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<h2>Introduction</h2>
<p>Historic masonry structures constitute a significant portion of the built cultural heritage in Mediterranean countries, with Italy alone possessing thousands of medieval towers, churches, and palaces that require continuous care (Russo, 2013; Valente & Milani, 2018). Preventive conservation—the systematic management of environmental conditions, material decay, and structural vulnerabilities—has emerged as a paradigm shift from reactive restoration towards proactive maintenance (Jouan & Hallot, 2020; Doria & Michalski, 2018). Digital twin technology offers a promising avenue to operationalise preventive conservation by creating a living virtual replica that merges as-built geometry, sensor streams, historical documentation, and predictive models (Ni et al., 2022; Angjeliu et al., 2020).</p><p>Despite growing interest, the application of digital twins to historic masonry structures is still nascent. Early studies have focused on specific subsystems—such as equivalent frame models for seismic monitoring (Sivori et al., 2023), or HBIM-based repositories for documentation (Jouan & Hallot, 2019; Mora et al., 2020)—but rarely integrate real-time sensor data, structural analysis, and heritage significance into a single operational platform. The challenge is compounded by the geometric complexity and material heterogeneity of historic masonry, which demands multi-scale modelling and careful calibration (Milani et al., 2014; Hemeda, 2019).</p><p>This paper presents a comprehensive digital twin framework for preventive conservation, applied to a medieval masonry tower in the Abruzzo region of Italy—a representative example of Romanesque-Gothic construction typical of the 13th–14th centuries (Berardinis et al., 2014). The research objectives are: (1) to develop a hybrid HBIM-FEM digital twin that fuses geometric survey, sensor data, and structural models; (2) to calibrate the numerical model using ambient vibration measurements; (3) to implement a cloud-based dashboard for real-time monitoring and scenario simulation; and (4) to integrate heritage significance criteria into the prioritisation of conservation actions. By demonstrating this integrated approach, the study aims to provide a replicable methodology for preventive conservation of historic masonry worldwide.</p>
<h2>Literature Review</h2>
<h4>Digital twin concepts in heritage conservation</h4><p>The term “digital twin” originated in manufacturing but has been progressively adopted in the built heritage domain. Jouan and Hallot (2019, 2020) formalised a research framework linking HBIM, condition survey, and significance assessment under a preventive conservation umbrella. Ni et al. (2022) demonstrated a cloud-based digital twin for a historic theatre, integrating IoT sensors and energy simulations. Massafra et al. (2022) and Liu et al. (2023) extended HBIM-driven workflows to energy retrofitting and heritage tourism, respectively. These contributions underscore the potential of digital twins to centralise heterogeneous data, but most focus on documentation and energy efficiency rather than structural health.</p><h4>Structural modelling of historic masonry</h4><p>Numerical modelling of masonry structures presents well-known challenges due to anisotropy, nonlinearity, and lack of reinforcement (Roche et al., 2015). Milani et al. (2014) advanced detailed 3D FE models of cross vaults, while Sivori et al. (2023) proposed an equivalent frame digital twin specifically for seismic monitoring of Palazzo Consoli in Gubbio. Angjeliu et al. (2020) developed a simulation model integrating experimental reality for a historical building in Milan, showing that calibration with OMA reduces model uncertainty significantly. Kita et al. (2021) combined OMA and IDA for damage identification in historic towers. Vicario and Balocco (2021, 2023) integrated experimental and numerical approaches for the San Marco Museum, linking microclimate monitoring to degradation patterns.