In the building sector, a defect refers to a visible anomaly affecting a component of the structure, whereas pathology refers to the study of these defects, their origin, mechanism, and evolution over the structure’s lifespan. Such defects most often result from a combination of design flaws, construction errors, environmental factors, and aging due to a lack of maintenance [1]. Pathologies fall into three categories, structural, functional, and aesthetic, depending on whether they affect the structure’s stability, usability, or merely its visual appearance.

Several definitions of BIM coexist in the literature, each highlighting a specific aspect of the approach. BIM entails the use of a shared digital representation of a structure, facilitating design, construction, operation, and decision-making [2]. It is also defined as a modeling technology linked to processes for model production, communication, and analysis [3]. This approach incorporates additional dimensions, ranging from geometric 3D to industrial 10D; 7D is utilized for operation and maintenance, which is the focus of the present assessment. Its precision is characterized by Levels of Development (LOD) [4], and its collaborative maturity by a dedicated maturity model [5], as illustrated inFigure 1.

Figure 1.BIM dimensions and maturity levels.

BIM interoperability refers to the ability to exchange and utilize digital model data between software applications without loss of information. It relies on the open IFC (Industry Foundation Classes) format, developed by buildingSMART International [6] and standardized via a dedicated ISO standard since 2013 [7]; this format supports exchange via STEP (.ifc) text files, XML (ifcXML), or compressed (ifcZIP) versions. The format has undergone several generations since its inception in 1996; IFC 2x3 and IFC 4 remain the most widely used versions, while the latest version, IFC 4x3, now extends the format to linear infrastructure.

The building subject to the study is located in the Zongo neighborhood (Lot 557, Plot B) within the UNDP residential zone in Cotonou’s 12th arrondissement, behind the headquarters of the National Water Company of Benin (SONEB), as shown in Figure 2. It is a reinforced concrete structure featuring a column-beam-slab framework; the building comprises a basement, a ground floor, and four upper stories (R + 4), topped by a concrete roof terrace. It was constructed approximately twenty years ago and has undergone no major renovations since. The basement houses offices, the ground floor contains reception and circulation areas, and the upper levels consist of individual offices created through the progressive partitioning of originally open-plan spaces.

Source: Open Street Map.

Figure 2. Location of the former CNCB study building.

The digital workflow implemented in this study relies on four complementary tools: the FARO Focus S350 3D laser scanner shown in Figure 3, Autodesk Revit 2025 software shown in Figure 4, BIMcollab Zoom software and Autodesk Robot Structural Analysis software shown in Figure 5.

Figure 3.FARO Focus S350 3D laser scanner and its operating principle.

Figure 4.Autodesk Revit 2025 software interface and its operation.

Figure 5.Autodesk Robot Structural Analysis 2025 software interface and its operation.

3.2.1. Traditional Assessment

1)Visual inspection

The equipment used for visual inspection is shown in/see Plate 1.

Plate 1.Main visual inspection instruments.

The various phases of the intervention are summarized in Table 1. A visual inspection of the building was carried out on site together with the AVESIG Bénin team, complementing and validating the technical expertise report produced by AVESIG in 2022 on behalf of the Ministry of Justice, the building having undergone no rehabilitation since then. This inspection covered all levels of the building, basement, ground floor and four upper floors, organized by work package, masonry and structural works, finishes, secondary works, plumbing, electrical and HVAC networks, in order to confirm on site the defects affecting the building fabric and to produce an updated condition survey. Each identified defect was photographed and located, by level, room and position on the element concerned, before being classified according to its nature, structural, functional or aesthetic, and according to four severity levels of the Table 2, assigned on the basis of a combined appraisal of the nature of the defect, its extent and its potential impact on the safety, functionality or appearance of the structure.

Table 1.Phases and methodology of the building’s visual inspection.

Table 2.Classification matrix for defects based on severity level.

2)Sizing of a beam-column system

The beam-column system identified as the most critical during the visual inspection was sized in accordance with the BAEL 91 (amended 1999) regulations, based on material assumptions in the absence of in-situ testing, concrete class C25/30 and Fe400 steel put on Table 3. The loads applied to the beam, self-weight, weight of the skylight glazing and weight of the aluminium profiles, were evaluated on the basis of standard dimension and density assumptions. The beam was modeled as statically determinate on two simple supports, subjected to a uniformly distributed load, while the associated column was considered fixed at its base in the lower floor slab of level R + 4 and pinned at its head at the connection with the beam.

Table 3.Input data for beam sizing.

