Different Types Of Damage Research Articles

Design and Implementation of Small Modular Amphibious Robot System

Various marine engineering facilities have been eroded by marine organisms and wind waves for a long time, resulting in different types of damage to the surface of marine engineering facilities, such as the pile legs of offshore platforms. Therefore, in order to carry out safety inspections and other work on marine engineering facilities, a small amphibious robot structure system and a set of control systems adapted to it are independently developed. Various problems such as the modular design of the structure, composite motion mode, adsorption stability, wall adaptability of the crawling mode, and flaw localization have been solved by means of three-dimensional modeling, mechanical analysis, simulation, and electronic design. At the same time, a set of control systems including hardware and software is developed for the amphibious robot. In order to improve the stability and efficiency of the amphibious robot working underwater, a sliding mode control algorithm based on the exponential reaching law and saturation function is designed. For the fixed depth and fixed heading control functions, the sliding mode control algorithm and the PID control algorithm are simulated and compared. Finally, several types of experiments are carried out for the amphibious robot. The simulation and experimental results show that all the functions of the amphibious robot meet work requirements, such as the motion performance of the composite motion mode. Compared with the PID control algorithm, the sliding mode control algorithm has a faster response speed and better stability, which is conducive to the efficient and stable work of the amphibious robot underwater.

Structural Damage Detection under Ambient Excitation Using Symbolic Three-Order Square Matrix Formed by Specific-Interval-Sampled Time-Domain Signals.

In the structural health monitoring of vibration systems, varying excitation always affects the accuracy of damage identification. The proposed symbolic three-order square matrix damage detection method with the matrix norm as a damage indicator can solve the difficult problem of damage identification under ambient excitation. The new sampling pattern extracts data from signals in the time domain at specific intervals based on the structural properties with the help of the autocorrelation coefficient. Then, the data extracted are converted into symbols and arranged into a three-order square matrix, and the Frobenius norm of the matrix is used for structural damage identification as a reliable damage indicator. In this process, the transmissibility function is employed to eliminate the effects of varying excitation. First, the method was verified by a cracked simply supported beam-a simulated Abaqus model. Then, a wooden truss bridge in the laboratory and an actual engineering scenario under ambient excitation together demonstrated the effectiveness and accuracy of the damage identification method and proved the proposed method to be robust to different types of damage under ambient excitation. Compared with other related methods, this method is more intuitive and efficient.

Refined analysis of a supporting column assembly in a traditional Tibetan timber building and its limiting tilt angle before collapse

Traditional Tibetan architecture is an important part of the Chinese architecture. Many of the Tibetan structures are suffering from different types of damages after hundreds of years of service. The structural arrangement, types of damage and failure modes of these timber structures have been investigated on site. Tilting of structural members is found to be the most common type of damage. This paper studies the limiting inclination angle of a single-column supporting assembly of these structures before its collapse for proactive maintenance and restoration. A method to estimate the limiting tilt angle of the column is proposed based on the relationship between the lateral force, F, at the column head and the tilt angle, θ, of the column. Simplified analytical models of this column assembly in a traditional Tibetan timber building under in-plane and out-of-plane loadings respectively are proposed. The behavior of the constituting column foot joint, column-Queti joint and Queti-beam joint are analyzed in detail to obtain the global deformation and M-θ relationship of the column assembly under different loads. The F-θ relationship of the column assembly from the intact state to collapse is then obtained. A verified finite element model of the column assembly is adopted as reference to validate the performances of the proposed models throughout the loading process. Comparison of the F-θ curves obtained from the analytical models and the simulated results demonstrates the validity and accuracy of the proposed models. They may be adopted for a quick analysis of practical structures to estimate their limit states for health monitoring, maintenance and strengthening of structures.

