Non-destructive Technique Research Articles

Detection and characterization of defects in the Brancacci Chapel wall paintings via holographic interferometry and microwave reflectometry

This paper evaluates the improvements in wall paintings diagnostic achievable by the synergistic combination of DHSPI, SIRT and MWR techniques. These innovative portable and non-destructive techniques for in-situ diagnostic were tested in laboratory on mock-ups mimicking the real conditions of wall painting defects (such as detachments, cavities and cracks), and on the frescoes painted by Masolino, Masaccio and Filippino Lippi (between 1422 and 1475) in the Brancacci Chapel in the church of Santa Maria del Carmine in Florence, Italy. This research activity was part of the Brancacci Chapel diagnostic and monitoring project, where the assessment of the state of conservation is a mandatory prerequisite for planning the subsequent restoration work. In common practice, restorers make the preliminary evaluation on the state of conservation of the wall paintings by visual and tactile inspection, without the use of any special scientific instrumentation. Cracks, cavities, lack of material, detachments, out-of-plumb and deformation of (non-)structural elements are reported in a condition report to document the state of conservation at a given point in time. In particular, the detachments are recognized by the tap test, but this is a discretionary and highly subjective method. Thus, there is a strong demand for the development of a portable and non-destructive approach that refines this preliminary survey with objective data. Here, we present the results produced by the combination of MWR (useful for the dimensional characterization of hidden defects in the wall) and DHSPI-SIRT to identify the extent of deformations induced by thermal stimulus, providing an objective confirmation of the preliminary investigation performed by restorers, in which the detachments were indicated as “stable”, “not very stable” and “unstable”, i.e., close to falling. The integrated use of these techniques in situ, supported by a laboratory study on ad-hoc prepared mock-ups, proved to be suitable for a quantitative evaluation of damage risk to guide restoration strategy accordingly.

Evaluation of Structural Stability of Four-Storied building using Non-Destructive Testing Techniques

This paper mainly dealt with the evaluation of the structural stability of four storied building using non-destructive on-destructive testing methods. During the construction stage, there are many tests available to assess the excellence of concrete. The quality of concrete mainly depends on the quality of materials, concrete grade, and water-cement ratio. In the case of existing structures, to check the quality of concrete destructive tests are not possible, meanwhile, concrete quality will be assessed by using non-destructive testing (NDT) techniques such as rebound hammer, ultrasonic pulse velocity (UPV) etc. In this present study, an attempt has been made to check the quality of concrete in an existing four-storied building using non-destructive testing methods such as rebound hammer test and ultrasonic pulse velocity test. Moreover, the stability of the structure was also assessed. Non-destructive testing method was chosen since existing information of the structure was unavailable. Test results showed that the basement (B1) was susceptible to corrosion, and the compressive strength was not in the recommended range. Ultrasonic pulse velocity (UPV) results also proved that the average quality of the concrete was poor. Hence, significant suggestions were given for necessary retrofitting measures to improve the stability of the structure.

Autofinding egg parasitoids in moth eggs by using machine learning methods in synchrotron-coherent X-ray imaging

The use of a non-destructive technique, such as the propagation-based X-ray phase contrast radiography (PBR) can be an innovative method for automatic parasitism analysis, especially if it presents standard structures. Herein, an artificial intelligence (AI) model is applied in order to establish a computer vision of egg parasitoids in PBRs of parasitized moth eggs acquired at the Synchrotron Radiation for Medical Physics (SYRMEP) beamline at ELETTRA. PBRs of eggs parasitized in four different stages of parasitism (0 days, 3 days, 5 days and 7 days) have been tested. The AI model performance was evaluated by using different metrics. Average Precision (AP), which measures the accuracy of object detection, was found to be 0.866 and 0.741 for the moth eggs and for the parasites, respectively. Additionally, we found that as stage of parasitism becomes longer, the accuracy of parasitism detection also increases (76 % at 7 days).

