# Infrastructure Maintenance and Monitoring

**Type:** Topics  
**Canonical URL:** https://scholariq.org/topics/infrastructure-maintenance-and-monitoring/

## Facts

| Field | Value |
| --- | --- |
| Description | This cluster of papers focuses on the automated inspection and maintenance of pavement and civil infrastructure using deep learning, image processing, and convolutional neural networks. The research covers topics such as crack detection, defect classification, road surface monitoring, bridge inspection, and infrastructure condition assessment. |
| Domain | Physical Sciences |
| Field | Engineering |
| OpenAlex ID | t11606 |
| Works | 84 |

## Topic papers all

Showing 15 of 84.

- [Analysis of Edge-Detection Techniques for Crack Identification in Bridges](https://scholariq.org/papers/analysis-of-edge-detection-techniques-for-crack-identification-in-bridges/)
- [Building Information Modelling (BIM) uptake: Clear benefits, understanding its implementation, risks and challenges](https://scholariq.org/papers/building-information-modelling-bim-uptake-clear-benefits-understanding-its/)
- [A deep hybrid learning model to detect unsafe behavior: Integrating convolution neural networks and long short-term memory](https://scholariq.org/papers/a-deep-hybrid-learning-model-to-detect-unsafe-behavior-integrating-convolution/)
- [Falls from heights: A computer vision-based approach for safety harness detection](https://scholariq.org/papers/falls-from-heights-a-computer-vision-based-approach-for-safety-harness-detection/)
- [Computer Vision Techniques in Construction: A Critical Review](https://scholariq.org/papers/computer-vision-techniques-in-construction-a-critical-review/)
- [Automated detection of workers and heavy equipment on construction sites: A convolutional neural network approach](https://scholariq.org/papers/automated-detection-of-workers-and-heavy-equipment-on-construction-sites-a/)
- [Perceived benefits of and barriers to Building Information Modelling (BIM) implementation in construction: The case of Hong Kong](https://scholariq.org/papers/perceived-benefits-of-and-barriers-to-building-information-modelling-bim/)
- [Correlating modal properties with temperature using long-term monitoring data and support vector machine technique](https://scholariq.org/papers/correlating-modal-properties-with-temperature-using-long-term-monitoring-data/)
- [Automatic congestion detection system for underground platforms](https://scholariq.org/papers/automatic-congestion-detection-system-for-underground-platforms/)
- [Internet of things for smart ports: Technologies and challenges](https://scholariq.org/papers/internet-of-things-for-smart-ports-technologies-and-challenges/)
- [LC-RNN: A Deep Learning Model for Traffic Speed Prediction](https://scholariq.org/papers/lc-rnn-a-deep-learning-model-for-traffic-speed-prediction/)
- [Fast Personal Protective Equipment Detection for Real Construction Sites Using Deep Learning Approaches](https://scholariq.org/papers/fast-personal-protective-equipment-detection-for-real-construction-sites-using/)
- [Natural language processing for smart construction: Current status and future directions](https://scholariq.org/papers/natural-language-processing-for-smart-construction-current-status-and-future/)
- [Wind Turbine Surface Damage Detection by Deep Learning Aided Drone Inspection Analysis](https://scholariq.org/papers/wind-turbine-surface-damage-detection-by-deep-learning-aided-drone-inspection/)
- [Underground sewer pipe condition assessment based on convolutional neural networks](https://scholariq.org/papers/underground-sewer-pipe-condition-assessment-based-on-convolutional-neural/)

## Topic primary papers

Showing 15 of 36.

- [Analysis of Edge-Detection Techniques for Crack Identification in Bridges](https://scholariq.org/papers/analysis-of-edge-detection-techniques-for-crack-identification-in-bridges/)
- [Automated detection of workers and heavy equipment on construction sites: A convolutional neural network approach](https://scholariq.org/papers/automated-detection-of-workers-and-heavy-equipment-on-construction-sites-a/)
- [Wind Turbine Surface Damage Detection by Deep Learning Aided Drone Inspection Analysis](https://scholariq.org/papers/wind-turbine-surface-damage-detection-by-deep-learning-aided-drone-inspection/)
- [Underground sewer pipe condition assessment based on convolutional neural networks](https://scholariq.org/papers/underground-sewer-pipe-condition-assessment-based-on-convolutional-neural/)
- [PCA-Based algorithm for unsupervised bridge crack detection](https://scholariq.org/papers/pca-based-algorithm-for-unsupervised-bridge-crack-detection/)
- [A Machine Learning Approach to Road Surface Anomaly Assessment Using Smartphone Sensors](https://scholariq.org/papers/a-machine-learning-approach-to-road-surface-anomaly-assessment-using-smartphone/)
- [DefectTR: End-to-end defect detection for sewage networks using a transformer](https://scholariq.org/papers/defecttr-end-to-end-defect-detection-for-sewage-networks-using-a-transformer/)
- [Automatic tunnel lining crack evaluation and measurement using deep learning](https://scholariq.org/papers/automatic-tunnel-lining-crack-evaluation-and-measurement-using-deep-learning/)
- [A semi-autonomous mobile robot for bridge inspection](https://scholariq.org/papers/a-semi-autonomous-mobile-robot-for-bridge-inspection/)
- [A robust instance segmentation framework for underground sewer defect detection](https://scholariq.org/papers/a-robust-instance-segmentation-framework-for-underground-sewer-defect-detection/)
- [Multiscale Attention Networks for Pavement Defect Detection](https://scholariq.org/papers/multiscale-attention-networks-for-pavement-defect-detection/)
- [Pixel-level tunnel crack segmentation using a weakly supervised annotation approach](https://scholariq.org/papers/pixel-level-tunnel-crack-segmentation-using-a-weakly-supervised-annotation/)
- [Deep learning-based masonry crack segmentation and real-life crack length measurement](https://scholariq.org/papers/deep-learning-based-masonry-crack-segmentation-and-real-life-crack-length/)
- [Towards a digital twin-based intelligent decision support for road maintenance](https://scholariq.org/papers/towards-a-digital-twin-based-intelligent-decision-support-for-road-maintenance/)
- [An imaging data model for concrete bridge inspection](https://scholariq.org/papers/an-imaging-data-model-for-concrete-bridge-inspection/)

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Source: ScholarIQ — public research metadata, principally OpenAlex. See https://scholariq.org/sources/ for provenance and https://scholariq.org/methodology/ for what these figures mean.