Manufacturing of composites with designed failure

Abstract The failure of polymer composites is a very complex process, which mainly occurs as a result of interactions between different types of damage in the material, often at random locations and without any particular signs. To increase the reliability of composites and enable their wider use in safety-critical components, it is crucial to make their failure processes more controllable and predictable, which could be achieved by the local modification of the interfacial adhesion. The aim of our research is to develop and investigate a method for designing the failure process of polymer composites in terms of the location and the mode of failure. We produced interfacially engineered composites containing a weakened adhesion zone by adding polycaprolactone (PCL) thermoplastic interlayer material, which was applied directly on the surface of the reinforcing material by fused filament fabrication (FFF). Then, material tests were carried out, and the failure mode and position were evaluated as a function of the geometry of the interlayer material.

Deletion of YTHDF1 (not YTHDF3) reduced brain and gut damage after traumatic brain injury

ABSTRACT Objective: To determine whether YTHDF1 and YTHDF3 play the same role in brain and gut damage after traumatic brain injury (TBI). Methods: We generated YTHDF1-/- and YTHDF3-/- mice using CRISPR/Cas9 technology, established a mouse brain injury model through severe controlled cortical impact (CCI), and finally observed the different types of damage between YTHDF1-/- and YTHDF3-/- mice by analysing the levels of oedema proteins in cortical tissue and inflammatory proteins and histopathological lesions in brain and gut tissues in mice at 3 days after CCI. Result: Compared with WT mice, YTHDF1-/- mice had decreased levels of oedema in cortical tissue and inflammation and histopathological lesions in brain and gut tissues at 3 days post-CCI, but YTHDF3-/- mice did not. Conclusion: Our results suggest that deletion of YTHDF1, but not YTHDF3, could reduce damage to the brain and gut following TBI.

Influence of stiffeners on acoustic emission monitoring of ship structures

Naval vessels are valuable assets that are expensive to build and maintain. Predictive maintenance is crucial to enhance efficiency and operability and to extend the service life of these structures. Fatigue is regarded as one of the main damage modes affecting the structural integrity of ship structures. Fatigue cracks can develop at the intersections of stiffeners and other structural elements, without being detected until they substantially grow, introducing a degree of uncertainty in the estimation of the fatigue damage. Acoustic emission (AE) is a passive ultrasound method for the detection and localization of different types of damage. AE is sensitive to crack propagation and can cover large areas, making it suitable for structural health monitoring of ship hulls. However, for accurate fatigue crack detection via AE it is crucial to understand how ultrasound waves interact with structural members i.e. stiffeners and longitudinal frames. Dispersion, scattering, reflection, and multimodality have so far hindered the application of AE in this context. This study aims to assess ultrasound wave behavior in ship structures in the presence of structural elements such as stiffeners and longitudinal/transversal frames. It investigates the ultrasound wave transmission dependence on source frequency and angle of incidence using spectral finite element (SEM) simulations and experimental measurements. The experimental setup includes a 10mm thick steel plate with a stiffener and a longitudinal beam (Figure 1). Pairs of sensors are placed on each side of the stiffener to record the incoming and transmitted waves, and to determine the transmission coefficient. By combining the results of both simulation and experiments, an expression for the transmission coefficient as a function of different frequencies and angles is presented. The results of this study can be used to maximize the coverage of AE monitoring systems and enhance their sensitivity for detection of fatigue cracks on ship structures.

Damage discrimination on drone fuselage with OBR Based Fibre-Optic Networks

This paper presents a methodology for a structural health monitoring (SHM) system based on optical backscatter reflectometry (OBR) fibre optic networks. The study focuses on the use of machine learning methods to monitor the microdeformation of a drone fuselage and to detect and discriminate be-tween different types of damage when the structure is subjected to static loads. A total of 1500 points are measured for each load, providing a comprehensive understanding of the structural response. In addition, four types of damage, in-cluding partial disbonding at different locations, are induced with varying de-grees of extension to evaluate the sensitivity and accuracy of the SHM system. The results show that the OBR-based SHM system is capable of detecting and localising damage with high accuracy, indicating its potential as a reliable and effective tool for real-time structural health monitoring in aerospace applica-tions. The methodology presented in this study could be extended to other types of structures and damage scenarios, contributing to the development of more advanced and effective SHM systems. Keywords: Machine Learning, OBR sensor network, Unmanned Aerial Vehi-cle, Drone.

