Predictive maintenance and its applications in civil engineering structures:A review

Structural health monitoring and performance prediction are crucial for smart disaster mitigation and intelligent management of structures throughout their lifespan.Recent advancements in predictive maintenance strategies within the industrial manufacturing industry have inspired similar innovations in civil engineering,aiming to improve structural performance evaluation,damage diagnosis,and capacity prediction.This review delves into the framework of predictive maintenance and examines various existing solutions,focusing on critical areas such as data acquisition,condition monitoring,damage prognosis,and maintenance planning.Results from real-world applications of predictive maintenance in civil engineering,covering high-rise structures,deep foundation pits,and other infrastructure,are presented.The challenges of implementing predictive maintenance in civil engineering structures under current technology,such as model interpretability of data-driven methods and standards for predictive maintenance,are explored.Future research prospects within this area are also discussed.

Development and optimization of object detection technology in pavement engineering: A literature review

Due to the rapid advancement of the transportation industry and the continual increase in pavement infrastructure,it is difficult to keep up with the huge road maintenance task by relying only on the traditional manual detection method.Intelligent pavement detection technology with deep learning techniques is available for the research and industry areas by the gradual development of computer vision technology.Due to the different characteristics of pavement distress and the uncertainty of the external environment,this kind of object detection technology for distress classification and location still faces great challenges.This paper discusses the development of object detection technology and analyzes classical convolutional neural network(CNN)architecture.In addition to the one-stage and two-stage object detection frameworks,object detection without anchor frames is introduced,which is divided according to whether the anchor box is used or not.This paper also introduces attention mechanisms based on convolutional neural networks and emphasizes the performance of these mechanisms to further enhance the accuracy of object recognition.Lightweight network architecture is introduced for mobile and industrial deployment.Since stereo cameras and sensors are rapidly developed,a detailed summary of three-dimensional object detection algorithms is also provided.While reviewing the history of the development of object detection,the scope of this review is not only limited to the area of pavement crack detection but also guidance for researchers in related fields is shared.

Dynamics and vibration reduction performance of asymmetric tristable nonlinear energy sink

With its complex nonlinear dynamic behavior,the tristable system has shown excellent performance in areas such as energy harvesting and vibration suppression,and has attracted a lot of attention.In this paper,an asymmetric tristable design is proposed to improve the vibration suppression efficiency of nonlinear energy sinks(NESs)for the first time.The proposed asymmetric tristable NES(ATNES)is composed of a pair of oblique springs and a vertical spring.Then,the three stable states,symmetric and asymmetric,can be achieved by the adjustment of the distance and stiffness asymmetry of the oblique springs.The governing equations of a linear oscillator(LO)coupled with the ATNES are derived.The approximate analytical solution to the coupled system is obtained by the harmonic balance method(HBM)and verified numerically.The vibration suppression efficiency of three types of ATNES is compared.The results show that the asymmetric design can improve the efficiency of vibration reduction through comparing the chaotic motion of the NES oscillator between asymmetric steady states.In addition,compared with the symmetrical tristable NES(TNES),the ATNES can effectively control smaller structural vibrations.In other words,the ATNES can effectively solve the threshold problem of TNES failure to weak excitation.Therefore,this paper reveals the vibration reduction mechanism of the ATNES,and provides a pathway to expand the effective excitation amplitude range of the NES.

Tensile strength and failure behavior of rock-mortar interfaces: Direct and indirect measurements

The tensile strength at the rock-concrete interface is one of the crucial factors controlling the failure mechanisms of structures,such as concrete gravity dams.Despite the critical importance of the failure mechanism and tensile strength of rock-concrete interfaces,understanding of these factors remains very limited.This study investigated the tensile strength and fracturing processes at rock-mortar interfaces subjected to direct and indirect tensile loadings.Digital image correlation(DIC)and acoustic emission(AE)techniques were used to monitor the failure mechanisms of specimens subjected to direct tension and indirect loading(Brazilian tests).The results indicated that the direct tensile strength of the rock-mortar specimens was lower than their indirect tensile strength,with a direct/indirect tensile strength ratio of 65%.DIC strain field data and moment tensor inversions(MTI)of AE events indicated that a significant number of shear microcracks occurred in the specimens subjected to the Brazilian test.The presence of these shear microcracks,which require more energy to break,resulted in a higher tensile strength during the Brazilian tests.In contrast,microcracks were predominantly tensile in specimens subjected to direct tension,leading to a lower tensile strength.Spatiotemporal monitoring of the cracking processes in the rock-mortar interfaces revealed that they show AE precursors before failure under the Brazilian test,whereas they show a minimal number of AE events before failure under direct tension.Due to different microcracking mechanisms,specimens tested under Brazilian tests showed lower roughness with flatter fracture surfaces than those tested under direct tension with jagged and rough fracture surfaces.The results of this study shed light on better understanding the micromechanics of damage in the rock-concrete interfaces for a safer design of engineering structures.