Raman spectroscopic imaging – practical applications in industrial pharmacy and medical diagnostics: a review.

Currently, Raman imaging as a non-destructive technique providing knowledge on structural characterization of the investigated samples is gaining more and more interest among industrial formulation and process scientists. In the pharmaceutical sector, the creation of novel products and formulations necessitates flexible and adaptable analysis, and increasingly also imaging capabilities. In this paper, the attention is directed toward the research progress, that has occurred in the application of non-invasive Raman chemical imaging techniques in industrial pharmacy and medical diagnostics in recent years. This work aims to explore the potential of Raman imaging as a knowledge discovery method for the non-destructive evaluation of pharmaceutical formulations as well as human cells to support medical diagnostics in clinical practice. A review of the latest scientific papers indicates that the applications of Raman imaging in pharmaceutical sciences and medical diagnostics are diverse and far-reaching from ensuring the quality of pharmaceutical formulations to studying complex drug delivery systems, monitoring manufacturing processes, and medical diagnostics of cancers and other diseases. As technology continues to evolve, the integration of Raman imaging into pharmaceutical and clinical practices is expected to expand, providing new insights and solutions to the challenges faced by the pharmaceutical industry and medical diagnostics.

Nondestructive Inspection and Quantification of Select Interface Defects in Honeycomb Sandwich Panels.

Honeycomb sandwich panels are utilized in many industrial applications due to their high bending resistance relative to their weight. Defects between the core and the facesheet compromise their integrity and efficiency due to the inability to transfer loads. The material system studied in the present paper is a unidirectional carbon fiber composite facesheet with a honeycomb core with a variety of defects at the interface between the two material systems. Current nondestructive techniques focus on defect detectability, whereas the presented method uses high-frequency ultrasound testing (UT) to detect and quantify the defect geometry and defect type. Testing is performed using two approaches, a laboratory scale immersion tank and a novel portable UT system, both of which utilize only single-side access to the part. Coupons are presented with defects spanning from 5 to 40 mm in diameter, whereas defects in the range of 15-25 mm and smaller are considered below the detectability limits of existing inspection methods. Defect types studied include missing adhesive, unintentional foreign objects that occur during the manufacturing process, damaged core, and removed core sections. An algorithm is presented to quantify the defect perimeter. The provided results demonstrate successful defect detection, with an average defect diameter error of 0.6 mm across all coupons studied in the immersion system and 1.1 mm for the portable system. The best accuracy comes from the missing adhesive coupons, with an average error of 0.3 mm. Conversely, the worst results come from the missing or damaged honeycomb coupons, with an error average of 0.7 mm, well below the standard detectability levels of 15-25 mm.

VNIR–SWIR reflectance spectroscopy as a nondestructive technique for compositional determination of archaeological talc samples with a machine learning approach

The material from which an archaeological piece is made provides a great deal of information regarding the society in which it was made; thus, any misidentification can lead to erroneous conclusions. The uniqueness of many of these pieces hinders their mineralogical analysis because the pieces cannot be damaged for sampling; therefore, errors in the classification of these materials are common. In the present study, we evaluate the suitability of the spectroradiometry technique in the analysis of two archaeological talc pieces. Both came from the Iron Age archaeological site of Peña del Castro (La Ercina, León) in the northwestern Iberian Peninsula. We compared the spectral curves of these 2 pieces with the spectral curves from 50 talc geological samples from different geographical sources, combining bulk and powdered samples. Our results show that spectral curves enabled the identification of the talc material in the powdered and bulk samples. Moreover, the absence of the talc characteristic features in other soft materials also used in antiquity enabled the detection of incorrect identification of the classified pieces. Even though our results cannot be used to define any absorption feature characteristic to establish the provenance of the material, in the present study, spectral analysis has been shown to be suitable as a nondestructive technique to mineralogically identify archaeological pieces.

