Triangular Membership Functions Research Articles

Comparative analysis of Fuzzy Tsukamoto's membership functions for determining irrigated rice field feasibility status

The representation of the fuzzy set membership curve consisting of trapezoidal, triangular, and linear shapes, has an important role in the fuzzy logic system. The selection of the curve's shapes determines the useable membership function and affects the fuzzy output value. Previous studies generally used curves that had been employed in predecessors or other studies that did not explain the reason for choosing a fuzzy member curve. This condition became problem because there was not a guide in selecting the appropriate membership function model for the parameters used in the fuzzy process so that most researchers only use membership functions that are commonly used in previous studies or in the same case as their research. The purpose of this study was to determine the effect of selecting trapezoidal and triangular curves on the performance of Tsukamoto's fuzzy logic for determining the rice-fields suitability status. The research methodology comprised 3 main stages. The first stage was data collecting, to collect soil pH values, soil moisture, and air temperature in rice fields. The second stage was the implementation of the Tsukamoto fuzzy. At this stage, two membership function curves were used. The third stage was a comparative analysis of Tsukamoto's fuzzy's output of trapezoidal and triangular curves. The results obtained indicate that there is no significant performance difference between the two different membership functions. The results of the research with the trapezoidal membership function have a better accuracy rate of 93% while the triangular membership function has an accuracy rate of 90%.

A class of general type-2 fuzzy controller based on adaptive alpha-plane for nonlinear systems

This study introduces a general type-2 Takagi–Sugeno–Kang fuzzy controller (GT2-TSKFC) for controlling uncertain systems. The proposed GT2-TSKFC uses equidistant type-2 triangular membership functions (MFs) for the antecedents, Larsen’s implication method, type-1 fuzzy sets for the consequent parameters, and a direct defuzzification method. The analytical structure of the proposed controller indicates that the alpha-plane and apex of the secondary MFs have a noticeable effect on calculating the control signal. Adaptation of the alpha-plane and apex of the secondary MFs is performed using the Lyapunov function to achieve the stability of the controlled system. The proposed controller is applied to an uncertain nonlinear inverted pendulum system. The results of the proposed control algorithm are compared with those of a general type-2 fuzzy controller with a specific number of alpha-planes, a quasi type-2 fuzzy controller, an interval type-2 fuzzy controller, and a type-1 fuzzy controller to demonstrate the robustness and effectiveness of the proposed scheme.

Estimating gabion weir oxygen transfer with data mining

Abstract Conventionally, impermeable weirs are employed for retaining, measuring, and regulating the water in the river. Today, alternative devices are more predominantly in vogue, which are made of locally available materials called gabion weirs chosen because the latter can better fulfill ecological needs due to their porous nature. Dissolved oxygen (D.O.) is one of the significant determinants for assessing the character of water bodies. This study mainly focuses on improving the estimation of the gabion oxygen transfer efficiency (OTE20) to enhance its efficacy. The backpropagation neural network (BPNN), adaptive neuro-fuzzy inference system (ANFIS), and multi-variant linear and nonlinear regression (MVLR and MVNLR) are developed with experimental data to estimate the OTE20 and their results are compared. In terms of statistical metrics, the BPNN has proved to be the best-performing model. At the same time, triangular membership function (mf)-based ANFIS is the second-best performing model. Nevertheless, other applied mf-based ANFIS, MVLR, and MVNLR are giving a comparable performance. Input variable discharge per unit width (q) is the most crucial parameter in the computation of the OTE20, followed by the gabion mean size (d50). Major challenges are found in computing porosity of the gabion materials and optimal parameters of proposed data mining techniques.

Identification of Risk Factors Using ANFIS-Based Security Risk Assessment Model for SDLC Phases

In the field of software development, the efficient prioritizing of software risks was essential and play significant roles. However, finding a viable solution to this issue is a difficult challenge. The software developers have to adhere strictly to risk management practice because each phase of SDLC is faced with its individual type of risk rather than considering it as a general risk. Therefore, this study proposes an adaptive neuro-fuzzy inference system (ANFIS) for selection of appropriate risk factors in each stages of software development process. Existing studies viewed the SDLC’s Security risk assessment (SRA) as a single integrated process that did not offer a thorough SRA at each stage of the SDLC process, which resulted in unsecure software development. Hence, this study identify and validate the risk factors needed for assessing security risk at each phase of SDLC. For each phase, an SRA model based on an ANFIS was suggested, using the identified risk factors as inputs. For the logical representation of the fuzzification as an input and output variables of the SRA risk factors for the ANFIS-based model employing the triangular membership functions. The proposed model utilized two triangular membership functions to represent each risk factor’s label, while four membership functions were used to represent the labels of the target SRA value. Software developers chose the SRA risk factors that were pertinent in their situation from the proposed taxonomy for each level of the SDLC process as revealed by the results. As revealed from the study’s findings, knowledge of the identified risk factors may be valuable for evaluating the security risk throughout the SDLC process.