Cross-instar larval identification leads divergent decision on pest management: An example from white grubs on peanuts

Accurate larval identification is a prerequisite for integrated pest management, but remains a long-term difficulty in insect taxonomy. Previous descriptions are mainly focused on the most disruptive final instar larvae. However, earlier instars especially cross-instar larval morphological comparisons were rarely delivered hitherto. Polyphylla laticollis and Maladera orientalis are pests that damage peanut crops at various developmental stages. The first instar larvae of P. laticollis and the mature larvae of M. orientalis attack peanuts simultaneously and cause different types of damage. In this study, we compared the life histories and larval morphologies through field investigations and scanning electron microscopy (SEM) observations. These cross-instar white grubs are similar in sizes, but exhibit morphological differences on their mouthparts, thoracic legs, anal slits, and rasters. This study presents a novel approach to larval identification, with a goal of generating new insights into integrated pest control practices on white grubs.

Predicting the remaining useful life of rails based on improved deep spiking residual neural network

the remaining useful life (RUL) of rails is important to ensure safe and reliable operation of railway transportation lines. However, the severity of deterioration of rails from different types of damage varies. Multichannel vibrational data collected by sensors can be utilized to identify the relevant complex correlations and temporal relationships; however, existing methods struggle to predict rail damage accurately. Thus, this paper proposes a RUL prediction method for rails based on an improved deep spiking residual neural network. First, multichannel data collected by sensors are used directly as input to the predictive network without requiring a separate feature extraction step. Additionally, an encoding module is designed to adaptively convert the temporal sequences of data into spiking signals to reduce the information loss during the encoding process. Second, a spiking residual convolutional neural network (CNN) is incorporated into the proposed method to extract optimal features. The separable, spiking CNN can model the relationships among multichannel data accurately, and an attention mechanism is implemented to recalibrate the spiking feature maps such that the predictive network can distinguish between items of information effectively. Third, the spiking residual connections are altered to mitigate network degradation caused by the incompatibility between the conventional residual connections and the spiking neural network. Finally, the obtained spiking features are input to a fully connected layer to predict the rail RUL. Experimental results demonstrated that the proposed method can predict the rail RUL accurately, and validation results obtained on a rolling bearing dataset demonstrated the high generalizability of the proposed method.

Optical evaluation of internal damage to human hair based on second near-infrared window polarization microscopy.

Hair beauty treatments glorify human life. As a side effect, there is a risk of deteriorating the health of the hair. Optically polarized microscopy has been used for many decades to evaluate hair conditions owing to its ease of use and low operating costs. However, the low biopermeability of light hinders the observation of detailed structures inside hair. The aim of this study is to establish an evaluation technique of internal damages in a hair by utilizing a near-infrared (NIR) light with a wavelength of 1000-1600 nm, called "second NIR window". We built a laser scanning transmission microscope system with an indium gallium arsenide detector, a 1064 nm laser source, and optical circular polarization to visualize the anisotropy characterization of keratin fibres in hair. Samples of Asian black hair before and after bleaching, after permanent-waving, after lithium bromide (LiBr) treatment, and after heating was observed. Some parameters reflecting intra-hair damage were quantitatively compared with the parameters in digitally recorded images with analytical developments. The light transmittance of black hair was dramatically improved by utilizing the second NIR window. Numerical analysis of circular polarization in hair quantified the internal damage in chemically or thermally treated hair and found two different types of damage. The present method enabled quantitative evaluation of the condition changes in the cortex; for example, a decrease in circular polarizability by LiBr treatment and restoration by replacing the LiBr solution with water. In addition, black speckles were observed after the heat treatment. Longer heating and wetting times increased the appearance probability and size of the speckles. According to quantitative analyses, the emergence of black spots was independent of polarizability changes, indicating that they were not pores. Circular polarization microscopy based on near-infrared optics in the second NIR window provides an effective evaluation method for quantifying intra-hair damage caused by cosmetic treatments. The present method provides noninvasive, easy, and inexpensive hair evaluation and has potential as a gold standard in hair care research/medical fields.