Autonomous Image-Based Corrosion Detection in Steel Structures Using Deep Learning.

Steel structures are susceptible to corrosion due to their exposure to the environment. Currently used non-destructive techniques require inspector involvement. Inaccessibility of the defective part may lead to unnoticed corrosion, allowing the corrosion to propagate and cause catastrophic structural failure over time. Autonomous corrosion detection is essential for mitigating these problems. This study investigated the effect of the type of encoder-decoder neural network and the training strategy that works the best to automate the segmentation of corroded pixels in visual images. Models using pre-trained DesnseNet121 and EfficientNetB7 backbones yielded 96.78% and 98.5% average pixel-level accuracy, respectively. Deeper EffiecientNetB7 performed the worst, with only 33% true-positive values, which was 58% less than ResNet34 and the original UNet. ResNet 34 successfully classified the corroded pixels, with 2.98% false positives, whereas the original UNet predicted 8.24% of the non-corroded pixels as corroded when tested on a specific set of images exclusive to the investigated training dataset. Deep networks were found to be better for transfer learning than full training, and a smaller dataset could be one of the reasons for performance degradation. Both fully trained conventional UNet and ResNet34 models were tested on some external images of different steel structures with different colors and types of corrosion, with the ResNet 34 backbone outperforming conventional UNet.

Infrared–Resin Crack Measurement and Preventive Work Technology for Maintenance

The use of thermography as a nondestructive evaluation technique is increasingly popular for maintaining concrete structures. Most inspections merely evaluate the locations and shapes of defects on surfaces. To address this shortcoming, it proposes an inspection method and preventive work using a coating-type resin sensor combined with an infrared camera. No method has been developed to assess the depth of defects. In this approach, infrared-reactive resin is applied. Thermographic images of the target area are captured sequentially. Temperature curves obtained at each pixel during the cooling process are analyzed using Fourier transform to differentiate defect states in various parts of the temperature distribution. The temperature change is found to be correlated with the defect size. Approximately 5% aluminum powder is mixed into the applied gel resin. Because of its specific gravity, it tends to concentrate in areas damaged by compression failure or to float. This report discusses technologies related to identification of defects and measuring their size in infrared-reactive resin, with examination of the effectiveness of measures to prevent scattering and collapse of defects caused by structural degradation. A concentric loading test on reinforced concrete columns confined by gel resin ties is described herein. Test variables include concrete compressive strength of 232–244 N/mm<sup>2</sup>, both below and above the equipment hole that caused the defect, and to measure the relation, a comparison with test specimens that are free of defects.

Toxicity and magnetometry evaluation of the uptake of core-shell maghemite-silica nanoparticles by neuroblastoma cells.

Nanoparticle uptake by cells is a key parameter in their performance in biomedical applications. However, the use of quantitative, non-destructive techniques to obtain the amount of nanoparticles internalized by cells is still uncommon. We have studied the cellular uptake and the toxicity of core-shell maghemite-silica magnetic nanoparticles (MNPs), with a core diameter of 9 nm and a shell thickness of 3 nm. The internalization of the nanoparticles by mouse neuroblastoma 2a cells was evaluated by sensitive and non-destructive Superconducting Quantum Interference Device (SQUID) magnetometry and corroborated by graphite furnace atomic absorption spectroscopy. We were thus able to study the toxicity of the nanoparticles for well-quantified MNP uptake in terms of nanoparticle density within the cell. No significant variation in cell viability or growth rate was detected for any tested exposure. Yet, an increase in both the amount of mitochondrial superoxide and in the lysosomal activity was detected for the highest concentration (100 μg ml-1) and incubation time (24 h), suggesting the onset of a disruption in ROS homeostasis, which may lead to an impairment in antioxidant responses. Our results validate SQUID magnetometry as a sensitive technique to quantify MNP uptake and demonstrate the non-toxic nature of these core-shell MNPs under our culture conditions.