Evaluation of slope stability through rock mass classification and kinematic analysis of some major slopes along NH-1A from Ramban to Banihal, North Western Himalayas

The network of Himalayan roadways and highways connects some remote regions of valleys or hill slopes,which is vital for India’s socio-economic growth.Due to natural and artificial factors,frequency of slope instabilities along the networks has been increasing over last few decades.Assessment of stability of natural and artificial slopes due to construction of these connecting road networks is significant in safely executing these roads throughout the year.Several rock mass classification methods are generally used to assess the strength and deformability of rock mass.This study assesses slope stability along the NH-1A of Ramban district of North Western Himalayas.Various structurally and non-structurally controlled rock mass classification systems have been applied to assess the stability conditions of 14 slopes.For evaluating the stability of these slopes,kinematic analysis was performed along with geological strength index(GSI),rock mass rating(RMR),continuous slope mass rating(CoSMR),slope mass rating(SMR),and Q-slope in the present study.The SMR gives three slopes as completely unstable while CoSMR suggests four slopes as completely unstable.The stability of all slopes was also analyzed using a design chart under dynamic and static conditions by slope stability rating(SSR)for the factor of safety(FoS)of 1.2 and 1 respectively.Q-slope with probability of failure(PoF)1%gives two slopes as stable slopes.Stable slope angle has been determined based on the Q-slope safe angle equation and SSR design chart based on the FoS.The value ranges given by different empirical classifications were RMR(37-74),GSI(27.3-58.5),SMR(11-59),and CoSMR(3.39-74.56).Good relationship was found among RMR&SSR and RMR&GSI with correlation coefficient(R 2)value of 0.815 and 0.6866,respectively.Lastly,a comparative stability of all these slopes based on the above classification has been performed to identify the most critical slope along this road.

Surface wave inversion with unknown number of soil layers based on a hybrid learning procedure of deep learning and genetic algorithm

Surface wave inversion is a key step in the application of surface waves to soil velocity profiling.Currently,a common practice for the process of inversion is that the number of soil layers is assumed to be known before using heuristic search algorithms to compute the shear wave velocity profile or the number of soil layers is considered as an optimization variable.However,an improper selection of the number of layers may lead to an incorrect shear wave velocity profile.In this study,a deep learning and genetic algorithm hybrid learning procedure is proposed to perform the surface wave inversion without the need to assume the number of soil layers.First,a deep neural network is adapted to learn from a large number of synthetic dispersion curves for inferring the layer number.Then,the shear-wave velocity profile is determined by a genetic algorithm with the known layer number.By applying this procedure to both simulated and real-world cases,the results indicate that the proposed method is reliable and efficient for surface wave inversion.

Effects of retained dry material on the impact,overflow and landing dynamics

During long-term operation,the performance of obstacles would be changed due to the material accumulating upslope the obstacle.However,the effects of retained material on impact,overflow and landing dynamics of granular flow have not yet been elucidated.To address this gap,physical flume tests and discrete element simulations are conducted considering a range of normalized deposition height h0/H from 0 to 1,where h0 and H represent the deposition height and obstacle height,respectively.An analytical model is modified to evaluate the flow velocity and flow depth after interacting with the retained materials,which further serve to calculate the peak impact force on the obstacle.Notably,the computed impact forces successfully predict the experimental results when a≥25°.In addition,the results indicate that a higher h0/H leads to a lower dynamic impact force,a greater landing distance L,and a larger landing coefficient Cr,where Cr is the ratio of slope-parallel component of landing velocity to flow velocity just before landing.Compared to the existing overflow model,the measured landing distance L is underestimated by up to 30%,and therefore it is insufficient for obstacle design when there is retained material.Moreover,the recommended Cr in current design practice is found to be nonconservative for estimating the landing velocity of geophysical flow.This study provides insightful scientific basis for designing obstacles with deposition.