A Physics-Based Model-Data-Driven Method for Spindle Health Diagnosis—Part III: Model Training and Fault Detection

Abstract The primary goal of the paper is to monitor the health of the spindle in machine tools to ensure optimal performance and reduce costly downtimes. Spindle health monitoring is essential to detect wear and cracks in spindle bearings, which can be challenging due to their gradual development and hidden locations. The proposed approach combines physics-based modeling and data-driven techniques to monitor spindle health effectively. In Part I and Part II of the paper, mathematical models of bearing faults and spindle imbalance are integrated into the digital model of the spindle. This allows for simulating the operation of the spindle both with and without faults. The integration of fault models enables the generation of vibrations at sensor locations along the spindle shaft. The generated vibration data from the physics-based model are used to train a recurrent neural network-based (RNN) fault detection algorithm. The RNN learns from the labeled vibration spectra to identify different fault conditions. Bayesian optimization is used to automatically tune the hyperparameters governing the accuracy and efficiency of the learning models during the training process. The RNN classifiers are further fine-tuned using a small set of experimentally collected data for the generalization of the model on real-world data. Once the RNN classifier is trained, it can distinguish between different types of damage and identify their specific locations on the spindle assembly. The proposed algorithms achieved an accuracy of 98.43% on experimental data sets that were not used in training the network.

Damage assessment of Siverek Castle during the Kahramanmaraş Earthquakes (Mw 7.7 and Mw 7.6) on 06 February 2023: Remediation and strengthening proposals

On February 6, 2023, two significant earthquakes with magnitudes (Mw) of 7.7 and 7.6 struck Turkey, occurring nine hours apart. In addition to the tragic loss of over 50,000 lives in the earthquakes centered in Kahramanmaraş, hundreds of thousands of engineering structures, such as residences, schools, hospitals, historical landmarks, highways, and more, were severely damaged. This study assesses the damages and risk scenario following the Kahramanmaraş earthquakes concerning Siverek Castle. In addition, remediation and strengthening proposals, required to eliminate the damage and the possible risk, have been developed. The initial stage involved observational damage assessments on the castle and surrounding slopes as part of field studies, identifying five different types of damage and potential risks. Subsequently, a precise 3D digital model of the damaged castle and its slopes was generated using the digital photogrammetry method. Additionally, geological and geophysical studies were conducted in the field to determine the characteristics of the mound structure, historical castle walls, soil and rock on the slope. Non-destructive, geophysical methods consisting Vertical Electrical Sounding (VES), Seismic Refraction Method (SRM) and Multichannel Analysis of Surface Waves (MASW) measurements were specifically employed in the area with historical remnants. To verify the obtained data, five boreholes were drilled in the lower parts of the slope, and experimental studies were conducted to determine the soil and rock material properties of the slope. In the numerical studies, a total of 54 2D stability analyses were performed under static, long-term static, and dynamic conditions. Additionally, 1000 different probabilistic rockfall analyses were conducted, both in 2D and 3D, to calculate the run-out distance, bounce height, velocity, and kinetic energies of the blocks that fell or were about to fall during the earthquake. In the final stage of the study, remediation and strengthening recommendations were prepared for the strengthening of the fortification walls and slopes where failures occurred, and stability analyses were conducted. Consequently, a design proposal recommending five distinct approaches to remediate and strengthen the castle and slopes impacted by the Kahramanmaraş earthquakes was endorsed by the relevant authorities, and construction has commenced. When the remediation and strengthening works are completed, the security of the cultural heritage will be ensured, and it is planned to be opened to visitors.