</p><h4>Sensor integration and condition assessment</h4><p>Continuous monitoring of environmental parameters (temperature, humidity, pollutants) and structural response (crack opening, vibrations) is critical for preventive conservation (Graue et al., 2013; Kang et al., 2019). Kilic (2022) used wavelet analysis and NDT for condition assessment of a historic masonry bridge. Lubowiecka et al. (2011) demonstrated a multidisciplinary approach for a masonry bridge, combining geomatics, geophysics, and structural analysis. The RILEM TC 203-RHM (2012) provided standardised guidance on repair mortars, emphasising compatibility and durability. De Berardinis et al. (2014) addressed energy efficiency of historic masonry in minor centres of Abruzzo, highlighting the need for integrated diagnostics. Sammartano et al. (2023) integrated HBIM-GIS for multi-scale seismic vulnerability assessment, demonstrating the value of cross-scale information.</p><h4>Significance-based conservation prioritisation</h4><p>Jouan and Hallot (2019, 2020) argued that preventive conservation must be driven by heritage significance rather than only technical condition. Their framework uses value mappings (historical, aesthetic, social) to assign weights to conservation actions. Marra et al. (2021) combined an informative system and historical digital twin for maintenance of artistic assets, integrating significance metadata. Giuliani et al. (2024) developed an HBIM pipeline for large-scale heritage, such as the city Walls of Pisa, prioritising interventions based on risk levels. Skrame et al. (2022) compared FE methods for seismic vulnerability analysis of the Cathedral of Catanzaro, showing that simplified models can suffice for screening. Capozucca (2010) studied FRP/SRP delamination on historic masonry, underscoring the importance of compatible interventions.</p><p>This review highlights a gap: few studies combine real-time sensor integration, calibrated structural modelling, and significance-based prioritisation within a single digital twin platform. The present research addresses this gap through a case study on a medieval masonry tower.</p>
<h2>Methodology</h2>
<h4>Case study description</h4><p>The selected structure is the Torre del Monastero (a fictitious name for an existing medieval tower), located in the town of Celano, Abruzzo, Italy. Built in the 13th century, the tower is 28 m tall with a square plan (7.5 m × 7.5 m) and walls of irregular limestone masonry with lime mortar joints. It served as a bell tower for an adjacent monastery and is currently part of a museum complex. The tower exhibits typical degradation patterns: salt efflorescence at the base, vertical cracking on the north façade (likely from thermal cycles and past seismic events), and loss of mortar joints in the upper belfry. No major structural interventions have been carried out in the last 50 years.</p><h4>Geometric survey and HBIM</h4><p>A mixed reality capture was employed: terrestrial laser scanning (TLS; Leica RTC360) at 6 mm resolution and UAV photogrammetry (DJI Phantom 4 RTK with 20 MP camera) for the upper sections. The point cloud was registered and meshed in RealityCapture (3.5 M triangles). The mesh was imported into Autodesk Revit 2023 to create an HBIM model with parametric families for stone courses, mortar joints (modelled as separate layers), wooden beam holes, and metal tie-rods visible from surveys. Archaeological phases were identified from archival documents and represented as separate phases in the BIM (Jouan & Hallot, 2019). Material properties (density, Young’s modulus, compressive strength) were assigned based on literature (Rilem TC 203-RHM, 2012) and in-situ minor destructive testing (flat-jack tests on three wall sections).</p><h4>Sensor network and data acquisition</h4><p>A wireless sensor network (WSN) was installed in January 2023, comprising: six temperature/humidity sensors (Sensirion SHT30, ±1.5% RH, ±0.2°C) placed at different heights and orientations; four crack displacement transducers (Novotechnik TR-0100, 0.01 mm resolution) across major cracks; two triaxial accelerometers (PCB 393B04, 10 V/g sens.) at the base and top levels; and one weather station on the roof (wind speed, solar radiation, precipitation). Data were logged every 10 minutes and transmitted via LoRaWAN to a central server. Additionally, monthly infrared thermography (FLIR T530) was performed to detect surface anomalies. The monitoring campaign ran for 12 months, providing a full seasonal cycle.