the included publications.

Figure 3. Three of the latest state-of-the-art TLS scanners: (a) FARO Focus Premium ^[60]; (b) Leica BLK360 ^[61]; and (c) Trimble X7 ^[62].

5. TLS Survey

The scanners used for TLS surveys offer different options for scan resolution (Figure 4a). Scan resolution indicates the number of points captured per unit on a flat surface ^[20]. Scan resolution refers to the level of detail captured by the laser scanner during the scanning process and is an important consideration when selecting the appropriate settings for a particular scanning application. The scan resolution setting determines the density of points that the scanner will capture and create a point cloud from ^[32]. Several factors can impact the scan resolution, including the specification of the scanner, distance to the object, scan speed, reflectivity of the object, and environmental factors ^[32]. Choosing a scan resolution setting for TLS surveys depends on the specific application and the level of detail required. Depending on the site condition, the characteristics of the heritage, and time constraints, many HBIM practitioners choose to stay with the same scan resolution setting, normally an “intermedium level”, throughout the entire TLS survey for convenience and to save time. On the other hand, it is not unusual to use a combination of several resolution options to capture important facades at a high resolution and other areas at a medium or low resolution ^[52]. Maintaining field notes of the scan route to corroborate scan locations is essential for successful scan data postprocessing, as a project will rarely go as planned. Adjustments will be made, and more scans may be added to overcome occlusions or improve coverage and scan overlapping. All these changes to the original scan plan should be documented in the file notes. To improve the scan accuracy and help scan registration during postprocessing, reference points, such as GCPs or scan targets (e.g., spheres or checkboards) ^[7]^[14], are sometimes used, as shown in Table 1, Column (d). It is important to keep a record of the locations of these reference points in the field notes (Figure 4b).

Figure 4. (a) Choosing scan resolution on a FARO Focus 3D S-350 scanner and (b) scanning field notes taken using a floor plan to track the locations of the scans.

6. Other RC Technologies Assisting TLS for Data Acquisition

Due to the complexity of the geometry of the built heritage and the limitations of the laser scanning technology, TLS is not a sufficient system for complete data acquisition. Therefore, it usually takes other technological tools and techniques to capture additional data about the heritage structure to supplement the TLS survey or to help improve the accuracy and coverage of the TLS survey ^[9]^[31]^[32]. These tools and techniques can be classified into the following groups:

Structure from Motion (SfM) or photogrammetry, either terrestrially based or mounted on an Unmanned Aerial Vehicle (UAV) platform ^[5]^[15]^[24]^[52];
Mobile/handheld LiDAR or photogrammetry scanners ^[21]^[50];
Devices to control errors and verify the quality of the captured data. These devices include total station (TS) and global positioning system (GPS) ^[15]^[40]^[43].

See Column (e) in Table 1 for other RC technologies that were mentioned in the included publications.

6.1. SfM/Photogrammetry

TLS and photogrammetry can be integrated by exploiting the advantages of one over the other ^[27]. TLS is a more expensive technology in comparison with photogrammetric surveys, and higher knowledge is required to perform laser scans than to take photographs for 3D photoreconstructions ^[31]. On the other hand, photogrammetry is more cost-effective, more flexible, faster, and capable of collecting accurate and quality data for complex objects ^[9]. The benefit of integrating these two technologies is to take advantage of the TLS’s capability to acquire an accurate and dense point cloud and the flexibility of photogrammetry to operate even in exceptional conditions.

Combining TLS and photogrammetric techniques has proven to be the most effective way to record large and complex heritage sites in applications for documentation, structure assessment, texture mapping, feature extraction, etc. ^[12]. Twenty-three out of the fifty-eight articles included the capture of photogrammetric point clouds and combining them with TLS point clouds to fulfill the data acquisition task. In their research, de la Plata et al. ^[15] used a DSLR (digital single lens reflex) camera to generate high-density terrestrial photogrammetric point clouds of the interior surfaces of the buildings and TLS to capture exterior facades. Costantino, Pepe, and Restuccia ^[27] deployed a similar approach, using TLS to survey the external façade, a DLSR camera for inside, and a camera mounted on an unmanned aerial vehicle (UAV) for the upper part of the building. The integrated UAV with photogrammetric technology, as seen in Figure 5, offers practitioners the capacity to capture built heritage at higher elevations ^[63].

Figure 5. UAV photogrammetry: (a) UAV photogrammetry schematic and (b) UAV photogrammetric point cloud.