Methodology for Determining the Probability of Damage to UAV as a Result of Exposure to Atmospheric Environmental Factors

Introduction. For the effective use of unmanned aerial vehicles (UAV), it is required to take into account the impact of atmospheric environmental factors. Based on the existing level of development of the research and methodological apparatus, it is impossible to determine the probability of damage to the UAV under the conditions of complex exposure to atmospheric environmental factors and to assess the feasibility of further performance of the flight task. Violation of the UAV operation process is caused by the influence of atmospheric precipitation, wind and temperature conditions of the environment. The work aims at the development of a methodology for determining the probability of damage to UAV as a result of exposure to atmospheric environmental factors, as well as to evaluate the performance of the software package implementing the developed algorithm using triangular, trapezoidal, pentagonal, and Gaussian membership functions.Materials and Methods. A technique is proposed that makes it possible, using the theory of fuzzy logic, to determine the probability of damage to the UAV with inaccuracies and uncertainties in the description of atmospheric effects of the environment. It takes into account possible atmospheric forcing and enables to determine the probability of damage to the UAV under various atmospheric influences. The computational complexity of the algorithm implementing the technique depends significantly on the number of qualitative assessments of atmospheric impacts on UAV.Results. A method for determining the probability of damage to UAV as a result of exposure to atmospheric environmental factors based on fuzzy sets (triangular, trapezoidal, pentagonal, Gaussian) was proposed and tested. The use of fuzzy sets for estimating the conditions of the UAV application environment was described. An algorithm was developed to determine the probability of damage to the UAV as a result of exposure to atmospheric environmental factors. A computational experiment was carried out to determine the complexity of calculating the probability of UAV damage as a result of atmospheric forcing under various environmental conditions: «moderate conditions» at a wind speed of 3 m/s, precipitation intensity of 0.8 mm/h, and air temperature of 5 °C; «very heavy conditions» of the external environment at a wind speed of 12 m/s, precipitation intensity 3.5 mm/h, and air temperature of –6 ºС. It was established that the use of triangular membership functions to calculate the probability of damage to the UAV as a result of atmospheric forcing provided higher performance compared to the rest ones (trapezoidal, pentagonal, Gaussian).Discussion and Conclusions. The values of the UAV damage probabilities obtained during the implementation of the methodology under atmospheric environmental influences can be used at the pre-flight preparation stage and during the flight to assess the feasibility of further performance of the flight task. Based on the analysis of the application of the considered forms of membership functions, recommendations for their application are given. The use of algorithms with triangular membership functions will provide high performance of UAV control systems (UAV CS).

Optimization of fuzzy photovoltaic maximum power point tracking controller using chimp algorithm

<span>In this paper, a photovoltaic (PV) fuzzy maximum power point tracking (MPPT) method optimized by the chimp algorithm is presented. The fuzzy logic controller (FLC) of seven triangular membership functions (MFs) is used. The optimization fitness function is composed of transient and steady-state indices under different irradiation and temperature operating conditions. By using MATLAB package, the performance of optimized method is examined and compared with asymmetrical FLC and well-known perturb and observe (P&O) tracking methods at different operating conditions in terms of: transient rising time (tr) and energy yield during 30 s. Moreover, the tracking methods are also compared in terms of the fitness <span>function value. From the comparison of simulation results, a more energy can be</span> harvested by using the proposed optimized tracking method compared to the <span>other methods. Consequently, at the various operating conditions, the proposed</span> method can be used as a more reliable tracking method for PV systems.</span>

Health status evaluation of photovoltaic array based on deep belief network and Hausdorff distance

Photovoltaic (PV) arrays, as the core part of PV plants, are sensitive to the complex environment that can lead to fluctuations in their power generation performance. The health status evaluation (HSE) of PV arrays is beneficial for routine maintenance and economic value evaluation. In this paper, a method for evaluating the health status of PV array based on deep belief network (DBN) and Hausdorff distance (HD) is proposed. First, the I–V curves of the PV array are preprocessed, including curve filtering and points redistribution. Then, the practical features of I–V characteristics are extracted by DBN. Next, the health indicator (HI) of the PV array is constructed by HD and Logistic function. Finally, the triangular fuzzy membership function is used to build the mapping relationship between the HI values and the health grades of the PV array. The proposed method enables fully extracting the features from the I–V characteristics of PV arrays and gives an accurate evaluation of different states of PV arrays. The experimental results show that the proposed HSE method can realize the expected objectives.