Topographic seismic effects and avalanche hazard:A case study of Mount Siella(L’Aquila,Central Italy)

In mountainous areas,snow avalanches could be triggered by the shaking produced by earthquakes.The forces induced by the earthquake can cause an irregular increase of shear strength load down the slope,for the presence of complex surface and buried morphologies.Topographic irregularities generate maximum effects of waves amplification linked to wavelengths comparable to the horizontal dimension of the topographic feature.For this reason,the selected time-histories represent an appropriate input for the two-dimensional numerical response analyses when a dynamic phenomenon produce the resonant motion of a whole mountain.This represents an important earthquake-induced hazard in snow-covered mountain areas with high probability of seismic events.Some valleys are located in regions with scare ground motion data and investments on infrastructures are not always accompanied by adequate protection against earthquake-induced avalanches.The paper points out a simple deterministic approach for selecting a set of real accelerograms applied to a real case of Siella Mountain(Central Italy)where a large avalanche destroying a tourist facility of Rigopiano resort on 18 January 2017.The selected time histories were used as input for the two-dimensional numerical model of the subsoil to evaluate the topographic seismic amplification in ridge and compare it with the results of other authors.These methods suggest that morphology-related inertial effects should be considered as an overload action on snow layers when controlling multi-hazard studies and spatial planning.

Monitoring and evaluation of the water quality of the Lower Neches River, Texas, USA

Increasing bacteria levels in the Lower Neches River caused by Hurricane Harvey has been of a serious concern.This study is to analyze the historical water sampling measurements and real-time water quality data collected with wireless sensors to monitor and evaluate water quality under different hydrological and hydraulic conditions.The statistical and Pearson correlation analysis on historical water samples determines that alkalinity,chloride,hardness,conductivity,and pH are highly correlated,and they decrease with increasing flow rate due to dilution.The flow rate has positive correlations with Escherichia coli,total suspended solids,and turbidity,which demonstrates that runoff is one of the causes of the elevated bacteria and sediment loadings in the river.The correlation between E.coli and turbidity indicates that turbidity greater than 45 nephelometric turbidity units in the Neches River can serve as a proxy for E.coli to indicate the bacterial outbreak.A series of statistical tools and an innovative two-layer data smoothing filter are developed to detect outliers,fill missing values,and filter spikes of the sensor measurements.The correlation analysis on the sensor data illustrates that the elevated sediment/bacteria/algae in the river is either caused by the first flush rain and heavy rain events in December to March or practices of land use and land cover.Therefore,utilizing sensor measurements along with rainfall and discharge data is recommended to monitor and evaluate water quality,then in turn to provide early alerts on water resources management decisions.

Evaluation of soil erosion vulnerability in Hubei Province of China using RUSLE model and combination weighting method

Soil erosion has been recognized as a critical environmental issue worldwide.While previous studies have primarily focused on watershed-scale soil erosion vulnerability from a natural factor perspective,there is a notable gap in understanding the intricate interplay between natural and socio-economic factors,especially in the context of spatial heterogeneity and nonlinear impacts of human-land interactions.To address this,our study evaluates the soil erosion vulnerability at a provincial scale,taking Hubei Province as a case study to explore the combined effects of natural and socio-economic factors.We developed an evaluation index system based on 15 indicators of soil erosion vulnerability:exposure,sensitivity,and adaptability.In addition,the combination weighting method was applied to determine index weights,and the spatial interaction was analyzed using spatial autocorrelation,geographical temporally weighted regression and geographical detector.The results showed an overall decreasing soil erosion intensity in Hubei Province during 2000 and 2020.The soil erosion vulnerability increased before 2000 and then.The areas with high soil erosion vulnerability were mainly confined in the central and southern regions of Hubei Province(Xiantao,Tianmen,Qianjiang and Ezhou)with obvious spatial aggregation that intensified over time.Natural factors(habitat quality index)had negative impacts on soil erosion vulnerability,whereas socio-economic factors(population density)showed substantial spatial variability in their influences.There was a positive correlation between soil erosion vulnerability and erosion intensity,with the correlation coefficients ranging from-0.41 and 0.93.The increase of slope was found to enhance the positive correlation between soil erosion vulnerability and intensity.