Entropy Wavelet-Based Method to Increase Efficiency in Highway Bridge Damage Identification

Highway bridges are crucial civil constructions for the transport infrastructure, which require proper attention from the corresponding institutions of each country and constant financing for their adequate maintenance; this is important because different types of damage can be generated within these structures, caused by natural disasters, among other sources, and the heavy loads they transport every day. Therefore, the development of simple, efficient, and low-cost methods is of vital importance, allowing us to identify damage in a timely manner and avoid bridges collapsing. As reported in a previous work, the wavelet energy accumulation method (WEAM) and its corresponding application in the Rio Papaloapan Bridge (RPB) represented an important advance within the field. Despite identifying damage in bridges with precision and at a low cost, there are several aspects to improve in that method. Therefore, in this work, that method was improved, eliminating several steps, and meaningfully reducing the computational burden by implementing an algorithm based on the Shannon entropy, thus giving way to the new entropy wavelet-based method (EWM). This new method was applied directly with regard to the real-life RPB, in both its healthy and damaged conditions. Also, its corresponding numerical model based on the finite element method in its healthy condition and different damage scenarios were carried out. The results indicate that the new EWM retains the advantages of WEAM, and it allows for damage identification to be completed more efficiently, increasing the precision by approximately 0.11%, and significantly reducing the computing time required to obtain results by 5.67 times.

Damage detection of thin plates by fusing variational mode decomposition and spectral entropy

This paper presents a new approach for damage detection in thin plates by fusing variational mode decomposition and spectral entropy (VMD-SE). In this method, after the received signal is decomposed into some intrinsic mode functions (IMFs) by variational mode decomposition (VMD), the spectral entropy ratio of the first and last IMFs is calculated for optimizing the VMD’s parameters and improving its decomposition performance. Moreover, the cross-correlation coefficient between the decomposed IMFs and the reference signal is computed to separate the desired IMF, which contains more damage information. Finally, the spectral entropy of the obtained IMF is calculated as an indicator for assessing the damage’s severity. The comparative analysis of the simulated signal clearly shows that only the proposed method can successfully separate the damage-related and reference signals. To verify the VMD-SE method, damage detection of two different types of damage on aluminum and composite fiber-reinforced polymer (CFRP) plates is conducted by using this new approach. The experimental results demonstrate that the parameters of VMD affect greatly its decomposition performance, and the best parameters are selected. The results also indicate that the normalized spectral entropy monotonically increases when the diameter of the through-hole or the length of the scratch increases. In addition, the correlation coefficients of the fitting lines of the plates are larger than 0.998. The experimental results of aluminum specimens demonstrate that the damage’s location has an influence on the normalized spectral entropy. At last, based on the linear relationship, the severity of damage in the fourth specimen is identified. The identification results demonstrate that the relative error of the aluminum and CFRP plates is less than 7.34%, which indicates that this new algorithm by fusing VMD and spectral entropy can detect the damage size in thin plates accurately and efficiently.

Combining transfer learning and hyperspectral imaging to identify bruises of pears across different bruise types.