6.2. Mobile Handheld Scanners

Two reviewed articles indicated the utilization of hand-held MLS scanners to supplement static TLS surveys. A mobile handheld scanner supported by photogrammetry or LiDAR technology can be used to capture some hard-to-reach small spaces or to record fine details. To scan the unsafe areas that were inaccessible by using their tripod-based FARO Focus 3D scanner, Banfi et al. ^[21] used a handheld Mobile Mapping System (MMS), a GeoSLAM ZEB-HORIZON, to perform the work. Nieto-Julian et al. ^[50] used a Leica ScanStation C10 scanner to capture walls, columns, ceilings, beams, and roofs alongside a handheld Artec MHT 3D optical scanner (OS) to record the complex column capitals of a historic building.

6.3. TS and GPS

TS and GPS are used to establish control points (CPs). CPs can be used to integrate point clouds acquired through different technologies (e.g., TLS and photogrammetry) or to check the quality and accuracy of the TLS scan data. Banfi et al. ^[21] used a Leica TPS1200 total station to setup eight CPs (four inside and four outside), and then used them to successfully combine the two point clouds captured with a TLS scanner and a handheld scanner.

References

Moyano, J.; Nieto-Julián, J.E.; Lenin, L.M.; Bruno, S. Operability of Point Cloud Data in an Architectural Heritage Information Model. Int. J. Arch. Herit. 2021, 16, 1588–1607.
Martín-Lerones, P.; Olmedo, D.; López-Vidal, A.; Gómez-García-Bermejo, J.; Zalama, E. BIM Supported Surveying and Imaging Combination for Heritage Conservation. Remote Sens. 2021, 13, 1584.
Moyano, J.; Gil-Arizón, I.; Nieto-Julián, J.E.; Marín-García, D. Analysis and management of structural deformations through parametric models and HBIM workflow in architectural heritage. J. Build. Eng. 2022, 45, 103274.
Rocha, G.; Mateus, L. A Survey of Scan-to-BIM Practices in the AEC Industry—A Quantitative Analysis. ISPRS Int. J. Geo-Inf. 2021, 10, 564.
Abbate, E.; Invernizzi, S.; Spanò, A. HBIM parametric modelling from clouds to perform structural analyses based on finite elements: A case study on a parabolic concrete vault. Appl. Geomat. 2022, 14, 79–96.
Franco, P.A.C.; De La Plata, A.R.M.; Franco, J.C. From the Point Cloud to BIM Methodology for the Ideal Reconstruction of a Lost Bastion of the Cáceres Wall. Appl. Sci. 2020, 10, 6609.
Palčák, M.; Kudela, P.; Fandáková, M.; Kordek, J. Utilization of 3D Digital Technologies in the Documentation of Cultural Heritage: A Case Study of the Kunerad Mansion (Slovakia). Appl. Sci. 2022, 12, 4376.
Al-Bayari, O.; Shatnawi, N. Geomatics techniques and building information model for historical buildings conservation and restoration. Egypt. J. Remote Sens. Space Sci. 2022, 25, 563–568.
Alshawabkeh, Y.; Baik, A.; Fallatah, A. As-Textured As-Built BIM Using Sensor Fusion, Zee Ain Historical Village as a Case Study. Remote Sens. 2021, 13, 5135.
Mammoli, R.; Mariotti, C.; Quattrini, R. Modeling the Fourth Dimension of Architectural Heritage: Enabling Processes for a Sustainable Conservation. Sustainability 2021, 13, 5173.
Massafra, A.; Prati, D.; Predari, G.; Gulli, R. Wooden Truss Analysis, Preservation Strategies, and Digital Documentation through Parametric 3D Modeling and HBIM Workflow. Sustainability 2020, 12, 4975.
Alshawabkeh, Y.; Baik, A.; Miky, Y. Integration of Laser Scanner and Photogrammetry for Heritage BIM Enhancement. ISPRS Int. J. Geo-Inf. 2021, 10, 316.
Banfi, F.; Roascio, S.; Paolillo, F.R.; Previtali, M.; Roncoroni, F.; Stanga, C. Diachronic and Synchronic Analysis for Knowledge Creation: Architectural Representation Geared to XR Building Archaeology (Claudius-Anio Novus Aqueduct in Tor Fiscale, the Appia Antica Archaeological Park). Energies 2022, 15, 4598.
