Open research questions in Infrastructure Maintenance and Monitoring

What the literature leaves open

• Further research is needed to enhance the computational speed of the KNN algorithm. The study of UAV-based SHM for linear infrastructures is essential to ensuring their service continuity.

  Unmanned Aerial Vehicle-Based Structural Health Monitoring and Computer Vision-Aided Procedure for Seismic Safety Measures of Linear Infrastructures · 2024 · DOI

• Prior works have focused on vibration-based SHM of buildings or bridges, but gaps exist for linear infrastructure systems - Practical and methodological gaps exist specifically for linear infrastructure systems such as pipelines

  Unmanned Aerial Vehicle-Based Structural Health Monitoring and Computer Vision-Aided Procedure for Seismic Safety Measures of Linear Infrastructures · 2024 · DOI

• Despite extensive research on condition rating and deterioration modeling of concrete bridges, a comprehensive and comparative understanding of these processes remains underexplored.

  Review of Condition Rating and Deterioration Modeling Approaches for Concrete Bridges · 2025 · DOI

• Although advancements in computer vision have enabled automated infrastructure inspections, research dedicated specifically to the inspection of guardrails remains scarce.

  Enhancing Road Safety on US Highways: Leveraging Advanced Computer Vision for Automated Guardrail Damage Detection and Evaluation · 2025 · DOI

• Moreover, a standardized maintenance index is currently lacking for planning repair work in Seoul.

  Development of predictive and evaluation models for the retro reflectivity performance of pavement lane markings · 2025 · DOI

• Future research will focus on further improving cross-platform interoperability to expand DT applications in infrastructure management.

  Bridge Digital Twin for Practical Bridge Operation and Maintenance by Integrating GIS and BIM · 2024 · DOI

• This study demonstrates the great potential of the innovative instance-based transfer learning method in pavement performance prediction of a region’s lack of data.

  Novel Instance-Based Transfer Learning for Asphalt Pavement Performance Prediction · 2024 · DOI

• The assessment results demonstrate that the improved fusion method in this study can deal with conflicting evidence better than existing fusion methods.

  Improved Evidence Fusion Theory for the Safety Assessment of Prestressed Concrete Bridges · 2024 · DOI

• Finally, an improved evidence fusion method was proposed based on the evidence credibility and preprocessing corrections for highly conflicting evidence.

  Improved Evidence Fusion Theory for the Safety Assessment of Prestressed Concrete Bridges · 2024 · DOI

• The study identifies the need for dedicated model fine-tuning for cracks on retaining walls. The study acknowledges the challenge of false detections in regions with dense vegetation.

  AI-Based Quantification of Crack Geometry on Retaining Walls from Mobile Earth-Observation Imagery · 2026 · DOI

• Vegetation, branches, leaves, and shadows often produce false detections, making it difficult for AI models to distinguish real cracks from environmental interference.

  AI-Based Quantification of Crack Geometry on Retaining Walls from Mobile Earth-Observation Imagery · 2026 · DOI

• Extending the proposed method to large-scale applications. Considering the intermittent nature of traffic flow and the high operational flexibility of UAVs to control the influence of traffic flow. Investigating the impacts of local meteorological conditions and pavement surface states on spectral observations.

  Assessment of Asphalt Pavement Skid Resistance Using Ground-Based and UAV-Based Hyperspectral Synergy · 2026 · DOI

• Current skid resistance detection methods are inefficient, costly, and limited in coverage. There is a lack of methods that can achieve efficient, contactless, and large-scale assessment of pavement skid resistance. Traditional methods are inadequate for large-scale road network monitoring.

  Assessment of Asphalt Pavement Skid Resistance Using Ground-Based and UAV-Based Hyperspectral Synergy · 2026 · DOI

• An important limitation is that our analysis does not in- corporate the additional complexity of reporting standards such as EN13508-2 (CEN 2003), since SewerML labels are not based on this coding system.

  Multi-Class Sewer Defect Detection with Vision-Language Models · 2026 · DOI

• A practical solution would be to design VLM outputs that combine textual explanations with real-time image highlighting of the perceived defect through bounding boxes or segmentation masks.