Mechanical bruise is one of the most crucial factors affecting the quality of pears, which has a huge influence on postharvest transportation, storage, and sale of pears. To rapidly detect early bruises of pears across different bruise types, hyperspectral imaging technology coupled with transfer learning methods was performed in this study. Two transfer learning methods, that is, transfer component analysis (TCA) and manifold embedded distribution alignment (MEDA), were applied for two tasks (impact bruise→crush bruise, crush bruise→impact bruise). Supporting vector machine (SVM) was set as a baseline to conduct analysis and comparison of the transferability of the models. The result showed that, for task 1 (impact bruise→crush bruise), MEDA and TCA-SVM model achieved a classification accuracy of 93.33% and 91.11% in target domain, individually. For task 2 (crush bruise→impact bruise), MEDA and TCA-SVM model achieved an accuracy of 88.89% and 85.19% in target domain, respectively. Both the two models improved the accuracy compared with SVM models (84.44% for task 1; 77.04% for task 2). Overall, the results indicated that transfer learning approaches could perform pear bruise detection across different bruise types. Hyperspectral imaging in combination with transfer learning methods is a promising possibility for the efficient and cost-saving field detection of fruit bruises among different bruise types. PRACTICAL APPLICATION: The production and export of pears are faced with problems of mechanical damage due to vibration, collision, impact, and other factors, which cause chemical changes in color, odor, and taste. Sometimes the bruise was too slight to be ignored which would infect with other fruits in the future. In this study, we used hyperspectral imaging combined with transfer learning method could detect these slight bruises caused by different factors. Distinguishing different types of damage can provide a reference for quick judgment of the process causing damage and take prompt measures to reduce economic losses.

On the influence of low-velocity impact damage on constrained-layer damping in hybrid CFRP-elastomer-metal laminates

Following the principle of constrained-layer damping (CLD), fiber-metal-elastomer laminates (FMELs) offer a high potential for damped lightweight structures, overcoming the undesirable vibration characteristics of conventional lightweight materials. While proven to be versatile and efficient, the damage-tolerance of such laminates is unexplored. This study for the first time in literature addresses the damage-tolerance of this efficient damping mechanism using a combined experimental and numerical approach. Results of experimental low-velocity impact tests on different configurations of FMELs are presented. In subsequent numerical modal analyses, different types of damage, namely delaminations, intra-ply damage and permanent deformation, are modeled and their influence on the vibrational behavior is investigated. While all types of damage influence the natural frequencies and modal damping ratios with a strong mode dependency, all laminates retain a high amount of modal damping with losses typically not higher than 15%. The results obtained reveal, that CLD is an efficient intrinsic damping measure in FMELs even in the presence of different types of damage. The key contributions of this paper include the thorough experimental characterization of low-velocity impact damages in different configurations of FMELs as well as the numerical assessment of those in frequency-domain simulations.

Assessment of post-earthquake fire capacity of RC frames without seismic design

To study the importance of post-earthquake fire events, 500 reinforced concrete (RC) frames were designed without seismic considerations. These consist in three-bays frames with a variation in the number of floors from two to six (100 frames each). Numerical analyses were performed in SAFIR to investigate the influence of different types of damage, damage location, fire location and fire curves on the post-earthquake fire resistance of the structures. It was observed that the seismic damage can significantly decrease the fire resistance of the frames. In an earthquake scenario it can be expected an increase in the response times of the firefighters as well as malfunctioning and damage of active and passive firefighting measures, which means that the use of parametric fire curves without active firefighting measures can be a better approach instead of the use of the fire curve ISO 834. It was observed, for the analysed cases, that parametric fire curves without active firefighting measures led to lower collapse times when compared to the fire curve ISO 834. On the other hand, when the active firefighting measures are considered, there is a very significant number of structures that do not collapse, which indicates the importance that these firefighting measures can have in a fire after an earthquake. The development of these analysis emphasizes the impacts of applying different fire curves on the damaged structure which consequently brings attention to an adequate definition of the fire curves.