Recycling arsenic-containing bio-leaching residue after thermal treatment in cemented paste backfill:Structure modification,binder properties and environmental assessment

The substantial arsenic(As)content present in arsenic-containing bio-leaching residue(ABR)presents noteworthy environ-mental challenges attributable to its inherent instability and susceptibility to leaching.Given its elevated calcium sulfate content,ABR exhibits considerable promise for industrial applications.This study delved into the feasibility of utilizing ABR as a source of sulfates for producing super sulfated cement(SSC),offering an innovative binder for cemented paste backfill(CPB).Thermal treatment at varying temperatures of 150,350,600,and 800℃ was employed to modify ABR’s performance.The investigation encompassed the examination of phase transformations and alterations in the chemical composition of As within ABR.Subsequently,the hydration characteristics of SSC utilizing ABR,with or without thermal treatment,were studied,encompassing reaction kinetics,setting time,strength development,and microstructure.The findings revealed that thermal treatment changed the calcium sulfate structure in ABR,consequently impacting the resultant sample performance.Notably,calcination at 600℃ demonstrated optimal modification effects on both early and long-term strength attributes.This enhanced performance can be attributed to the augmented formation of reaction products and a densified micro-structure.Furthermore,the thermal treatment elicited modifications in the chemical As fractions within ABR,with limited impact on the As immobilization capacity of the prepared binders.

Data-driven prediction of dimensionless quantities for semi-infinite target penetration by integrating machine-learning and feature selection methods

This study employs a data-driven methodology that embeds the principle of dimensional invariance into an artificial neural network to automatically identify dominant dimensionless quantities in the penetration of rod projectiles into semi-infinite metal targets from experimental measurements.The derived mathematical expressions of dimensionless quantities are simplified by the examination of the exponent matrix and coupling relationships between feature variables.As a physics-based dimension reduction methodology,this way reduces high-dimensional parameter spaces to descriptions involving only a few physically interpretable dimensionless quantities in penetrating cases.Then the relative importance of various dimensionless feature variables on the penetration efficiencies for four impacting conditions is evaluated through feature selection engineering.The results indicate that the selected critical dimensionless feature variables by this synergistic method,without referring to the complex theoretical equations and aiding in the detailed knowledge of penetration mechanics,are in accordance with those reported in the reference.Lastly,the determined dimensionless quantities can be efficiently applied to conduct semi-empirical analysis for the specific penetrating case,and the reliability of regression functions is validated.

Evaluation of the seismic behavior of reinforced concrete structures with flat slab-column gravity frame and shear walls through nonlinear analysis methods

This paper presents an investigation of the seismic behavior of reinforced concrete(RC)structures in which shear walls are the main lateral load-resisting elements and the participation of flat slab floor systems is not considered in the seismic design procedure.In this regard,the behavior of six prototype structures(with different heights and plan layouts)is investigated through nonlinear static and time history analyses,implemented in the OpenSees platform.The results of the analyses are presented in terms of the behavior of the slab-column connections and their mode of failure at different loading stages.Moreover,the global response of the buildings is discussed in terms of some parameters,such as lateral overstrength due to the gravity flat slab-column frames.According to the nonlinear static analyses,in structures in which the slab-column connections were designed only for gravity loads,the slab-column connections exhibited a punching mode of failure even in the early stages of loading.However,the punching failure was eliminated in structures in which a minimum transverse reinforcement recommended in ACI 318(2019)was provided in the slabs at joint regions.Furthermore,despite neglecting the contribution of gravity flat slab-column frames in the lateral load resistance of the structures,a relatively significant overstrength was imposed on the structures by the gravity frames.

Analytical evaluation of steady-state solute distribution in through- diffusion and membrane behavior test under non-perfectly flushing boundary conditions

The through-diffusion and membrane behavior testing procedure using a closed-system apparatus has been widely used for concurrent measurement of diffusion and membrane efficiency coefficients of low-permeability clay-based barrier materials.However,the common assumption of perfectly flushing conditions at the specimen boundaries could induce errors in analyses of the diffusion coefficients and membrane efficiencies.In this study,an innovative pseudo three-dimensional(3D)analytical method was proposed to evaluate solute distribution along the boundary surfaces of the soil-porous disks system,considering the non-perfectly flushing conditions.The results were consistent with numerical models under two scenarios considering different inflow/outflow positions.The proposed model has been demonstrated to be an accurate and reliable method to estimate solute distributions along the bound-aries.The calculated membrane efficiency coefficient and diffusion coefficient based on the proposed analytical method are more accurate,resulting in up to 50%less relative error than the traditional approach that adopts the arithmetic mean value of the influent and effluent concentrations.The retar-dation factor of the clay specimen also can be calculated with a revised cumulative mass approach.Finally,the simulated transient solute transport matched with experimental data from a multi-stage through-diffusion and membrane behavior test,validating the accuracy of the proposed method.