Design load combination

Maximum bending moment

Required reinforcement area

Non-brittleness condition

For the column subjected to simple compression, the ultimate axial load (Nu) was determined using the same load combination; the buckling length (lf) was used to calculate the slenderness ratio which was compared against the regulatory limit of 50 before verifying the reinforcement area against the minimum required by the regulations.

Loading at the Ultimate Limit State (ULS)

Slenderness condition

Theoretical reinforcement area

Minimum reinforcement area

3.2.2. Implementation of the BIM Process

1)Scan-to-BIM Process and Quality Control

I)Scan-to-BIM Process

a)3D Laser Scanning

The building survey was carried out by the AVESIG Bénin technical team using a FARO Focus S350 3D laser scanner, in accordance with its standard acquisition protocol. The parameters were adapted to site conditions, with an indoor profile for distances under 10 m and an outdoor profile for distances under 20 m. An appropriate resolution was selected to ensure the required level of detail, while color acquisition enabled point cloud colorization. Several scan stations were set up to ensure sufficient overlap between acquisitions. Data registration relied on the overlapping areas between scans, without the use of artificial targets. After cleaning extraneous elements and assembling the scans in FARO Scene, a single, coherent point cloud was obtained to serve as the basis for the digital reconstruction of the building.

b)Modeling in Autodesk Revit

The assembled point cloud was imported into Autodesk Revit 2025 and aligned with the project’s coordinate system. Parametric modeling was then carried out progressively, drawing directly on the survey data. The building’s main elements, including walls, columns, beams, slabs, stairs, joinery and suspended ceilings, were reconstructed to form a digital model representative of the existing condition shown in Figure 6. Throughout the process, the model’s geometric consistency was checked against the source point cloud, with occasional adjustments made to improve the accuracy of the model.

II)Quality Control

Given the scope of the survey, quality control focused primarily on the geometry of the existing building, as summarized in Table 4. Technical networks were not included in the scanning campaign and are therefore not covered by the compliance checks described below.

Table 4.Quality control.

Figure 6.Raw point cloud (from basement to second floor) on the left and reconstructed digital model on the right.

III)Integration of defects

Each defect identified during the traditional diagnosis was linked to the corresponding element in the model via a custom property set, Pset_DiagnosticCNCB. This set populates three attributes: the nature of the defect, the severity level (based on the adopted classification matrix), and the reference of the associated diagnostic report.

IV)Creation of Smart Views

The enriched model was exported in IFC 4x3 format and then checked in BIMcollab Zoom, where “Smart Views” were created to enable automatic color-coded visualization of defects based on their severity level.

V)Sizing in Robot Structural Analysis

The beam-column system underwent a second verification in Autodesk Robot Structural Analysis Professional. This was achieved by directly transferring the analytical model from Revit, leveraging the native interoperability between the two software applications. Loads and load combinations were defined in accordance with the BAEL 91 (amended 1999) regulations covering both ultimate limit states and serviceability limit states prior to calculating reinforcement and verifying the design within Robot.

4.1.1. Identified Defects

Nine non-structural defects were identified on the building as Table 5shows, primarily affecting the structural shell and secondary elements without compromising the structure’s overall stability. These fall into two main categories: moisture-related issues (such as water ingress, mold, and façade cracks) and deterioration of finishes and secondary elements due to a lack of maintenance. Additionally, localized structural degradation was observed on the beam supporting the glass roof at the fourth-floor level (R + 4), characterized by spalling of the concrete cover and exposed reinforcement.

Table 5.Illustration of some defects identified on the building.

4.1.2. Digital Model and Diagnostic Model

Applying the Scan-to-BIM process resulted in a comprehensive 3D digital model covering all six levels of the building from the basement to the fourth floor that faithfully reproduces the structure’s actual geometry. Enriching the model with semantic data via the Pset_Diagnostic CNCB property set made it possible to associate three attributes with each affected component: the type of defect, the severity level, and the diagnostic report reference. Configuring “Smart Views” in BIMcollab Zoom based on the severity parameter generated a color-coded visualization of the defects across four criticality levels shown in Figure 7, providing an immediate spatial overview of the structure’s condition.

Figure 7.3D rendering of the digital model (left) and the diagnostic model (right).