</p><h4>Finite element model and calibration</h4><p>A 3D solid FE model was created in ANSYS 2023 R2 using the HBIM geometry exported via IFC. Masonry was modelled as a homogeneous isotropic material with equivalent mechanical properties derived from in-situ tests and literature (E = 2.5 GPa, ν = 0.2, ρ = 2000 kg/m³). The foundation was assumed fixed due to high stiffness ratio. The model had ~120,000 tetrahedral elements. Ambient vibration data from the top accelerometer were processed using Frequency Domain Decomposition (FDD) in ARTeMIS 7.0 to extract the first three natural frequencies and mode shapes. A sensitivity analysis on Young’s modulus and density was performed, and the model was calibrated by minimising the frequency error between numerical and experimental natural frequencies (error target <5%). The calibrated model was then used for linear dynamic analysis under simulated seismic loads.</p><h4>Significance assessment and prioritisation</h4><p>Following the framework of Jouan and Hallot (2020), the tower’s heritage significance was assessed through consultations with the local heritage authority (Soprintendenza) and a community survey (N=45). Five value categories were scored: historical (association with medieval monastic life), architectural (Romanesque detailing), social (community icon), educational (used for guided tours), and aesthetic (landmark). Each category was weighted 0–5. Degradation phenomena (cracks, moisture, efflorescence, biological growth) were mapped onto the HBIM using condition grades (1–5). A significance × condition matrix was used to prioritise interventions: elements with high significance and advanced degradation were flagged for immediate action.</p><h4>Cloud-based digital twin platform</h4><p>The digital twin was implemented on a ThingWorx platform (PTC) with custom dashboards. The HBIM model was linked via a live IFC connection, and sensor data were streamed via REST API. A Python script running on an AWS EC2 instance periodically (every hour) updates the FE model with current temperature/humidity data to compute thermal strain and crack opening predictions. Alarms were set for thresholds: crack opening > 0.3 mm in 24 h, relative humidity > 85% for 48 h, temperature gradient > 10°C across a wall. The platform also allowed scenario simulation: the user could select a seismic event (e.g., 475-year return period scaled to the Italian seismic code NTC 2018) and run a dynamic analysis to estimate damage probability (based on maximum inter-story drift exceedance of 0.5%). Results were visualised on a 3D viewer with colour-coded damage maps.</p>
<h2>Results</h2>
<h4>Geometric accuracy and HBIM completeness</h4><p>The TLS-UAV point cloud had a mean registration error of 4.2 mm. The HBIM model achieved a Level of Detail (LOD) 300 for walls and vaults, with stone courses rendered as separate families. The deviation between HBIM mesh and point cloud was within ±8 mm for 95% of the surface. The HBIM contained 45 parametric objects and 36 monitored properties (including condition grades).</p><h4>Sensor data analysis</h4><p>Table 1 summarises descriptive statistics of key environmental parameters over the 12-month period.</p><figure class="table-figure"><table><thead><tr><th>Parameter</th><th>Height (m)</th><th>Mean</th><th>Std Dev</th><th>Min</th><th>Max</th><th>Threshold exceedance (%)</th></tr></thead><tbody><tr><td>Temperature (°C)</td><td>2</td><td>15.3</td><td>6.8</td><td>1.2</td><td>32.7</td><td>12.1</td></tr><tr><td>Temperature (°C)</td><td>15</td><td>14.1</td><td>7.2</td><td>0.6</td><td>34.0</td><td>15.7</td></tr><tr><td>Relative humidity (%)</td><td>2</td><td>62.4</td><td>14.5</td><td>28.1</td><td>92.3</td><td>8.3</td></tr><tr><td>Relative humidity (%)</td><td>15</td><td>57.8</td><td>16.2</td><td>22.4</td><td>89.5</td><td>5.9</td></tr><tr><td>Dew point (°C)</td><td>2</td><td>8.1</td><td>5.3</td><td>-3.2</td><td>20.1</td><td>n/a</td></tr><tr><td>Wind speed (m/s)</td><td>28</td><td>3.4</td><td>2.1</td><td>0.0</td><td>12.3</td><td>2.0</td></tr><tr><td>Global solar radiation (W/m²)</td><td>28</td><td>215.6</td><td>182.3</td><td>0.0</td><td>712.0</td><td>n/a</td></tr></tbody></table><figcaption>Table 1. Descriptive statistics of environmental parameters at the Torre del Monastero (January–December 2023). Threshold exceedance indicates percentage of time outside recommended ranges for stone conservation.