Abdollah Malekjafarian

This paper presents a novel approach to the drive-by Structural Health Monitoring (SHM) of bridges based on crowdsourced data collected from multiple vehicle passages. The methodology is experimentally validated on a laboratory-scale bridge model designed to replicate the dynamic behaviour of real bridge structures, exhibiting representative natural frequencies and mode shapes. Acceleration data were acquired using smartphones mounted on a specially adapted vehicle traversing the bridge under varying conditions, including different speeds, directions, and lateral positions. Multiple crossings were conducted to simulate a crowdsourcing scenario, where independent vehicle measurements contribute to a shared monitoring database. This experimental setup enabled the evaluation of how data aggregation from numerous runs can enhance the robustness and accuracy of the drive-by monitoring technique. The collected acceleration signals were processed in MATLAB to extract the bridge’s key dynamic parameters. The analysis focused on estimating natural frequencies and identifying mode shapes based on the aggregated datasets. The results were compared with reference measurements obtained from high-precision accelerometers permanently installed on the bridge model. The identified parameters showed only minor deviations—typically within a few percent—from the reference values, confirming the reliability of the proposed approach. Furthermore, combining data from multiple vehicle passages reduced measurement variability and improved the stability of the identified dynamic characteristics. The findings demonstrate the feasibility and potential of using crowdsourced data for drive-by bridge monitoring with widely available mobile sensors. By leveraging repeated measurements from multiple independent crossings, the study highlights a scalable, low-cost, and non-invasive solution for continuous bridge health assessment. This approach paves the way toward practical implementation of large-scale SHM systems capable of exploiting existing traffic as a dynamic sensing network.

Drive-by bridge health monitoring provides a low-cost means of assessing bridge integrity through vehicle-mounted sensors. Yet, its effectiveness is limited by data scarcity and the high noise of field measurements. This study investigates how different data augmentation strategies—including physics-guided synthesis, time-series generative adversarial networks (TimeGAN), and stochastic noise—can enhance the accuracy and robustness of drive-by damage detection. These augmented datasets were validated using vibration data from the MITICA (Monitoring Transport Infrastructures with Connected and Automated Vehicles) testbed bridge in Ispra, Italy. Comparative experiments show that appropriate augmentation can improve damage classification accuracy by more than 10%, under low-sample and noisy conditions. The findings demonstrate that carefully designed augmentation can mitigate data scarcity and substantially boost the reliability of AI-driven drive-by monitoring systems.

Aging civil infrastructure presents growing risks to public safety and structural resilience. Although structural health monitoring (SHM) has proven highly effective in monitoring and assessing critical assets, its broader implementation is hindered by upfront costs and the practical difficulties of deploying dedicated monitoring systems. Meanwhile, today’s cities generate massive, continuous data streams from connected vehicles, mobile devices, satellites, surveillance cameras, fiber-optic cables, and many other systems, originally designed for purposes like transportation, utilities, and security. A growing body of research shows that such urban data sources can be repurposed to infer structural health conditions across infrastructure networks more practically, while also supporting the objectives of the Smart & Connected Communities and Infrastructure Systems and People paradigms. Despite increasing interest, this field has lacked a unifying identity and systematic structure. Thus, this article attempts to formalize this domain of repurposing existing sensing mechanisms for infrastructure health monitoring under the term Latent Infrastructure Sensing (LIS). “Latent” emphasizes that valuable structural health information is already embedded in everyday urban data flow but remains largely concealed/underutilized/unexplored. In this article, we define the conceptual boundaries of LIS, propose a general evaluation criterion grounded in Reliability, Effectiveness, Actionability, and Practicality, and introduce the LIS Intelligence Framework that traces raw latent data through processing stages to decision-ready insights. We further synthesize existing LIS research into distinct categories (with representative studies), including crowdsensing via vehicles and mobile devices, satellite interferometry, stationary camera analytics, and emerging infrastructure sensing using fiber-optic cables, while highlighting key methodological gaps as well as socio-technical and governance challenges. We also identify promising future opportunities, such as sensing enabled by delivery robots, electric vehicle charging roads, and smart home devices. Hence, this article lays the foundation for scalable, practical, and potentially cost-effective infrastructure health monitoring that leverages data already coursing through contemporary cities.