Experimental study on workability and permeability of sandy soils conditioned with thickened foam

Water spewing and muck plugging often occur during earth pressure balance(EPB)shield machines tunnelling in water-rich sandy strata,even though the conventional foam has been employed to condition sandy soils.In this study,a novel thickened foaming agent suitable for EPB shield tunnelling in water-rich sandy strata is developed.In contrast to conventional foam-conditioned sands,the thickened foam-conditioned sand has a low permeability due to the consistent filling of soil pores with the thickened foam,and the initial permeability coefficient decreases by approximately two orders of magnitude.It also exhibits a suitable workability,which is attributed to the enhanced capability of the thickened foam to condition sandy soils.In addition,the effect of concentration on the stability of the foam is explained by the Gibbs-Marangoni effect,and conditioning mechanisms for the thickened foam on sands are discussed from the evolution of foam bubbles.

Facilitated Prediction of Micropollutant Degradation via UV-AOPs in Various Waters by Combining Model Simulation and Portable Measurement

The degradation of micropollutants in water via ultraviolet(UV)-based advanced oxidation processes(AOPs)is strongly dependent on the water matrix.Various reactive radicals(RRs)formed in UV-AOPs have different reaction selectivities toward water matrices and degradation efficiencies for target micropollutants.Hence,process selection and optimization are crucial.This study developed a facilitated prediction method for the photon fluence-based rate constant for micropollutant degradation(K′_(p,MP))in various UV-AOPs by combining model simulation with portable measurement.Portable methods for measuring the scavenging capacities of the principal RRs(RRSCs)involved in UV-AOPs(i.e.,HO^(·),SO_(4)^(·-),and Cl^(·))using a mini-fluidic photoreaction system were proposed.The simulation models consisted of photochemical,quantitative structure–activity relationship,and radical concentration steady-state approximation models.The RRSCs were determined in eight test waters,and a higher RRSC was found to be associated with a more complex water matrix.Then,by taking sulfamethazine,caffeine,and carbamazepine as model micropollutants,the k′_(p,MP) values in various UV-AOPs were predicted and further verified experimentally.A lower k′_(p,MP) was found to be associated with a higher RRSC for a stronger RR competition;for example,k′_(p,MP) values of 130.9 and 332.5 m^(2) einstein^(–1),respectively,were obtained for carbamazepine degradation by UV/H_(2)O_(2) in the raw water(RRSC=9.47×10^(4) s^(-1))and sand-filtered effluent(RRSC=2.87×10^(4) s^(-1))of a drinking water treatment plant.The developed method facilitates process selection and optimization for UV-AOPs,which is essential for increasing the efficiency and cost-effectiveness of water treatment.

Understanding the Resilience of Urban Rail Transit:Concepts,Reviews,and Trends

As the scale of urban rail transit(URT)networks expands,the study of URT resilience is essential for safe and efficient operations.This paper presents a comprehensive review of URT resilience and highlights potential trends and directions for future research.First,URT resilience is defined by three primary abilities:absorption,resistance,and recovery,and four properties:robustness,vulnerability,rapidity,and redundancy.Then,the metrics and assessment approaches for URT resilience were summarized.The metrics are divided into three categories:topology-based,characteristic-based,and performance-based,and the assessment methods are divided into four categories:topological,simulation,optimization,and datadriven.Comparisons of various metrics and assessment approaches revealed that the current research trend in URT resilience is increasingly favoring the integration of traditional methods,such as conventional complex network analysis and operations optimization theory,with new techniques like big data and intelligent computing technology,to accurately assess URT resilience.Finally,five potential trends and directions for future research were identified:analyzing resilience based on multisource data,optimizing train diagram in multiple scenarios,accurate response to passenger demand through new technologies,coupling and optimizing passenger and traffic flows,and optimal line design.