Optical Modification of a Nanoporous Alumina Structure Associated with Surface Coverage by the Ionic Liquid AliquatCl.

This manuscript analyses changes in the optical parameters of a commercial alumina nanoporous structure (AnodiscTM or AND support) due to surface coverage by the ionic liquid (IL) AliquatCl (AlqCl). XPS measurements were performed for chemical characterization of the composite AND/AlqCl and the AND support, but XPS resolved angle analysis (from 15° to 75°) was carried out for the homogeneity estimation of the top surface of the ANDAlqCl sample. Optical characterization of both the composite AND/AlqCl and the AND support was performed by three non-destructive and non-invasive techniques: ellipsometry spectroscopy (SE), light transmittance/reflection, and photoluminescence. SE measurements (wavelength ranging from 250 nm to 1250 nm) allow for the determination of the refraction index of the AND/AlqCl sample, which hardly differs from that corresponding to the IL, confirming the XPS results. The presence of the IL significantly increases the light transmission of the alumina support in the visible region and reduces reflection, affecting also the maximum position of this latter curve, as well as the photoluminescence spectra. Due to these results, illuminated I-V curves for both the composite AND/AlqCl film and the AND support were also measured to estimate its possible application as a solar cell. The optical behaviour exhibited by the AND/AlqCl thin film in the visible region could be of interest for different applications.

Assessment of the effect of embedded RFID tag on a composite laminate strength

RFID (Radio Frequency Identification) is commonly used to monitor goods along the supply chain. The feasibility of the application of a RFID tag to monitor CFRP components was demonstrated in a previous work by some of the authors. This work is aimed at evaluating how the integration of a RFID tag inside a CFRP laminate affects the quasi-static strength and the failure mode. Finite element modelling is used to design a specimen with an embedded tag, that may be representative of the tensile state of stress in a larger laminate. Tensile testing is performed both up to failure and by interrupting tests at different load levels in order to inspect the specimen by C-scan. Acoustic Emission (AE) and Digital Image Correlation (DIC) are used as additional non-destructive monitoring techniques. Some interrupted tests were selected to extract micrographic sections that illustrate damage development in the laminate. The extensive destructive and non-destructive characterization allowed to quantify the effect of embedding a RFID tag in the laminate in terms of strength decrease and failure mode.

Development of deep learning based user-friendly interface for fruit quality detection

The implementation of deep learning algorithms has contributed to various applications related to the detection of fruit quality. The quality attributes of fruit such as total soluble solids, moisture content, pH, colour changes, and firmness at different varieties can be predicted according to different storage conditions with reliable classification accuracy. The advances in non-destructive techniques have led to the rapid utilisation of the imaging approach in order to monitor the fruit quality. These image datasets encompass diverse information which requires extensive data extraction. To overcome this issue, a deep learning approach using convolutional neural network was used to evaluate the fruit quality. A graphical user interface-based software (DLFRUIT-GUI) for data processing of fruit quality is developed. The toolbox allows the model training and selection based on the image datasets of the fruit. The software offers a push-button approach to establish deep learning models for monitoring fruit quality. The adoption of convolutional neural network model successfully improves the model performance which demonstrated efficient results in predicting the fruit quality at different varieties according to various storage conditions. The DLFRUIT-GUI toolbox provides rapid monitoring of fruit quality detection that can easily be accessible by users who have no programming skills and tedious data analysis.

Identification of documented constructive stages of the San Ignacio Bridge (Aguascalientes, Mexico) using electrical resistivity tomography (ERT)

The non-destructive technique known as Electrical Resistivity Tomography (ERT) has been used in the analysis of historical buildings in recent years. It provides insights into the internal structure of the structural elements, such as layer thicknesses, and potential irregularities. In this investigation the ERT technique was applied to the ancient San Ignacio Bridge, located in the state of Aguascalientes, Mexico. The resulting resistivity profiles unveil the primary construction stages of the bridge, which corroborate and enhance the historical information regarding its construction phases. Additionally, the profiles indicate the presence of moisture concentrations in the pavement fillings, which could be attributed to rainwater infiltration. The usefulness of the ERT technique applied to edified patrimony is demonstrated in this study.