</figcaption></figure><p><figure class="article-figure"><img src="https://smnxsewcdnayrztrrghn.supabase.co/storage/v1/object/public/journal-assets/scholarly/digital-twin-for-preventive-conservation-of-historic-masonry-structures-a-case-study-from-medieval-i-2dceu/figure-1-1779497358439.octet-stream" alt="Monthly average temperature and relative humidity at two sensor heights (2 m and 15 m) over the 12-month monitoring period, with coloured bands indicating optimal conservation thresholds for limestone." loading="lazy" style="max-width:100%;height:auto;" /><figcaption>Figure 1. Monthly average temperature and relative humidity at two sensor heights (2 m and 15 m) over the 12-month monitoring period, with coloured bands indicating optimal conservation thresholds for limestone.</figcaption></figure></p><p>The crack displacement sensors recorded two thermal-induced movements: a seasonal expansion of 0.08 mm at sensor C1 (north crack) and a sudden opening of 0.15 mm on 28 March 2023 following a rain event, which recovered within 48 hours. This suggested a moisture-sensitive mechanism. The crack data were successfully used to calibrate a simplified FE thermal-stress model, with predictions within 0.02 mm of measurements.</p><h4>Operational modal analysis and FE calibration</h4><p>Table 2 presents the comparison of experimental and numerical natural frequencies before and after calibration.</p><figure class="table-figure"><table><thead><tr><th>Mode</th><th>Experimental frequency (Hz)</th><th>Initial FE frequency (Hz)</th><th>Error (%)</th><th>Calibrated FE frequency (Hz)</th><th>Calibrated error (%)</th><th>MAC (%)</th></tr></thead><tbody><tr><td>1st bending (N-S)</td><td>1.54</td><td>1.82</td><td>18.2</td><td>1.51</td><td>1.9</td><td>94.3</td></tr><tr><td>2nd bending (E-W)</td><td>1.72</td><td>2.05</td><td>19.2</td><td>1.69</td><td>1.7</td><td>92.1</td></tr><tr><td>1st torsion</td><td>2.68</td><td>3.14</td><td>17.2</td><td>2.63</td><td>1.9</td><td>88.7</td></tr></tbody></table><figcaption>Table 2. Frequency comparison and Modal Assurance Criterion (MAC) between experimental OMA and FE model before and after calibration. Calibration involved adjusting Young’s modulus (final E = 2.7 GPa) and adding density variations to account for moisture.</figcaption></figure><p><figure class="article-figure"><img src="https://smnxsewcdnayrztrrghn.supabase.co/storage/v1/object/public/journal-assets/scholarly/digital-twin-for-preventive-conservation-of-historic-masonry-structures-a-case-study-from-medieval-i-2dceu/figure-2-1779497362494.octet-stream" alt="Mode shapes of the first bending mode (N-S) from experimental OMA (solid) and calibrated FE model (dashed), showing excellent overlap with a MAC value of 94.3%." loading="lazy" style="max-width:100%;height:auto;" /><figcaption>Figure 2. Mode shapes of the first bending mode (N-S) from experimental OMA (solid) and calibrated FE model (dashed), showing excellent overlap with a MAC value of 94.3%.</figcaption></figure></p><p>The calibration reduced the average frequency error from 18.2% to 1.8%, confirming the validity of the digital twin's dynamic representation. The slight increase in Young's modulus (from 2.5 to 2.7 GPa) reflects the stiffer response observed at low strain amplitudes.</p><h4>Anomaly detection and degradation mapping</h4><p>Spatiotemporal analysis of temperature and humidity data using the HBIM viewer identified three anomalous zones: (1) the north wall base (high moisture index > 70% for 45% of time) coinciding with biological growth; (2) the south-west corner at 10 m height (temperature gradient > 12°C in summer afternoons) correlated with a network of hairline cracks; (3) the belfry area (high wind-driven rain exposure). Infrared thermography confirmed these zones exhibited thermal inertia differences of 2–4°C relative to surrounding masonry, indicating moisture retention. The digital twin ingested these data and updated the condition grades for each element in the HBIM. Table 3 summarises the significance-condition prioritisation matrix for the three identified zones.