  Multi-Class Sewer Defect Detection with Vision-Language Models · 2026 · DOI

• The issues of low recognition accuracy, false detection, and missed detection in highway pavement crack identification. The need for a more efficient and accurate pavement crack detection method.

  Research on Pavement Crack Identification Method Based on Improved YOLOv11 · 2026 · DOI

• The visualization results section appears incomplete ('it can be seen from Figure 5 that the original YOLOv11 model exhibited issues of false dete'), and no discussion of false positive/negative cases or failure modes is provided.

  Research on Pavement Crack Identification Method Based on Improved YOLOv11 · 2026 · DOI

• Future research directions emphasize autonomous digital twins, robotics and UAV-assisted inspection, blockchain-based data security, and interdisciplinary collaboration. The use of artificial intelligence and machine learning in predictive maintenance and automated BIM model updates is also a potential area of future research.

  Integration of BIM and SHM For Sustainable Management of Existing Structures: A Systematic Review · 2026 · DOI

• 100. Best Applications 102. Immediate visuali- zation, high tem- poral resolution 106. Structured storage, analytical flexibility 110. Interoperability, modularity 114. Continuous moni- toring, predictive maintenance 103. Software-specific, limited scalability 104. Small to medium structures, pilot studies 107. Reduced real-time re- 108. Large infrastructure net- sponsiveness works 111. Additional computa- tional complexity 115. Bandwidth, security, visualization chal- lenges 112. Multi-platform projects, col- laborative workflows 116. Critical infrastructure, bridges, high-rise buildings This comparison illustrates that no single method is universally optimal; instead, in- tegration should be selected based on project scale, monitoring requirements, and stake- holder needs. Future research could explore hybrid approaches, combining the scalability of database-driven methods with the responsiveness of IoT streaming. 9.3 Strengths and Weaknesses of Current Research Current research in BIM–SHM integration demonstrates several notable strengths:  Enhanced Structural Safety: Continuous monitoring enables early detection of structural anomalies, reducing failure risk.  Predictive Maintenance and Lifecycle Optimization: SHM data integrated into BIM supports proactive interventions, lowering maintenance costs and material usage.  Sustainability Contributions: Integration facilitates energy optimization, material efficiency, and supports green certification initiatives.  Emerging AI Applications: Machine learning models improve prediction ac- curacy, anomaly detection, and automated BIM updates. However, several weaknesses remain, limiting broader adoption:  Standardization Deficiencies: Lack of universal protocols and semantic models inhibits interoperability across platforms and projects.  Data Management Challenges: High-frequency sensor data can overwhelm storage and analytics systems, creating bottlenecks.  Financial and Operational Constraints: High installation costs, mainte- nance, and training requirements limit adoption, especially for smaller pro- jects. 1204 1205 1206 1207 1208 1209 1210 1211 1212 1213 1214 1215 1216 1217 1218 1219 1220 1221 1222 1223 1224 1225 1226 1227 1228 1229 1230 1231 1232 1233 1234 1235 1236 1237 1238 1239 Fusion Journal of Engineering & Sciences 2025, 17, x https://doi.org/10.3390/xxxxx 1240 1241 1242 1243 1244 1245 1246 1247 1248 1249 1250 1251 1252 1253 1254 1255 1256 1257 1258 1259 1260 1261 1262 1263 1264 1265 1266 1267 1268 1269 1270 1271 1272 Fusion Journal of Engineering & Sciences 2025 30 of 38  Human Factors: Limited expertise in both BIM and SHM systems creates skill gaps and affects decision-making.  Limited Real-World Validation: Many approaches are tested in pilot or con- trolled settings, with limited deployment in operational infrastructure.

  Integration of BIM and SHM For Sustainable Management of Existing Structures: A Systematic Review · 2026 · DOI

• Low detection efficiency of traditional manual inspection methods. Safety issues for workers operating at great heights. The need for extensive preparatory work for traditional manual inspection methods.

  A hybrid approach based on deep feature extraction and machine learning classification for structural damage detection in concrete structures · 2026 · DOI

• Future investigations will focus on optimizing these hybrid structures for edge-computing devices and expanding the dataset to include multi-scale damage types in diverse environmental settings to further enhance urban resilience. Future investigations may focus on incorporating more deep learning structures and optimization strategies to improve crack detection.