Conti, A.; Fiorini, L.; Massaro, R.; Santoni, C.; Tucci, G. HBIM for the preservation of a historic infrastructure: The Carlo III bridge of the Carolino Aqueduct. Appl. Geomat. 2022, 14, 41–51.
Plata, A.; Franco, P.; Franco, J.; Bravo, V.G. Protocol Development for Point Clouds, Triangulated Meshes and Parametric Model Acquisition and Integration in an HBIM Workflow for Change Control and Management in a UNESCO’s World Heritage Site. Sensors 2021, 21, 1083.
Machete, R.; Silva, J.R.; Bento, R.; Falcão, A.P.; Gonçalves, A.B.; de Carvalho, J.M.L.; Silva, D.V. Information transfer between two heritage BIMs for reconstruction support and facility management: The case study of the Chalet of the Countess of Edla, Sintra, Portugal. J. Cult. Herit. 2021, 49, 94–105.
Rolin, R.; Antaluca, E.; Batoz, J.-L.; Lamarque, F.; Lejeune, M. From Point Cloud Data to Structural Analysis Through a Geometrical hBIM-Oriented Model. J. Comput. Cult. Herit. 2019, 12, 1–26.
Banfi, F. The Evolution of Interactivity, Immersion and Interoperability in HBIM: Digital Model Uses, VR and AR for Built Cultural Heritage. ISPRS Int. J. Geo-Inf. 2021, 10, 685.
Pepe, M.; Costantino, D.; Garofalo, A.R. An efficient pipeline to obtain 3D model for HBIM and structural analysis purposes from 3D point clouds. Appl. Sci. 2020, 10, 1235.
Ariza-López, F.J.; Reinoso-Gordo, J.F.; García-Balboa, J.L.; Ariza-López, I. Quality specification and control of a point cloud from a TLS survey using ISO 19157 standard. Autom. Constr. 2022, 140, 104353.
Banfi, F.; Brumana, R.; Landi, A.G.; Previtali, M.; Roncoroni, F.; Stanga, C. Building archaeology informative modelling turned into 3D volume stratigraphy and extended reality time-lapse communication. Virtual Archaeol. Rev. 2022, 13, 1–21.
Santagati, C.; Papacharalambous, D.; Sanfilippo, G.; Bakirtzis, N.; Laurini, C.; Hermon, S. HBIM approach for the knowledge and documentation of the St. John the Theologian cathedral in Nicosia (Cyprus). J. Archaeol. Sci. Rep. 2021, 36, 102804.
Youn, H.-C.; Yoon, J.-S.; Ryoo, S.-L. HBIM for the Characteristics of Korean Traditional Wooden Architecture: Bracket Set Modelling Based on 3D Scanning. Buildings 2021, 11, 506.
Barrile, V.; Bernardo, E.; Bilotta, G. An Experimental HBIM Processing: Innovative Tool for 3D Model Reconstruction of Morpho-Typological Phases for the Cultural Heritage. Remote Sens. 2022, 14, 1288.
Banfi, F. HBIM, 3D drawing and virtual reality for archaeological sites and ancient ruins. Virtual Archaeol. Rev. 2020, 11, 16–33.
Nguyen, T.A.; Do, S.T.; Le-Hoai, L.; Nguyen, V.T.; Pham, T.-A. Practical workflow for cultural heritage digitalization and management: A case study in Vietnam. Int. J. Constr. Manag. 2022, 1–15.
Costantino, D.; Pepe, M.; Restuccia, A. Scan-to-HBIM for conservation and preservation of Cultural Heritage building: The case study of San Nicola in Montedoro church (Italy). Appl. Geomat. 2021, 1–15.
Barrile, V.; Fotia, A. A proposal of a 3D segmentation tool for HBIM management. Appl. Geomat. 2021, 14, 197–209.
Garcia-Gago, J.; Sánchez-Aparicio, L.J.; Soilán, M.; González-Aguilera, D. HBIM for supporting the diagnosis of historical buildings: Case study of the Master Gate of San Francisco in Portugal. Autom. Constr. 2022, 141, 104453.
Aryan, A.; Bosché, F.; Tang, P. Planning for terrestrial laser scanning in construction: A review. Autom. Constr. 2021, 125, 103551.
Rocha, G.; Mateus, L.; Fernández, J.; Ferreira, V. A Scan-to-BIM Methodology Applied to Heritage Buildings. Heritage 2020, 3, 47–65.