Ireland was an early offshore wind pioneer, with Arklow Bank Phase 1 commissioned in 2004 as one of the world’s first commercial offshore wind farms (OWFs). Despite this early start, offshore wind development (OWD) in Ireland remained limited for almost two decades. In recent years, however, the Government of Ireland has declared ambitious offshore renewable energy (ORE) targets, aiming to deliver up to 37 GW of capacity by 2050. One of the key constraints during this period has been the absence of a coherent and integrated marine planning and consenting framework capable of supporting large-scale ORE. This paper examines the evolution of Ireland’s ORE planning and consenting regime, tracing the transition from fragmented, largely “developer-led” arrangements toward a more coordinated and “state-led” framework. It reviews key legislative and policy developments, including the National Marine Planning Framework, the Maritime Area Planning (MAP) Act 2021, the establishment of the Maritime Area Regulatory Authority (MARA), and the introduction of Designated Maritime Area Plans (DMAPs), particularly the South Coast DMAP. The paper also situates Ireland’s recent reforms within selected leading European jurisdictions, highlighting persistent challenges related to governance coordination, permitting complexity, and regulatory sequencing in offshore wind deployment in Ireland.

Selecting the most effective location for a single accelerometer is a critical challenge in the structural health monitoring (SHM) of wind turbine blades (WTBs), where high installation costs, restricted access, and strong environmental and operational variability (EOV) often limit instrumentation. This study proposes a fully unsupervised, temperature-aware data-driven framework for determining the optimal accelerometer position directly from healthy-state vibration data, enabling reliable monitoring under ambient excitation. This study addresses this challenge using a full-scale dataset from a Vestas V27 wind turbine blade instrumented with 12 accelerometers. The complete multi-sensor configuration is employed to form the basis of a systematic, data-driven investigation into optimal sensor placement under realistic operating conditions. The framework begins with a frequency-domain fusion (FDF) stage, where a discrete wavelet transform (DWT) identifies a frequency split, separating low- and high-frequency regimes. Continuous wavelet transform (CWT) coefficients are then computed within each regime to capture both global blade dynamics and localized transient responses. Features from all sensors are merged and ranked using two complementary unsupervised feature selection strategies: the Laplacian score, which preserves local geometric structure, and a temperature-aware statistical filter that penalizes environmentally sensitive features. A sensor-level attribution analysis traces high-ranking features back to their source sensors across multiple compression thresholds, identifying the most consistently informative location at each operational speed (32 RPM and 43 RPM). An autoencoder trained exclusively on healthy-state data from the selected sensor is then deployed for anomaly detection, with reconstruction error serving as the damage index. The results reveal that, in both regimes, the accelerometer located at the leading edge near the mid-span provides the most stable and sensitive response to early-stage damage. Notably, the single optimally placed sensor achieves anomaly-detection performance comparable to that of the full twelve-sensor network, demonstrating a scalable and cost-efficient pathway toward interpretable, field-deployable SHM of operational wind turbines.

In structural health monitoring (SHM), the scarcity of labeled data representing different structural states and structural configurations remains a key challenge for developing robust and generalizable deep learning models. Building upon a reported attention-enhanced star generative adversarial network with gradient penalty (AT-StarGAN-GP), this study examines its application to generating synthetic structural vibration signals that reflect both cross-structure domain differences and multiple localized structural state scenarios. The framework supports conditional translation across domains, learning from acceleration responses measured on a source structure to generate synthetic responses for a target structure under structural state conditions. The architecture integrates multi-type attention mechanisms, including self-attention, channel-attention, and spatial-attention, and evaluates four conditioning strategies: concatenation (Concat), feature-wise linear modulation (FiLM), adaptive instance normalization (AdaIN), and conditional instance normalization (CIN). A dual-conditional formulation based on joint location and structural state level is employed. The framework is evaluated using two small-scale physical bridge models, each instrumented at six joint locations, with structural state variations simulated at selected joints using three mass levels (no mass, low mass, and high mass). Evaluation across modal, temporal, distributional, and spectral metrics shows that CIN provides improved performance, achieving a modal assurance criterion of 0.894, frequency response assurance criterion of 0.872, the lowest distributional discrepancy (maximum mean discrepancy 0.0006), and an 86% pass rate in Kolmogorov–Smirnov tests. Attention mechanisms improve localization accuracy by 34.35% and reduce entropy by 6.98%, while self-attention reduces cross-domain Kullback-Leibler (KL) divergence by 24.5%. The AdaIN variant shows more consistent behavior under unseen-domain scenarios (MAC 0.8207–0.8405). All evaluated configurations retain over 94% of baseline performance under 25% noise. Five-fold cross-validation confirms training stability, and Friedman–Nemenyi testing indicates statistically significant separation from weaker baselines, although differences relative to mid-tier methods remain limited in some comparisons. Overall, the results indicate that the extended AT-StarGAN-GP framework can generate structurally consistent synthetic vibration responses across different structures and state conditions under controlled laboratory settings.