Collimated beam ultrasound systems are a technology for imaging inside multi-layered structures such as geothermal wells. These systems work by using a collimated narrow-band ultrasound transmitter that can penetrate through multiple layers of heterogeneous material. A series of measurements can then be made at multiple transmit frequencies. However, commonly used reconstruction algorithms such as Synthetic Aperture Focusing Technique (SAFT) tend to produce poor quality reconstructions for these systems both because they do not model collimated beam systems and they do not jointly reconstruct the multiple frequencies. In this article, we propose a multi-frequency ultrasound model-based iterative reconstruction (UMBIR) algorithm designed for multi-frequency collimated beam ultrasound systems. The combined system targets reflective imaging of heterogeneous, multi-layered structures. For each transmitted frequency band, we introduce a physics-based forward model to accurately account for the propagation of the collimated narrow-band ultrasonic beam through the multi-layered media. We then show how the joint multi-frequency UMBIR reconstruction can be computed by modeling the direct arrival signals, detector noise, and incorporating a spatially varying image prior. Results using both simulated and experimental data indicate that multi-frequency UMBIR reconstruction yields much higher reconstruction quality than either single frequency UMBIR or SAFT.

Non-destructive testing is a cornerstone of structural integrity assessment that enables internal evaluation of materials without inflicting damage. Among various imaging methods, Ultrasonic Model-Based Iterative Reconstruction (UMBIR) has gained attention for its ability to enhance ultrasonic imaging by incorporating physical and statistical priors. Notably, its extension, Multi-Frequency Ultrasonic Model-Based Iterative Reconstruction (MF-UMBIR) to process multi-frequency datasets have shown improved accuracy over traditional techniques. However, its performance has some limitations in highly complex structures. This paper presents Deep Multi-Agent Consensus Equilibrium (Deep-MACE) method, a novel reconstruction framework that integrates multi-frequency forward model agents with a deep learning prior using the consensus equilibrium formulation. By combining data consistency across different excitation frequencies with the expressive power of a learned U-Net prior, Deep-MACE enables high-fidelity imaging in acoustically heterogeneous environments. The primary objective is to reconstruct images that reveal structural damage, such as corroded rebars and delaminations, thereby supporting non-invasive Structural Health Monitoring (SHM) of steel-lined concrete structures. Experimental results demonstrate that both UMBIR and MF-UMBIR suffer from limitations in defect visibility and robustness when applied to steel-lined concrete structures. In contrast, Deep-MACE consistently produces clearer reconstructions, successfully identifying all internal rebars with fewer artifacts and improved spatial resolution. These results highlight the potential of integrating deep priors into multi-agent frameworks for advanced ultrasonic non-destructive testing.

The present study proposes an optical fiber sensor for humidity measurement. The sensor fabrication utilizes a femtosecond laser to expose the fiber core region to specific dimensions. The fiber-exposed core section becomes sensitive to the surrounding environment. The humidity variations in the surroundings of the sensing zone have a greater influence on the optical power propagation in the fiber. As the humidity level increases, the accumulation of water vapor over the sensor section creates variable refractive index values between air and water. In the results, a variation in optical power intensity is noted at the sensor output. The obtained sensor response supports the sensor design for effectively measuring the humidity level from (20% to 80%) either in ascending or descending levels. The sensor, capable a withstanding 60% humidity variation, exhibits a significant sensitivity of 1.078 μW/%. The R ² values for humidity level variation are found to be approximately 0.9878, indicating a very linear response. Furthermore, the sensor has a rapid time response of 0.1 s, enabling it to detect variations in humidity levels quickly. The sensor’s humidity measuring capability with significant sensitivity, linearity, and fast response is competing with existing traditional techniques of humidity measurement.

Ensuring the durability of reinforced concrete structures is a central challenge in structural health monitoring (SHM). Corrosion of embedded steel rebars is among the most common and damaging deterioration mechanisms, yet its early detection remains difficult due to the complexity of wave propagation in concrete and the presence of strong clutter from surface layers and structural heterogeneity. Ultrasonic imaging provides a promising nondestructive tool, but conventional reconstruction approaches often suffer from limited contrast and high background interference. In this work, a novel reconstruction approach, termed Baseline Cancellation UMBIR (BACU), is proposed to effectively mitigate baseline-related artifacts while maintaining high image fidelity. The method integrates baseline subtraction into the ultrasonic model-based iterative reconstruction (UMBIR) framework, suppressing dominant direct arrivals and low-rank background responses while preserving target-related signals. This formulation improves robustness to modeling errors and limited array coverage, both of which are common in practical SHM applications. Synthetic experiments based on a concrete specimen with an embedded rebar demonstrate the advantages of the proposed approach compared to the conventional UMBIR. The proposed method achieves stronger suppression of clutter, sharper localization of the rebar, and improved preservation of contrast across varying corrosion levels. These results provide more reliable and interpretable reconstructions, advancing the use of ultrasonic imaging for nondestructive evaluation, and long-term monitoring of critical infrastructure.