Alrecon: computed tomography reconstruction web application based on Solara

Synchrotron X-ray computed tomography is a non-destructive 3D imaging technique that offers the possibility to study the internal microstructure of samples with high spatial and temporal resolution. Given its unmatched image quality and acquisition speed, and the possibility to preserve the specimens, there is an increasing demand for this technique, from scientific users from innumerable disciplines. Computed tomography reconstruction is the computational process by which experimental radiographs are converted to a meaningful 3-dimensional image after the scan. The procedure involves pre-processing steps for image background and artifact correction on raw data, a reconstruction step approximating the inverse Radon-transform, and writing of the reconstructed volume image to disk. Several open-source Python packages exist to help scientists in the process of tomography reconstruction, by offering efficient implementations of reconstruction algorithms exploiting central or graphics processing unit (CPU and GPU, respectively), and by automating significant portions of the data processing pipeline. A further increase in productivity is attained by scheduling and parallelizing demanding reconstructions on high performance computing (HPC) clusters. Nevertheless, visual inspection and interactive selection of optimal reconstruction parameters remain crucial steps that are often performed in close interaction with the end-user of the data. As a result, the reconstruction task involves more than one software. Graphical user interfaces are provided to the user for fast inspection and optimization of reconstructions, while HPC resources are often accessed through scripts and command line interface. We propose Alrecon, a pure Python web application for tomographic reconstruction built using Solara. Alrecon offers users an intuitive and reactive environment for exploring data and customizing reconstruction pipelines. By leveraging upon popular 3D image visualization tools, and by providing a user-friendly interface for reconstruction scheduling on HPC resources, Alrecon guarantees productivity and efficient use of resources for any type of beamline user.

High-energy computed tomography for inspection and monitoring of flexible riser

Flexible risers have been widely used in the offshore oil and gas industry and have many applications, such as production, gas lifting, or gas and water injection. The structure comprises several metal and polymer layers, which allow it to withstand a sizeable bending curve and high internal pressure. Therefore, guaranteeing the integrity of flexible risers throughout their useful life is extremely important. It requires developing and applying effective inspection and monitoring techniques, particularly emphasizing the tensile armor layers. This work evaluated the ability of the High-Energy Computed Tomography (CT) Technique to detect internal defects such as cracks, breaks, and fissures in the riser layers. A flexible riser sample of approximately 2 m in length and 281 mm in external diameter was scanned using a tomography system consisting of a 6 MeV Betatron and a set of 14-bit A-Si digital detectors. The images were acquired in 360° with angular steps of 2°. After acquisition, the images were reconstructed, and 3D models were built for qualitative and quantitative evaluation. Through the segmentation process, the metal layers of the flexible riser could be analyzed individually. Defects were found in the tensile armor layers, indicating ruptures and cracks and the onset of pitting. The results strongly suggest that computed tomography can be used as a non-destructive technique for inspecting flexible risers, with good efficiency in detecting defects, and that its application can be extended to field tests.

Simultaneous Determination of Four Catechins in Black Tea via NIR Spectroscopy and Feature Wavelength Selection: A Novel Approach.