Spatiotemporal variations of sand hydraulic conductivity by microbial application methods

The spatiotemporal distributions of microbes in soil by different methods could affect the efficacy of the microbes to reduce the soil hydraulic conductivity.In this study,the specimens of bio-mediated sands were prepared using three different methods,i.e.injecting,mixing,and pouring a given microbial so-lution onto compacted sand specimens.The hydraulic conductivity was measured by constant-head tests,while any soil microstructural changes due to addition of the microbes were observed by scan-ning electron microscope(SEM)and mercury intrusion porosimetry(MIP)tests.The amount of dextran concentration produced by microbes in each type of specimen was quantified by a refractometer.Results show that dextran production increased exponentially after 5-7 d of microbial settling with the supply of culture medium.The injection and mixing methods resulted in a similar amount and uniform dis-tribution of dextran in the specimens.The pouring method,however,produced a nonuniform distri-bution,with a higher concentration near the specimen surface.As the supply of culture medium discontinued,the dextran content near the surface produced by the pouring method decreased dramatically due to high competition for nutrients with foreign colonies.Average dextran concentration was negatively and correlated with hydraulic conductivity of bio-mediated soils exponentially,due to the clogging of large soil pores by dextran.The hydraulic conductivity of the injection and mixing cases did not change significantly when the supply of culture medium was absent.

Infiltration,runoff,and slope stability behaviors of infinite slope with macropores based on an improved Green–Ampt model

Infiltration–runoff–slope instability mechanism of macropore slope under heavy rainfall is unclear.This paper studied its instability mechanism with an improved Green–Ampt(GA)model considering the dual-porosity(i.e.,matrix and macropore)and ponding condition,and proposed the infiltration equations,infiltration–runoff coupled model,and safety factor calculation method.Results show that the infiltration processes of macropore slope can be divided into three stages,and the proposed model is rational by a comparative analysis.The wetting front depth of the traditional unsaturated slope is 17.2%larger than that of the macropore slope in the early rainfall stage and 27%smaller than that of the macropore slope in the late rainfall stage.Then,macropores benefit the slope stability in the early rainfall but not in the latter.Macropore flow does not occur initially but becomes pronounced with increasing rainfall duration.The equal depth of the wetting front in the two domains is regarded as the onset criteria of macropore flow.Parameter analysis shows that macropore flow is delayed by increasing proportion of macropore domain(ω_(f)),whereas promoted by increasing ratio of saturated permeability coefficients between the two domains(μ).The increasing trend of ponding depth is sharp at first and then grows slowly.Finally,when rainfall duration is less than 3 h,ωf andμhave no significant effect on the safety factor,whereas it decreases with increasingωf and increases with increasingμunder longer duration(≥3 h).With the increase ofω_(f),the slope maximum instability time advances by 10.5 h,and with the increase ofμ,the slope maximum instability time delays by 3.1 h.

Prediction of Damping Capacity Demand in Seismic Base Isolators via Machine Learning

Base isolators used in buildings provide both a good acceleration reduction and structural vibration control structures.The base isolators may lose their damping capacity over time due to environmental or dynamic effects.This deterioration of them requires the determination of the maintenance and repair needs and is important for the long-termisolator life.In this study,an artificial intelligence prediction model has been developed to determine the damage and maintenance-repair requirements of isolators as a result of environmental effects and dynamic factors over time.With the developed model,the required damping capacity of the isolator structure was estimated and compared with the previously placed isolator capacity,and the decrease in the damping property was tried to be determined.For this purpose,a data set was created by collecting the behavior of structures with single degrees of freedom(SDOF),different stiffness,damping ratio and natural period isolated from the foundation under far fault earthquakes.The data is divided into 5 different damping classes varying between 10%and 50%.Machine learning model was trained in damping classes with the data on the structure’s response to random seismic vibrations.As a result of the isolator behavior under randomly selected earthquakes,the recorded motion and structural acceleration of the structure against any seismic vibration were examined,and the decrease in the damping capacity was estimated on a class basis.The performance loss of the isolators,which are separated according to their damping properties,has been tried to be determined,and the reductions in the amounts to be taken into account have been determined by class.In the developed prediction model,using various supervised machine learning classification algorithms,the classification algorithm providing the highest precision for the model has been decided.When the results are examined,it has been determined that the damping of the isolator structure with the machine learning method is predicted successfully at a level exceeding 96%,and it is an effective method in deciding whether there is a decrease in the damping capacity.