Prediction of e-waste generation: Application of modified adaptive neuro-fuzzy inference system (MANFIS).

An accurate estimation of generated electronic waste (e-waste) plays a pivotal role in the development of any appropriate e-waste management plan. The present study aimed to exploit modified adaptive neuro-fuzzy inference system (MANFIS) for the estimation of generated e-waste. There are different parameters affecting e-waste generation, the most important of which need to be identified to achieve the accurate estimation. The MANFIS used for parameter selection involves evaluating multiple choices between twelve initially specified parameters. The MANFIS models with five inputs have the highest mean R2(train) and R2(test) (0.978 and 0.952, respectively, in training and testing stages). According to the results, the best combination of parameters was related to legal imports of electrical and electronic equipment (EEE), smuggling (illegal) imports of EEE, exports of EEE, accumulation of EEE in Tehran, and accumulation of EEE in Iran with RMSE(train) and RMSE(test) of 0.221 and 2.221, respectively. The findings showed that the model with three triangular membership functions had the best performance; R2(train) and RMSE(train) values were 0.981 and 1.371, as well as R2(test) and RMSE(test) values were 0.971 and 1.678, respectively. Finally, the developed model was successfully applied for prediction of monthly e-waste generation in Tehran for thirteen selected electronic items. The obtained consistent results emphasized that appropriate selection of the number of input parameters and their combination, along with identifying optimal structure of MANFIS, provides a proper, simple and accurate prediction of e-waste.

Multi-characteristic optimization and modeling analysis of electrocoagulation treatment of abattoir wastewater using iron electrode pairs

Multi-characteristic optimization and modeling analysis of electrocoagulation (EC) treatment of abattoir wastewater (AWW) using iron‑iron electrodes are reported. Response Surface Methodology (RSM) and Artificial Intelligent (AI) modeling tools viz. Artificial Neural Network (ANN) and Adaptive Neuro-Fuzzy Inference System (ANFIS) were used for the modeling while the numerical method of RSM and RSM-based Genetic Algorithm (RSM-GA) were used to optimize the response variable. The independent variables were pH, current intensity, electrolysis time, settling time, and temperature while the dependent variable was percentage turbidity removal. Based on high determination coefficient (R2) and low standard deviation (SD) values, the quadratic model was selected from the ANOVA of the RSM model. For the ANN model, the number of neurons were varied between 2 and 10 but based on low MSE and high R2 values, 10 was selected as the optimum number of neurons. The ANFIS network used the triangular input membership function (trimf) with 100 epochs. The three models depicted linear adequacy with respective R2 values of 0.9978, 0.9995, and 0.8285 for RSM, ANFIS, and ANN respectively. However, the performance error indices indicated that the prediction accuracy of the three models followed the order — ANFIS > ANN > RSM. Further, the validated optimization results produced 94.74 % and 97.42 % turbidity removal for the RSM and RSM-GA optimization methods respectively. This proved that RSM, ANN, and ANFIS can reliably be used to model the EC treatment of AWW using FeFe electrodes; while the response variable can successfully be optimized by RSM and RSM-GA methods.

PID Regulator and Fuzzy-Digital Filter Combination in the Thermoelement Control Task

The thermoelement control system basis on the fuzzy logic has been developed in this article. This system improves the stability characteristics of devices using a PID controller in their structure. This effect is achieved by reducing the output signal, the value of which is calculated using a fuzzy digital filter. The main aspect of the fuzzy digital filter is the use of the area ratio method during defuzzifing. The limitation of this method is the use of triangular membership functions. The experimental results presented in the article demonstrate the effectiveness of the proposed solution. The study has shown that the time of transient processes with the combined control of the thermoelement is reduced by 16—22 %.