As a non-destructive, fast, and cost-effective technique, near-infrared (NIR) spectroscopy has been widely used to determine the content of bioactive components in tea. However, due to the similar chemical structures of various catechins in black tea, the NIR spectra of black tea severely overlap in certain bands, causing nonlinear relationships and reducing analytical accuracy. In addition, the number of NIR spectral wavelengths is much larger than that of the modeled samples, and the small-sample learning problem is rather typical. These issues make the use of NIRS to simultaneously determine black tea catechins challenging. To address the above problems, this study innovatively proposed a wavelength selection algorithm based on feature interval combination sensitivity segmentation (FIC-SS). This algorithm extracts wavelengths at both coarse-grained and fine-grained levels, achieving higher accuracy and stability in feature wavelength extraction. On this basis, the study built four simultaneous prediction models for catechins based on extreme learning machines (ELMs), utilizing their powerful nonlinear learning ability and simple model structure to achieve simultaneous and accurate prediction of catechins. The experimental results showed that for the full spectrum, the ELM model has better prediction performance than the partial least squares model for epicatechin (EC), epicatechin gallate (ECG), epigallocatechin (EGC), and epigallocatechin gallate (EGCG). For the feature wavelengths, our proposed FIC-SS-ELM model enjoys higher prediction performance than ELM models based on other wavelength selection algorithms; it can simultaneously and accurately predict the content of EC (Rp2 = 0.91, RMSEP = 0.019), ECG (Rp2 = 0.96, RMSEP = 0.11), EGC (Rp2 = 0.97, RMSEP = 0.15), and EGCG (Rp2 = 0.97, RMSEP = 0.35) in black tea. The results of this study provide a new method for the quantitative determination of the bioactive components of black tea.

Robust Algorithms for the Analysis of Fast-Field-Cycling Nuclear Magnetic Resonance Dispersion Curves

Fast-Field-Cycling (FFC) Nuclear Magnetic Resonance (NMR) relaxometry is a powerful, non-destructive magnetic resonance technique that enables, among other things, the investigation of slow molecular dynamics at low magnetic field intensities. FFC-NMR relaxometry measurements provide insight into molecular motion across various timescales within a single experiment. This study focuses on a model-free approach, representing the NMRD profile R1 as a linear combination of Lorentzian functions, thereby addressing the challenges of fitting data within an ill-conditioned linear least-squares framework. Tackling this problem, we present a comprehensive review and experimental validation of three regularization approaches to implement the model-free approach to analyzing NMRD profiles. These include (1) MF-UPen, utilizing locally adapted L2 regularization; (2) MF-L1, based on L1 penalties; and (3) a hybrid approach combining locally adapted L2 and global L1 penalties. Each method’s regularization parameters are determined automatically according to the Balancing and Uniform Penalty principles. Our contributions include the implementation and experimental validation of the MF-UPen and MF-MUPen algorithms, and the development of a “dispersion analysis” technique to assess the existence range of the estimated parameters. The objective of this work is to delineate the variance in fit quality and correlation time distribution yielded by each algorithm, thus broadening the set of software tools for the analysis of sample structures in FFC-NMR studies. The findings underline the efficacy and applicability of these algorithms in the analysis of NMRD profiles from samples representing different potential scenarios.

Spontaneous-stimulated Raman co-localization dual-modal analysis approach for efficient identification of tumor cells

The study of highly heterogeneous tumor cells, especially acute myeloid leukemia (AML) cells, usually relies on invasive analytical methods such as morphology, immunology, cytogenetics, and molecular biology classification, which are complex and time-consuming to perform. Mortality is high if patients are not diagnosed in a timely manner, so rapid label-free analysis of gene expression and metabolites within single-cell substructures is extremely important for clinical diagnosis and treatment. As a label-free and non-destructive vibrational detection technique, spontaneous Raman scattering provides molecular information across the full spectrum of the cell but lacks rapid imaging localization capabilities. In contrast, stimulated Raman scattering (SRS) provides a high-speed, high-resolution imaging view that can offer real-time subcellular localization assistance for spontaneous Raman spectroscopic detection. In this paper, we combined multi-color SRS microscopy with spontaneous Raman to develop a co-localized Raman imaging and spectral detection system (CRIS) for high-speed chemical imaging and quantitative spectral analysis of subcellular structures. Combined with multivariate statistical analysis methods, CRIS efficiently differentiated AML from normal leukocytes with an accuracy of 98.1 % and revealed the differences in the composition of nuclei and cytoplasm of AML relative to normal leukocytes. Compared to conventional Raman spectroscopy blind sampling without imaging localization, CRIS increased the efficiency of single-cell detection by at least three times. In addition, using the same approach for further identification of AML subtypes M2 and M3, we demonstrated that intracytoplasmic differential expression of proteins is a marker for their rapid and accurate classifying. CRIS analysis methods are expected to pave the way for clinical translation of rapid tumor cell identification.