4.1.3. Results of Manual Sizing and Sizing Using Robot Structural Analysis

Manual sizing of the beam yields a required minimum depth of 40 cm for a width of 20 cm, against an actual depth measured on site of 33 cm for a width of 25 cm, the beam thus being deemed non-compliant according to the manual calculation of Table 6. The required steel cross-section is 1.57 cm²; this value, like that given for the column, corresponds to the theoretically required reinforcement according to the calculation, and not to a survey of the reinforcement actually in place. The associated column, with a 20 × 20 cm section, is confirmed compliant with a minimum reinforcement area of 3.2 cm² satisfied by 4HA12 mentioned in Table 7. Under Robot Structural Analysis, the section from the manual calculation, 20 × 40 cm, is first confirmed compliant, as is the actual section measured on site, 25 × 33 cm, validated despite the degradation observed. An optimization then yields a minimum section of 20 × 30 cm for 1.57 cm² of steel. For the column, Robot confirms compliance with 4.52 cm² of steel and the same 4HA12 reinforcement.

Table 6.Comparison of beam sizing results.

The manual calculation, more conservative, judges the actual section non-compliant as it does not reach the required 40 cm, whereas Robot Structural Analysis, applied to this same 25 × 33 cm section, confirms it compliant, qualifying the severity of the manual diagnosis without invalidating it. The optimization to 20 × 30 cm further confirms a substantial margin between the regulatory minimum and the section actually in place.

Table 7.Comparison of column sizing results.

Both methods converge regarding the column’s compliance, with the same 4HA12 reinforcement selected in both cases. Only the calculated steel cross-section differs slightly governed by the regulatory minimum in the manual calculation versus a slightly higher value in Robot reflecting a load-bearing capacity reserve without calling into question the compliance confirmed by both approaches.

4.1.4. Structural Model and Internal Force Diagrams in Robot Structural Analysis

The building’s analytical model of Figure 8, obtained via transfer from the Revit model, represents the geometry of all load-bearing elements columns, beams, and floor slabs across the structure’s six levels. Only the beam supporting the glass canopy at level R + 4 and its associated column underwent a full structural analysis; the resulting internal force diagrams showing bending moments and shear forces of Figure 9 were extracted for the ultimate limit state.

Figure 8.Analytical model of the building and the beam under study in Autodesk Robot Structural Analysis.

Figure 9. Moment and force diagrams at the Ultimate Limit State (ULS) for the beam under study.

The bending moment diagram confirms a maximum moment at mid-span, consistent with the expected behavior of a statically determinate beam subjected to a uniformly distributed load, while the shear force diagram shows a symmetrical linear distribution with maximum values at the supports.

Figure 10. Total deflection and permissible deflection.

Furthermore, the deflection check confirms that the calculated total deflection remains within the permissible limit across the entire span as it is shown on Figure 10.

This compliance with serviceability limit states suggests that the deterioration observed on site stems more from reinforcement corrosion caused by chronic water ingress than from excessive deformation of the element.

Analysis of the results reveals that Building Information Modeling serves as a methodological complement to traditional assessment rather than a substitute for it. Geometric reconstruction via Scan-to-BIM overcame the lack of original plans a major constraint identified in BIM applications for existing structures [8]. Semantic enrichment using the CNCB Pset_Diagnostic transforms the model into an active management tool, aligning with the vision of BIM as a shared information repository [3], while color-coded visualization of defects facilitates the prioritization of interventions, echoing findings on BIM’s contribution to decision-making [9]. Furthermore, exporting data in IFC 4x3 format ensures collaborative utility in accordance with principles established by buildingSMART International [6], and native interoperability between Revit and Robot Structural Analysis eliminates the risk of manual data re-entry by ensuring consistency between the architectural and structural analysis models.

However, this approach has limitations. The precision reports transmitted for point cloud processing remain partial, not allowing exact documentation of the geometric registration tolerance between scan stations, only a qualitative verification of the model consistency having been carried out. The absence of more advanced non-destructive testing, in particular pachometry for detecting the existing reinforcement, leaves the assessment of load-bearing elements incomplete, visual inspection needing to be supplemented by further investigations whenever structural defects are suspected [10]. Similarly, the structural characterization relies on loads evaluated using standard dimension and density assumptions, rather than on measured or documented loads, as well as on material properties derived from standard regulatory classes rather than confirmed by in-situ testing. Moreover, the reproducibility of this approach is constrained by the lack of a national BIM regulatory framework and the cost of equipment, two significant barriers in the Beninese context. Overcoming these obstacles would be a natural next step for this work, involving the performance of the recommended tests and the gradual extension of the approach to other existing public buildings, contingent upon the establishment of national BIM regulations and enhanced training for sector stakeholders.