A Fuzzy Approach to Drum Cymbals Classification

The many factors that influence the sound of a cymbal, combined with the external aspects that modify its sound perception, make the study of the acoustics of these instruments more challenging. Within the context of machine learning, most researches involving cymbal classification from their sounds aim to identify those instruments according to their types. However, there is a lack of studies investigating the acoustic elements of cymbals using machine learning techniques as tools. Hence, this paper proposes to classify cymbals according to their constitutive materials, since the metallic alloy assumes a significant portion is responsible for their acoustics. In addition, there is an interest in evaluating a fuzzy logic approach as a classifier applied to three sets of attributes, formed from temporal features and Mel Frequency Cepstral Coefficients extracted from audio signals, comparing triangular and Gaussian membership functions. For this, 276 audios, referring to 4 drum cymbals, were collected from a standardized procedure for capturing the sounds that considered variations in microphones and environments. As a result, the implemented model achieved 94.72% of average accuracy with a standard deviation of 2.51%, considering the Gaussian membership function and Mel Frequency Cepstral Coefficients as the audio descriptor.

A Non-Invasive Method to Evaluate Fuzzy Process Capability Indices via Coupled Applications of Artificial Neural Networks and the Placket–Burman DOE

The capability analysis of a process against requirements is often an instrument of change. The traditional and fuzzy process capability approaches are the most useful statistical techniques for determining the intrinsic spread of a controlled process for establishing realistic specifications and use for comparative processes. In the industry, the traditional approach is the most commonly used instrument to assess the impact of continuous improvement projects. However, these methods used to evaluate process capability indices could give misleading results because the dataset employed corresponds to the final product/service measures. This paper reviews an alternative procedure to assess the fuzzy process capability indices based on the statistical methodology involved in the modeling and design of experiments. Firstly, a model with reasonable accuracy is developed using a neural network approach. This model is embedded in a graphic user interface (GUI). Using the GUI, an experimental design is carried out, first to know the membership function of the process variability and then include this variability in the model. Again, an experimental design identifies the improved operating conditions for the significative independent variables. A new dataset is generated with these operating conditions, including the minimum error reached for each independent variable. Finally, the GUI is used to get a new prediction for the response variable. The fuzzy process capability indices are determined using the triangular membership function and the predicted response values. The feasibility of the proposed method was validated using a random data set corresponding to the basis weight of a papermaking process. The results indicate that the proposed method provides a better overview of the process performance, showing its true potential. The proposed method can be considered non-invasive.

A GIS-based offshore wind site selection model using fuzzy multi-criteria decision-making with application to the case of the Gulf of Maine

In the last decades, a considerable number of studies have been conducted to find the optimal locations for renewable energy facilities. The reviewed literature demonstrates how the combination of spatial representation computer tools, such as geographic information systems (GIS), with multi-criteria decision making (MCDM) methodologies, has successfully solved the problem of identifying optimal locations. Furthermore, since the appearance of fuzzy logic, the combination approaches have extended to the field of fuzzy sets to consider the imprecision and vagueness that some criteria may involve. In this paper, we propose a comparative analysis among fuzzy versions of MCDM methodologies, including GIS technologies, for the optimal site selection of offshore wind power plants. With this aim, we combined a classical pair-wise comparison method (AHP) with two distance-based approaches (TOPSIS and VIKOR), applying GIS software and comparing the two most extended fuzzy membership functions: triangular and linear. As a case study, this optimal location problem was applied to offshore wind site selection in the Gulf of Maine (USA). Initially, 56 alternatives for potential locations were identified from 22,331 km2 study area. After applying the AHP methodology, the weights of the criteria were obtained, turning out to be the wind speed and bathymetry the most important criteria. The results demonstrate the robustness of the fuzzy TOPSIS methodology against potential variations in the criteria weights, since the best alternatives (optimal locations) and almost 90% of the 25 top-ranked alternatives were matching. Likewise, the rankings of alternatives illustrate that the use of triangular or linear fuzzy membership functions does not cause significant differences after applying the fuzzy VIKOR methodology and ArcGIS software. In fact, the most appropriate alternative is the same for both cases, and there is only an exchange of positions among the top-ranked alternatives. The proposed solutions can be applied to other locations and both onshore and offshore installations.