Off‐axis compression behavior and failure mechanisms of needled carbon/quartz fiber reinforced phenolic resin composite based on acoustic emission

AbstractThe effect of the off‐axis angle (note as θ) on compressive properties and failure mechanisms in needled carbon/quartz fiber reinforced phenolic resin (CF‐QF/PF) composite has been investigated. For this objective, a series of quasi‐static off‐axis compression tests were performed, and a more precise and general model for predicting the compressive modulus and strength has been proposed. Further, the acoustic emission (AE) technology, including parameter‐based analysis and the Hilbert Huang Transform (HHT) method, was also employed to scrutinize the intricate damage mechanisms. The results show that specimens with θ = 0° exhibit linear and brittle behavior and are the most dangerous scenarios because delamination (accounting for 58% of the total AE accumulative energy) dominates their failure. For specimens with small off‐axis angles (0° < θ ≤ 45°), the fiber‐matrix interface determines their compressive properties. While for specimens with large off‐axis angles(45° < θ ≤ 90°), the enhanced transverse load‐bearing capacity of fibers becomes the dominant factor, characterized by significant debonding (33% when θ = 60°) and fiber breakage (27% when θ = 90°). Finally, these results were verified by optical microscopy (OM) and scanning electron microscopy images (SEM).Highlights Off‐axis compression behaviors of needled CF‐QF/PF composite are studied. A more precise and general model for predicting off‐axis compression properties is proposed. Acoustic emission technology is used to quantify different types of damage. Failure mechanisms are revealed by combining in‐situ and offline techniques.

Materials and Climate Change: A Set of Indices as the Benchmark for Climate Vulnerability and Risk Assessment for Tangible Cultural Heritage in Europe

Among the issues most related to climate change, the built environment is also subjected to short- and long-term risks. Referring to tangible cultural heritage, materials and buildings are subjected to different types of damage that require adaptive risk prevention and containment strategies, currently missing from conventional risk assessments. Thus, there is an increasingly urgent need for scientific and technical knowledge, tools, and solutions aimed at solving critical issues in cultural heritage due to climate change. In this context, the aim of this study is to study the mechanisms of impacts brought about by climate change and the formulation of a possible set of indices as benchmarks to measure climate change’s effect on cultural heritage buildings. The study is structured on a methodology that identifies three sections: the first and second parts systematize and critically interpret data on impact mechanisms and indices for climate vulnerability and risk assessment; the third part, data processing, reports the perspective findings. The main intermediate indices, contributing to a comprehensive damage index, were identified, and a procedural protocol was developed. Finally, through the correlation of indices, a potential case study could be analyzed, and benchmarks made effective. The study reports partial results of one of the “Ecosystems of Innovation” pilot projects funded by the National Recovery and Resilience Plan. The study is still a work in progress and needs advancement and deepening to verify case study indices.

Correlation of nerve damage and peripheral neuropathy incidence using the MNSI and MDNS instrument approaches

The World Health Organization (WHO) reported that by 2030, diabetes mellitus would become the 7th leading cause of death. Diabetes mellitus is a chronic disease that causes various complications, one of which is peripheral neuropathy. Preventive efforts for peripheral neuropathy involve conducting detection examinations. The purpose of this study was to analyze nerve damage in peripheral neuropathy cases using the MNSI (Michigan Neuropathy Screening Instrument) and MDNS (Michigan Diabetic Neuropathy Score) instruments. The study employed a cross-sectional study approach with a sample of 50 people, using total sampling as the sampling technique. The independent variable in this study was nerve damage, and the dependent variable was peripheral neuropathy. Data collection in the study was carried out using the MNSI and MDNS instruments to link the dependent and independent variables. Hypothesis analysis in this study was conducted using the Spearman's rho correlation test. The study found that autonomic, sensory, and motor damage correlated with peripheral neuropathy, with a P-value of < 0.05. Examinations in the feet of diabetics were significantly related to the level of peripheral neuropathy. However, there was no evidence of a correlation between the characteristics of the respondents and the incidence of peripheral neuropathy. The results of the nerve damage examination demonstrated a correlation between different types of damage, and the MNSI and MDNS instruments proved effective in detecting peripheral neuropathy damage. Future research should focus on more in-depth studies to explore the correlation of nerve damage in patients with diabetes mellitus detected at a young age and consider other variables, such as HbA1c levels, as potential risk factors for peripheral neuropathy.