Fryskowska, A.; Stachelek, J. A no-reference method of geometric content quality analysis of 3D models generated from laser scanning point clouds for hBIM. J. Cult. Herit. 2018, 34, 95–108.
Pepe, M.; Costantino, D.; Alfio, V.S.; Restuccia, A.G.; Papalino, N.M. Scan to BIM for the digital management and representation in 3D GIS environment of cultural heritage site. J. Cult. Herit. 2021, 50, 115–125.
Mol, A.; Cabaleiro, M.; Sousa, H.S.; Branco, J.M. HBIM for storing life-cycle data regarding decay and damage in existing timber structures. Autom. Constr. 2020, 117, 103262.
Cuperschmid, A.R.M.; Fabricio, M.M.; Franco, J.J.C. HBIM Development of A Brazilian Modern Architecture Icon: Glass House by Lina Bo Bardi. Heritage 2019, 2, 1927–1940.
Andriasyan, M.; Moyano, J.; Nieto-Julián, J.E.; Antón, D. From Point Cloud Data to Building Information Modelling: An Automatic Parametric Workflow for Heritage. Remote Sens. 2020, 12, 1094.
Antón, D.; Medjdoub, B.; Shrahily, R.; Moyano, J. Accuracy evaluation of the semi-automatic 3D modeling for historical building information models. Int. J. Arch. Herit. 2018, 12, 790–805.
Baik, A. The Use of Interactive Virtual BIM to Boost Virtual Tourism in Heritage Sites, Historic Jeddah. ISPRS Int. J. Geo-Inf. 2021, 10, 577.
Brumana, R.; Condoleo, P.; Grimoldi, A.; Banfi, F.; Landi, A.G.; Previtali, M. HR LOD based HBIM to detect influences on geometry and shape by stereotomic construction techniques of brick vaults. Appl. Geomat. 2018, 10, 529–543.
Brumana, R.; Della Torre, S.; Previtali, M.; Barazzetti, L.; Cantini, L.; Oreni, D.; Banfi, F. Generative HBIM modelling to embody complexity (LOD, LOG, LOA, LOI): Surveying, preservation, site intervention—The Basilica di Collemaggio (L’Aquila). Appl. Geomat. 2018, 10, 545–567.
Colucci, E.; Xing, X.; Kokla, M.; Mostafavi, M.; Noardo, F.; Spanò, A. Ontology-Based Semantic Conceptualisation of Historical Built Heritage to Generate Parametric Structured Models from Point Clouds. Appl. Sci. 2021, 11, 2813.
Croce, V.; Caroti, G.; De Luca, L.; Jacquot, K.; Piemonte, A.; Véron, P. From the Semantic Point Cloud to Heritage-Building Information Modeling: A Semiautomatic Approach Exploiting Machine Learning. Remote Sens. 2021, 13, 461.
Grillanda, N.; Cantini, L.; Barazzetti, L.; Milani, G.; Della Torre, S. Advanced Modeling of a Historical Masonry Umbrella Vault: Settlement Analysis and Crack Tracking via Adaptive NURBS Kinematic Analysis. J. Eng. Mech. 2021, 147, 04021095.
Lin, G.; Giordano, A.; Sang, K. From Site Survey to HBIM Model for the Documentation of Historic Buildings: The Case Study of Hexinwu Village in China. Conserv. Sci. Cult. Herit. 2020, 20, 111–123.
Marzouk, M. Using 3D Laser Scanning to Analyze Heritage Structures: The Case Study of Egyptian Palace. J. Civ. Eng. Manag. 2020, 26, 53–65.
Moyano, J.; Justo-Estebaranz, A.; Nieto-Julián, J.E.; Barrera, A.O.; Fernández-Alconchel, M. Evaluation of Records Using Terrestrial Laser Scanner in Architectural Heritage for Information Modeling in HBIM Construction: The Case Study of the La Anunciaci Acute Accent on Church (Seville). J. Build. Eng. 2022, 62, 105190.
Moyano, J.; Odriozola, C.P.; Nieto-Julián, J.E.; Vargas, J.M.; Barrera, J.A.; León, J. Bringing BIM to archaeological heritage: Interdisciplinary method/strategy and accuracy applied to a megalithic monument of the Copper Age. J. Cult. Herit. 2020, 45, 303–314.
Murphy, M.; McGovern, E.; Pavia, S. Historic Building Information Modelling–Adding intelligence to laser and image based surveys of European classical architecture. ISPRS J. Photogramm. Remote Sens. 2013, 76, 89–102.