  A hybrid approach based on deep feature extraction and machine learning classification for structural damage detection in concrete structures · 2026 · DOI

• The complexity and fast-changing nature of encroachment threats. The limitations of traditional inspection directives and conventional surveying techniques. The need for rapid identification of hazards and improved emergency response.

  Application of Drone Remote Sensing in Fast Detection of Encroachment Threats in the Vicinity of Urban Rail Transit Lines: Use in Emergency Operations · 2026 · DOI

• The lack of effective methods for rapid identification of hazards in urban rail transit lines. The limitations of traditional inspection directives and conventional surveying techniques.

  Application of Drone Remote Sensing in Fast Detection of Encroachment Threats in the Vicinity of Urban Rail Transit Lines: Use in Emergency Operations · 2026 · DOI

• Potholes are a major global challenge for infrastructure management and transportation safety. The lower accuracy of YOLOv7 can lead to missed potholes, increasing long-term repair costs. The need for better road maintenance for safety considerations.

  Automated detection of road surface anomalies using image processing techniques · 2026 · DOI

• The traditional detection and maintenance modes for highway pavement have problems such as low efficiency and poor accuracy. There is a need for an integrated system of intelligent detection and preventive maintenance for highway pavement management.

  Integration of Intelligent Inspection and Preventive Maintenance Strategies for Expressway Pavement Management · 2026 · DOI

Most-cited papers in Infrastructure Maintenance and Monitoring

• Toward safer highways, application of XGBoost and SHAP for real-time accident detection and feature analysis · Accident Analysis & Prevention · 2019 · 1,004 citations
• Machine learning for structural engineering: A state-of-the-art review · Structures · 2022 · 677 citations
• A critical review and comparative study on image segmentation-based techniques for pavement crack detection · Construction and Building Materials · 2022 · 409 citations
• Crack Detection and Comparison Study Based on Faster R-CNN and Mask R-CNN · Sensors · 2022 · 342 citations
• Deep learning-based structural health monitoring · Automation in Construction · 2024 · 334 citations
• 3D vision technologies for a self-developed structural external crack damage recognition robot · Automation in Construction · 2024 · 279 citations
• DMA-Net: DeepLab With Multi-Scale Attention for Pavement Crack Segmentation · IEEE Transactions on Intelligent Transportation Systems · 2022 · 258 citations
• Real-time detection of cracks in tiled sidewalks using YOLO-based method applied to unmanned aerial vehicle (UAV) images · Automation in Construction · 2023 · 222 citations
• Crack detection and quantification for concrete structures using UAV and transformer · Automation in Construction · 2023 · 210 citations
• Automatic detection method of tunnel lining multi‐defects via an enhanced You Only Look Once network · Computer-Aided Civil and Infrastructure Engineering · 2022 · 207 citations

Most recent work

• A visual attention-guided approach for concrete crack detection in complex environments · Engineering Applications of Artificial Intelligence · 2026
• Implementation of an ıntelligent vision approach for automated structural defect detection · Discover Artificial Intelligence · 2026
• Enhancing crack detection and severity assessment in historical Tabiya basins using U-Net and adaptive thresholding · Frontiers in Artificial Intelligence · 2026
• Wave-PINN: a wavelet-based physics-informed neural network for continuous pavement roughness evaluation from vehicle dynamics · Mechanical Systems and Signal Processing · 2026
• A maintenance-aware machine learning framework for network-level highway pavement condition prediction · Computer-Aided Civil and Infrastructure Engineering · 2026
• An end-to-end automated framework for pavement crack detection and dimensional quantification using foundation models and learned skeletonization · Engineering Applications of Artificial Intelligence · 2026
• FCS-Net: A frequency-aware dual-path transformer for pixel-level crack detection and quantitative width measurement in Shihe Bridge inspection · Structures · 2026
• Pothole detection and characterization: integrated segmentation and depth estimation in road anomaly systems · Neural Computing and Applications · 2026
• QualNet: Qualitive knowledge and small data-driven neural network for performance assessment of reinforced concrete specimens · Engineering Structures · 2026
• A Lightweight UAV-Mounted Metrology System for Standards-Aligned Metric Crack Width Measurement in Reinforced Concrete Bridges · Metrology · 2026

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