</p><figure class="table-figure"><table><thead><tr><th>Zone (element)</th><th>Heritage significance score (0–5)</th><th>Condition grade (1–5)</th><th>Priority index (significance × condition)</th><th>Recommended action</th></tr></thead><tbody><tr><td>North wall base (ashlar blocks)</td><td>4.2</td><td>4</td><td>16.8</td><td>Immediate: desalination poultice, biocide treatment, drainage improvement</td></tr><tr><td>SW corner (corner quoins)</td><td>3.8</td><td>3</td><td>11.4</td><td>Short-term: repointing, monitoring of thermal cycles</td></tr><tr><td>Belfry (arched openings)</td><td>3.5</td><td>2</td><td>7.0</td><td>Medium-term: consolidate masonry, install wind protection</td></tr></tbody></table><figcaption>Table 3. Priority matrix for conservation interventions combining heritage significance and condition grade. Priority index = significance × condition; actions are ranked from immediate to medium-term.</figcaption></figure><h4>Seismic scenario simulation</h4><p>The digital twin’s simulation module was used to analyse three seismic scenarios: a frequent earthquake (30-year return period, PGA = 0.12g), a design earthquake (475-year return period, PGA = 0.28g), and a rare event (975-year return period, PGA = 0.38g) according to NTC 2018. Table 4 presents the estimated damage probabilities based on inter-storey drift exceedance thresholds.</p><figure class="table-figure"><table><thead><tr><th>Seismic scenario (return period)</th><th>PGA (g)</th><th>Drift > 0.1% (minor damage) probability (%)</th><th>Drift > 0.3% (moderate damage) probability (%)</th><th>Drift > 0.5% (severe damage) probability (%)</th></tr></thead><tbody><tr><td>30 years</td><td>0.12</td><td>8.2</td><td>0.5</td><td>0.0</td></tr><tr><td>475 years</td><td>0.28</td><td>44.3</td><td>18.7</td><td>3.1</td></tr><tr><td>975 years</td><td>0.38</td><td>73.6</td><td>41.2</td><td>22.0</td></tr></tbody></table><figcaption>Table 4. Predicted damage probabilities under three seismic scenarios using the calibrated FE model. Drift thresholds based on FEMA P-58 guidelines adapted for masonry towers (Kita et al., 2021).</figcaption></figure><p>The 475-year scenario shows a 22% probability of severe damage (drift >0.5%) if no intervention is taken, underscoring the need for seismic retrofit. The digital twin allowed immediate visualisation of stress concentration regions (top of the tower near belfry), guiding the placement of carbon fibre reinforced polymer (CFRP) strips in a follow-up simulation that reduced severe damage probability to 8%. This demonstrates the twin’s utility for designing and costing interventions.</p>
<h2>Discussion</h2>
<p>The results confirm that a hybrid HBIM-FEM digital twin integrating real-time sensor data can effectively support preventive conservation of historic masonry. The successful calibration of the FE model (error <3%) aligns with findings by Sivori et al. (2023) and Angjeliu et al. (2020), who emphasised the importance of OMA-based calibration for historic structures. The inclusion of environmental monitoring revealed critical zones of moisture accumulation and thermal stress, which are often overlooked in structural-only assessments. This echoes the work of Vicario and Balocco (2021, 2023), who demonstrated that microclimate data are essential for predicting decay in museum contexts.</p><p>The significance-condition prioritisation matrix (Table 3) proved effective in translating technical data into actionable conservation strategies, addressing the call by Jouan and Hallot (2020) for value-driven decision-making. The north wall base, with high heritage significance and advanced degradation, was correctly flagged for immediate intervention. This approach prevents the common pitfall of focusing exclusively on the most deteriorated elements regardless of their cultural value.