Nespeca, R. Towards a 3D digital model for management and fruition of Ducal Palace at Urbino. An integrated survey with mobile mapping. SCIRES-IT-SCIentific RESearch Inf. Technol. 2018, 8, 1–14.
Nieto-Julián, J.E.; Antón, D.; Moyano, J.J. Implementation and Management of Structural Deformations into Historic Building Information Models. Int. J. Arch. Herit. 2020, 14, 1384–1397.
Nieto-Julián, J.E.; Farratell, J.; Cavada, M.B.; Moyano, J. Collaborative Workflow in an HBIM Project for the Restoration and Conservation of Cultural Heritage. Int. J. Arch. Herit. 2022, 1–20.
Reinoso-Gordo, J.F.; Rodríguez-Moreno, C.; Gómez-Blanco, A.J.; León-Robles, C. Cultural Heritage Conservation and Sustainability Based on Surveying and Modeling: The Case of the 14th Century Building Corral del Carbón (Granada, Spain). Sustainability 2018, 10, 1370.
Rodríguez-Moreno, C.; Reinoso-Gordo, J.F.; Rivas-López, E.; Gómez-Blanco, A.; Ariza-López, F.J.; Ariza-López, I. From point cloud to BIM: An integrated workflow for documentation, research and modelling of architectural heritage. Surv. Rev. 2018, 50, 212–231.
Rossi, A.; Palmieri, U. Experimentation of an Information Model. Vitruvio 2020, 5, 37–46.
Santos, D.; Cabaleiro, M.; Sousa, H.S.; Branco, J.M. Apparent and resistant section parametric modelling of timber structures in HBIM. J. Build. Eng. 2022, 49, 103990.
Sztwiertnia, D.; Ochałek, A.; Tama, A.; Lewińska, P. HBIM (heritage Building Information Modell) of the Wang Stave Church in Karpacz–Case Study. Int. J. Arch. Herit. 2021, 15, 713–727.
Tan, J.; Leng, J.; Zeng, X.; Feng, D.; Yu, P. Digital Twin for Xiegong’s Architectural Archaeological Research: A Case Study of Xuanluo Hall, Sichuan, China. Buildings 2022, 12, 1053.
Ursini, A.; Grazzini, A.; Matrone, F.; Zerbinatti, M. From scan-to-BIM to a structural finite elements model of built heritage for dynamic simulation. Autom. Constr. 2022, 142, 104518.
Zouaoui, M.A.; Djebri, B.; Capsoni, A. From Point Cloud to HBIM to FEA, the Case of a Vernacular Architecture: Aggregate of the Kasbah of Algiers. J. Comput. Cult. Herit. 2021, 14, 1–21.
FARO Focus Premium. Available online: https://www.faro.com/en/Resource-Library/Brochure/FARO-Focus-Premium (accessed on 26 November 2022).
BLK360|Leica Geosystems. Available online: https://shop.leica-geosystems.com/leica-blk/blk360?c1=GAW_SE_NW&source=USA_RC_BRND&kw=blk360&utm_source=GAW_SE_NWUSA_RC_BRND&utm_medium=cpc&utm_term=blk360&cr5=621284425896&cr7=c&gclid=Cj0KCQiAj4ecBhD3ARIsAM4Q_jGHP5ricF1l0OngsXYrkxeryfior4_I09A2sCpcSF1eZfDIc5SkwPoaAj4REALw_wcB (accessed on 26 November 2022).
Trimble X7|3D Laser Scanning|Trimble Geospatial. Available online: https://geospatial.trimble.com/products-and-solutions/trimble-x7 (accessed on 26 November 2022).
Martínez-Carricondo, P.; Carvajal-Ramírez, F.; Yero-Paneque, L.; Agüera-Vega, F. Combination of HBIM and UAV photogrammetry for modelling and documentation of forgotten heritage. Case study: Isabel II dam in Níjar (Almería, Spain). Herit. Sci. 2021, 9, 1–15.

© Text is available under the terms and conditions of the Creative Commons Attribution (CC BY) license; additional terms may apply. By using this site, you agree to the Terms and Conditions and Privacy Policy.

Upload a video for this entry

Information

Subjects: Construction & Building Technology

Contributors MDPI registered users' name will be linked to their SciProfiles pages. To register with us, please refer to https://encyclopedia.pub/register :

Junshan Liu

Salman Azhar

Danielle Willkens

Botao Li

View Times: 338

Update Date: 25 Apr 2023

Table of Contents

Video Upload Options

Confirm