A new fuzzy rule based multi-objective optimization method for cross-scale injection molding of protein electrophoresis microfluidic chips

Injection molding is one of the most promising technologies for the large-scale production and application of polymeric microfluidic chips. The multi-objective optimization of injection molding process for substrate and cover plate on protein electrophoresis microfluidic chip is performed to solve the problem that the forming precision is difficult to coordinate because of the cross-scale structure characteristics for chip in this paper. The innovation for this research is that an optimization approach and a detailed fuzzy rule determination method are proposed in multi-objective optimization for protein electrophoresis microfluidic chip. In more detail, firstly, according to the number and level of process parameters, the orthogonal experimental design is carried out. Then, the experiments are performed. Secondly, the grey relational analysis (GRA) approach is employed to process the response data to gain the grey relational coefficient (GRC). Thirdly, the grey fuzzy decision making method which combines triangular membership function and gaussian membership function is adopted to obtain the grey fuzzy grade (GFG). After that, the optimal scheme of process parameters was predicted by the grey fuzzy grade analysis. Finally, the superiority of Taguchi grey fuzzy decision making method are verified by comparing the results of original scheme, optimal scheme and prediction scheme. As a result, compared with the original design, the residual stress of substrate plate (RSS), residual stress of cover plate (RSC), warpage of substrate plate (WS), warpage of cover plate (WC) and replication fidelity of microchannel for substrate plate (RFM) on the prediction scheme for Taguchi grey fuzzy decision making method were reduced by 32.816%, 29.977%, 88.571%, 74.390% and 46.453%, respectively.

State of charge estimation of ultracapacitor based on equivalent circuit model using adaptive neuro-fuzzy inference system

Ultracapacitors have been attracting interest to apply as energy storage devices with advantages of fast charging capability, high power density, and long lifecycle. As a storage device, accurate monitoring is required to ensure and operate safely during the charge/discharge process. Therefore, high accuracy estimation of the state of charge (SOC) is needed to keep the Ultracapacitor working properly. This paper proposed SOC estimation using the Adaptive Neuro-Fuzzy Inference System (ANFIS). The ANFIS is tested by comparing it to true SOC based on an equivalent circuit model. To find the best method, the ANFIS is modified and tested with various membership functions of triangular, trapezoidal, and gaussian. The results show that triangular membership is the best method due to its high accuracy. An experimental test is also conducted to verify simulation results. As an overall result, the triangular membership shows the best estimation. Simulation results show SOC estimation mean absolute percentage error (MAPE) is 0.70 % for charging and 0.83 % for discharging. Furthermore, experimental results show that MAPE of SOC estimation is 0.76 % for random current. The results of simulations and experimental tests show that ANFIS with a triangular membership function has the most reliable ability with a minimum error value in estimating the state of charge on the Ultracapacitor even under conditions of indeterminate random current.

A Prognostic Based Fuzzy Logic Method to Speculate Yarn Quality Ratio in Jute Spinning Industry

Jute is a bio-degradable, agro-renewable, and widely available lingo cellulosic fiber having high tensile strength and initial modulus, moisture regain, good sound, and heat insulation properties. For these unique properties and eco-friendly nature of jute fibers, jute-based products are now widely used in many sectors such as packaging, home textiles, agro textiles, build textiles, and so forth. The diversified applications of jute products create an excellent opportunity to mitigate the negative environmental effect of petroleum-based products. For producing the best quality jute products, the main prerequisite is to ensure the jute yarn quality that can be defined by the load at break (L.B), strain at break (S.B), tenacity at break (T.B), and tensile modulus (T.M). However, good quality yarn production by considering these parameters is quite difficult because these parameters follow a non-linear relationship. Therefore, it is essential to build up a model that can cover this entire inconsistent pattern and forecast the yarn quality accurately. That is why, in this study, a laboratory-based research work was performed to develop a fuzzy model to predict the quality of jute yarn considering L.B, S.B, T.B, and T.M as input parameters. For this purpose, 173 tex (5 lb/spindle) and 241 tex (7 lb/spindle) were produced, and then L.B, S.B, T.B and T.M values were measured. Using this measured value, a fuzzy model was developed to determine the optimum L.B, S.B, T.B, and T.M to produce the best quality jute yarn. In our proposed fuzzy model, for 173 tex and 241 tex yarn count, the mean relative error was found to be 1.46% (Triangular membership) and 1.48% (Gaussian membership), respectively, and the correlation coefficient was 0.93 for both triangular and gaussian membership function. This result validated the effectiveness of the proposed fuzzy model for an industrial application. The developed fuzzy model may help a spinner to produce the best quality jute yarn.

