Condition assessment of civil engineering structures has been an active research area due to growing concerns over the safety of aged as well as new civil structures. Utilization of emerging immersive visualization technologies such as Virtual Reality (VR), Augmented Reality (AR), and Mixed Reality (MR) in the architectural, engineering, and construction (AEC) industry has demonstrated that these visualization tools can be paradigm-shifting. Extended Reality (XR), an umbrella term for VR, AR, and MR technologies, has found many diverse use cases in the AEC industry. Despite this exciting trend, there is no review study on the usage of XR technologies for the condition assessment of civil structures. Thus, the present paper aims to fill this gap by presenting a literature review encompassing the utilization of XR technologies for the condition assessment of civil structures. This study aims to provide essential information and guidelines for practitioners and researchers on using XR technologies to maintain the integrity and safety of civil structures.

Adversarial attacks pose serious traffic safety risks that could potentially impede the deployment of automated driving systems (ADS). Recent discoveries show that imperceptible perturbations (also referred to as adversarial attacks) to inputs of deep neural networks employed in these systems could impair their image recognition and prediction capabilities. Adversarial attacks can misguide advanced driving assistance systems (ADAS) to misclassify a traffic sign (e.g., classify a stop sign as a speed limit sign), causing the vehicle to accelerate instead of stop, which may potentially result in catastrophic incidents. This paper investigates encrypted invisible security patches placed on traffic signs and an onboard machine-vision system that can be integrated into the ADAS (i.e., ADS and cooperative adaptive cruise control systems). The security patches are essentially a visual representation of encrypted hash values generated via a one-way cryptographic algorithm. In this way, every traffic sign will be associated with a unique security pattern that can only be recognized by the proposed machine-vision technology. Additionally, these patches will be made invisible with the help of ultraviolet reflective sign technology. The proposed methodology aims to bring an additional security layer to sign recognition to enhance the reliability of ADAS. If a traffic sign is physically altered to attempt an adversarial threat, the security patch on the sign will not match with the sign information, and the driver will receive a warning to take control of the driving. For self-driving vehicles, a signal will be sent to the autonomous vehicle to enforce a safe driving action.

Manual inspection of constructed infrastructure assets remains labor-intensive, subjective, and poorly suited for large-scale automation across construction and maintenance lifecycles. Although vibration-based and artificial intelligence (AI)-driven methods offer potential for automated inspection, their adoption has been constrained by sensor deployment complexity, data-management burden, and limited integration with operational digital workflows. This paper presents a methodological framework for AI-enhanced connected structural health monitoring (SHM), whose primary novelty lies in integrating established methodological components, including vibration-based feature extraction, physics-informed modal feature engineering, conventional feedforward neural-network (NN) regressors, edge computing, vehicle-to-everything (V2X) middleware, and telematics-based visualization, into a unified V2X-enabled SHM workflow for automated condition assessment of traffic-signal mast-arm structures. Validation results demonstrate strong predictive performance, with coefficients of determination of 0.91, 0.98, and 0.70 for base, mid-arm, and mast-arm regions, respectively, and mean absolute errors of 0.038, 0.020, and 0.052, respectively, under realistic sensor noise. A data-driven alerting strategy, integrating isotonic regression and change-point detection, enables automated classification of normal, warning, and critical conditions, correctly identifying 86% of undamaged cases and 72% of critically damaged cases while conservatively flagging more than 99% of severe deterioration scenarios for intervention. The complete analytical pipeline, including signal processing, NN inference, and threshold evaluation, executes in under 5 ms per analysis window on central processing unit-only edge hardware, indicating computational feasibility for edge-based SHM without cloud dependency. By reusing existing digital infrastructure for sensing, analytics, and reporting, the proposed methodological framework has the potential to reduce manual inspection burden and provides a practical pathway for supporting construction inspection, post-construction verification, and infrastructure condition assessment. Field validation remains a necessary next step to evaluate long-term performance under real operational conditions.