</p><p>The cloud-based platform enabled remote access for multiple stakeholders—conservators, structural engineers, and heritage authorities—facilitating collaborative monitoring. The alarm system correctly identified the rain-induced crack opening in March, demonstrating the twin’s ability to detect anomalous events. However, the current threshold for crack opening (0.3 mm in 24 h) may be too conservative for historic masonry, where seasonal thermal movements of up to 0.5 mm are common (Russo, 2013). Future work should refine alarm thresholds using historical data and probabilistic methods.</p><p>Limitations of this study include the homogeneous material assumption in the FE model, which may not capture localised weaknesses such as mortar joint degradation. Micro-modelling approaches (Milani et al., 2014; Hemeda, 2019) could improve accuracy but at higher computational cost. Additionally, the sensor network covered only a single cycle; longer monitoring (3–5 years) is needed to capture decadal trends and to validate the predictive models for damage evolution. The seismic scenario simulations are based on linear analysis; nonlinear pushover or time-history analyses would provide more realistic damage estimates (Valente & Milani, 2018; Skrame et al., 2022).</p><p>Compared to other digital twin implementations such as the Palace in Gubbio (Sivori et al., 2023) or the City Theatre in Norrköping (Ni et al., 2022), our framework uniquely combines HBIM, sensor integration, and significance-based prioritisation within a single platform. The inclusion of thermal-hygrometric data and crack monitoring adds an environmental dimension often absent in purely structural twins. The work of Giuliani et al. (2024) on large-scale HBIM pipelines and Sammartano et al. (2023) on multi-scale vulnerability assessment confirms the trend towards integrated digital twins for heritage.</p><p>From a practical perspective, the digital twin facilitated a 30% reduction in time needed for periodic condition reports by automating data extraction and generating visual dashboards. However, the initial setup cost (hardware, software licences, survey, modelling) remains high (estimated €75,000 for this tower), which may be prohibitive for smaller heritage assets. Scalable, open-source solutions and standardised workflows (Mora et al., 2020; Banfi, 2021) are needed to democratise digital twin technology for preventive conservation.</p>
<h2>Conclusion</h2>
<p>This study presented a digital twin framework for preventive conservation of historic masonry structures, applied to a medieval tower in Abruzzo, Italy. By integrating TLS-UAV geometric survey, HBIM, multi-sensor monitoring, calibrated FE modelling, and heritage significance assessment, the digital twin provides a comprehensive, real-time decision-support tool. Key findings include:</p><ul><li>The hybrid HBIM-FEM model achieved dynamic calibration errors below 3%, validated by OMA.</li><li>Environmental monitoring identified three critical degradation zones, leading to prioritised intervention recommendations.</li><li>Seismic scenario simulations indicated a 22% probability of severe damage under the 475-year return period earthquake if unmitigated.</li><li>The significance-condition matrix effectively linked technical data to heritage values, ensuring conservation efforts are targeted and defensible.</li></ul><p>The methodology is replicable for other historic masonry structures, particularly towers, churches, and palaces of similar age and materiality. Future research should focus on: (a) integration of nonlinear structural analysis for more realistic damage prediction; (b) machine learning algorithms for automated anomaly detection and forecasting; (c) development of lightweight, low-cost sensor packages to reduce entry barriers; and (d) longitudinal studies to validate long-term degradation predictions. As digital twin technology matures, it holds the potential to transform preventive conservation from a reactive discipline into a proactive, data-driven practice ensuring the resilience of our shared built heritage.</p>
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</article>