Assessing most productive scale size with fuzzy stochastic data envelopment analysis

Classic data envelopment analysis (DEA) is a linear programming method for evaluating the relative efficiency of decision making units (DMUs) that uses multiple inputs to produce multiple outputs. In the classic DEA model inputs and outputs of DMUs are deterministic, while in the real world, are often fuzzy, random, or fuzzy-random. Many researchers have proposed different approaches to evaluate the relative efficiency with fuzzy and random data in DEA. In many studies, the most productive scale size (mpss) of decision making units has been estimated with fuzzy and random inputs and outputs. Also, the concept of fuzzy random variable is used in the DEA literature to describe events or occurrences in which fuzzy and random changes occur simultaneously. This paper has proposed the fuzzy stochastic DEA model to assess the most productive scale size of DMUs that produce multiple fuzzy random outputs using multiple fuzzy random inputs with respect to the possibility-probability constraints. For solving the fuzzy stochastic DEA model, we obtained a nonlinear deterministic equivalent for the probability constraints using chance constrained programming approaches (CCP). Then, using the possibility theory the possibilities of fuzzy events transformed to the deterministic equivalents with definite data. In the final section, the fuzzy stochastic DEA model, proposed model, has been used to evaluate the most productive scale size of sixteen Iranian hospitals with four fuzzy random inputs and two fuzzy random outputs with symmetrical triangular membership functions.

Time-dependent failure possibility of structures involving epistemic uncertainty

Time-dependent failure possibility (TDFP) is the possibility of performance less than zero under fuzzy uncertainty in the given time interval. Failure possibility of structures gets accelerated by improper consideration of input variables, mathematical and structural models, and environmental factors. To explore the environmental factors in structures, the time-variant creep and shrinkage model for concrete structures, and deterioration of steel cross-section and other material properties due to corrosion, and increment of action on the structure with time are considered. A real-time data has been collected from the city of Addis Ababa, Ethiopia for the case study to substantiate the methodology presented in this paper. In this paper, identification the possible failure modes, estimation of the time-dependent performance of failure modes, generating membership function (MF) of fuzzy input and output variables, and finally, the TDFP evaluated from MF of output quantities. Because of its simplicity and frequent application, triangular MF is used to characterize epistemic uncertainty of parameters. However, the failure possibility analysis is a complex problem for practicing engineering that needs a rigorous procedure, a single-loop optimization method (SLOM) is employed to evaluate TDFP of structures involving epistemic uncertainty. The effectives of SLOM has been checked by adaptive SORM Breitung’s algorithm. To enhance the validation of the proposed approach, several examples are thoroughly carried out that indicate the SLOM is bit conservative compared with SORM Breitung’s algorithm in such a way that their maximum recorded percentage difference is 9.464%.

Modeling and performance analysis of gas leakage emergency disposal process in gas transmission station based on Stochastic Petri nets

Efficient emergency disposal on site after gas leakage is crucial to avoid accident escalatio and expansion. However, current performance analysis studies of emerency disposal process are insufficient due to the uncertainty of rescue time prediction. In this paper, we aimed to explore the integration of triangular membership and regional center method to SPN for the modeling and evaluation of gas leakage emergency rescue process in gas transmission station. The stochastic Petri nets model was established by considering the interrelation of each execution stage of emergency disposal. Considering the subjectivity of expert analysis, triangular membership function and regional center method were combined to obtain the values of firing delays. Then isomorphic Markov chain was built from the SPN model and the stability probability linear equations were obtained. By calculating the transition utilization rate and place busy probability, the critical places and transitions were obtained. The list of critical emergency disposal stages and actions was generated, based on which countermeasures were presented for the risk reduction of gas leakage emergency disposal process in gas transmission stations.

Predicting Pavement Condition Index Using Fuzzy Logic Technique

The fuzzy logic technique is one of the effective approaches for evaluating flexible and rigid pavement distress. The process of classifying pavement distress is usually performed by visual inspection of the pavement surface or using data collected by automated distress measurement equipment. Fuzzy mathematics provides a convenient tool for incorporating subjective analysis, uncertainty in pavement condition index, and maintenance-needs assessment, and can greatly improve consistency and reduce subjectivity in this process. This paper aims to develop a fuzzy logic-based system of pavement condition index and maintenance-needs evaluation for a pavement road network by utilizing pavement distress data from the U.S. and Canada. Considering rutting, fatigue cracking, block cracking, longitudinal cracking, transverse cracking, potholes, patching, bleeding, and raveling as input variables, the variables were fuzzified into fuzzy subsets. The fuzzy subsets of the variables were considered to have triangular membership functions. The relationships between nine pavement distress parameters and PCI were represented by a set of fuzzy rules. The fuzzy rules relating input variables to the output variable of sediment discharge were laid out in the IF–THEN format. The commonly used weighted average method was employed for the defuzzification procedure. The coefficient of determination (R2), root mean squared error (RMSE), and mean absolute error (MAE) were used as the performance indicator metrics to evaluate the performance of analytical models.