The Li battery digital twin – Combining 4D modelling, electro-chemistry, neutron, and ion-beam techniques

Knowledge driven materials and component design is key for improving the performance of Li-ion batteries and solving the remaining hurdles for next-generation battery concepts like all-solid-state batteries. While the spatial and time dependent distribution of Li would help elucidating performance bottlenecks and degradation phenomena, only a few analysis techniques are available, due to the unique nature of Li, especially as Li+-ion. In fact, only two non-destructive techniques with good time resolution can combine spatial information with absolute quantification of Li, one being Neutron Depth Profiling (NDP), the other Ion-Beam-Analysis (IBA). While both exploit nuclear processes, the information gained is complementary. NDP provides high depth resolution, but only limited lateral resolution, whereas IBA has high lateral, but only limited depth resolution. In this study, we benchmark both techniques for the first time using a set of Li-battery test-samples and show the strengths and synergies of both techniques. The derived information regarding the depth dependent Li-concentration is then used to validate a microstructure resolved continuum model of charge, discharge, and relaxation behavior of the cells and electro-chemical analysis. This fundamental work demonstrates a new route to optimize Li batteries on material and component level by combining advanced characterization and digital twin modelling.

Utilizing Optimized Machine Learning Techniques to Predict the Compressive Strength of Concrete through Non-Destructive Testing Methodologies

Background:: Examining the concrete quality in its original location and optimizing machine learning models for precise forecasting of concrete compressive strength(fc) is crucial. Current research advocates the fine tuning of hyperparameters within machine learning methodologies in tandem with non-destructive testing techniques to forecast the compressive strength of concrete. Objective:: This study aimsto incorporate age as a crucial factor by utilizing data spanning from 3 days to 365 days. This approach enhances the study’s applicability for real-time forecasting purposes. Methods:: In the methodology of this current research, three machine learning (ML) models— specifically, Multi-Linear Regression (MLR), Decision Tree Regressor (DTR), and Random Forest Regressor (RFR)—are introduced within the context of age as a significant factor influencing measurements obtained from the Rebound Hammer (RN) and Ultra Sonic Pulse Velocity (UPV). These ML models were sequentially applied, followed by a meticulous process of hyperparameter finetuning conducted through grid search Cross-Validation (CV). To gain insights into the predictive results, the study also employed SHapley Additive exPlanations (SHAP) for interpretation purposes. Results:: The results of this study reveal the development of an empirical relationship using Multi- Linear Regression, which yielded an R2 value of 0.88. Furthermore, the evaluation showed that Random Forest Regression outperformed other models with an R2 value of 0.95 in the training and 0.92 in the testing datasets. These models hold promise for facilitating decisions about qualitative analyses based on UPV and Rebound Hammer measurements relative to the age of the concrete. Rigorous validation of the models was conducted through standard cross-validation techniques. Conclusion:: The research has created and validated hyper tunned machine learning models with the help of grid search cross-validation function, with Random Forest Regression being the most effective. These models can potentially guide decisions regarding qualitative analyses using UPV and Rebound Hammer measurements concerning concrete age. They provide a valuable tool for on-site assessments in construction and structural evaluations. The primary objective of the research is to introduce age as a significant feature. To achieve this, data ranging from 3 days to 365 days was integrated. This inclusion aims to enhance real-time decision-making in construction processes, facilitating actions like the prompt removal of formwork in high-speed construction projects.