Evolutionary Polynomial Regression Model Research Articles

Predictive modeling of wide-shallow RC beams shear strength considering stirrups effect using (FEM-ML) approach

This paper presents an analysis and prediction of the shear strength of wide-shallow reinforced concrete beams, utilizing Finite Element Analysis (FEA) and machine learning techniques. The methodology involves validating a detailed Finite Element Model (FEM) against experimental results, conducting a parametric study, and developing three Machine Learning prediction equations. The FEM captures concrete and steel behaviors, including cracking and crushing for concrete and linear isotropic properties for steel reinforcement. Loading and boundary conditions are defined for accuracy and validated against 13 experimental specimens, exhibiting a maximum 8% and 12% difference in loads and deflections, respectively. A parametric study generates a dataset of 77 wide beam configurations, exploring variations in beam widths, concrete strengths, compression rebars, and shear reinforcement. This dataset is used to develop machine learning models, including “Genetic Programming (GP)”, “Evolutionary Polynomial Regression (EPR)”, and “Artificial Neural Network (ANN)”. Comparative analysis reveals GP and EPR models with over 95% correlation, while the ANN model outperforms with 99% accuracy. Sensitivity analysis underscores the significant influence of concrete strength and beam aspect ratio on shear strength. In conclusion, the study demonstrates the potential of FEA and machine learning models to predict shear strength in wide-shallow reinforced concrete beams, providing valuable insights for architectural design and engineering practices and emphasizing the role of concrete strength and beam geometry in shear behavior.

An EPR model for predicting the bearing capacity of single and double-strip foundations near earth slope crests.

It is imperative to understand how foundations behave on earthen slopes to accurately predict their allowable carrying capacity in geotechnical engineering. A comprehensive finite element (FE) simulation with PLAXIS 2D was conducted to assess the effects of various parameters on the bearing capacity (BC) of single- and double-strip foundations placed near the earth's slope crest. The specified parameters include foundation width (B) and depth (Df/B); setback distance between the slope edge and foundation (b/B); soil internal friction (ϕ) and cohesion (c); slope inclination (β); and spacing between foundations (S/B). In addition, the numerically simulated database was used to develop simple mathematical expressions for predicting the capacities in both cases using evolutionary polynomial regression (EPR). The results revealed that the bearing capacity of single- and double-strip foundations increased with an increase in all studied parameters except slope inclination. For single-strip foundations, the outcomes demonstrated that slope inclination has no impact on BC when it is located 6B from the slope edge. However, under interference conditions, the critical center-to-center spacing between foundations is 3-4B, beyond which they behave as individual foundations. Additionally, EPR provides a robust method of predicting the BC of single- and double-strip foundations within slope crests based on the strong correlation of various statistical criteria between simulated and predicted results from training, validation, and testing. Finally, according to sensitivity analysis, in both single and double-strip foundations resting on an earthen slope crest, b/B, B, and ϕ are the most important input parameters that impact the output results.

Modeling the influence of lime on the unconfined compressive strength of reconstituted graded soil using advanced machine learning approaches for subgrade and liner applications.

In the field of soil mechanics, especially in transportation and environmental geotechnics, the use of machine learning (ML) techniques has emerged as a powerful tool for predicting and understanding the compressive strength behavior of soils especially graded ones. This is to overcome the sophisticated equipment, laboratory space and cost needs utilized in multiple experiments on the treatment of soils for environmental geotechnics systems. This present study explores the application of machine learning (ML) techniques, namely Genetic Programming (GP), Artificial Neural Networks (ANN), Evolutionary Polynomial Regression (EPR), and the Response Surface Methodology in predicting the unconfined compressive strength (UCS) of soil-lime mixtures. This was for purposes of subgrade and landfill liner design and construction. By utilizing input variables such as Gravel, Sand, Silt, Clay, and Lime contents (G, S, M, C, L), the models forecasted the strength values after 7 and 28 days of curing. The accuracy of the developed models was compared, revealing that both ANN and EPR achieved a similar level of accuracy for UCS after 7 days, while the GP model performed slightly lower. The complexity of the formula required for predicting UCS after 28 days resulted in decreased accuracy. The ANN and EPR models achieved accuracies of 85% and 82%, with R2 of 0.947 and 0.923, and average error of 0.15 and 0.18, respectively, while the GP model exhibited a lower accuracy of 66.0%. Conversely, the RSM produced models for the UCS with predicted R2 of more than 98% and 99%, for the 7- and 28- day curing regimes, respectively. The RSM also produced adequate precision in modelling UCS of more than 14% against the standard 7%. All input factors were found to have almost equal importance, except for the lime content (L), which had an average influence. This shows the importance of soil gradation in the design and construction of subgrade and landfill liners. This research further demonstrates the potential of ML techniques for predicting the strength of lime reconstituted G-S-M-C graded soils and provides valuable insights for engineering applications in exact and sustainable subgrade and liner designs, construction and performance monitoring and rehabilitation of the constructed civil engineering infrastructure.

Local scour depth at piles group exposed to regular waves: On the assessment of expressions based on classification concepts and evolutionary algorithms

An accurate estimation of local scour depth around piles group is inevitably essential to provide stability of marine structures. Over the past decades, many investigations have been made to understand the scouring process at piles group exposed to waves for field and experimental scales. This study aims to predict the wave-induced local scour depth by various robust Data Driven Models (DDMs) and Machine Learning Models (MLMs) developed by classification and evolutionary concepts: Model Tree (MT), Evolutionary Polynomial Regression (EPR), Multivariate Adaptive Regression Spline (MARS), and Gene-Expression Programming (GEP). From relevant literature, 125 data series were employed to provide empirical relationships for scour depth predictions. The raw variables were related to bed sediment, pile configuration, pile geometry, approaching flow, and wave characteristics. Non-dimensional parameters have been obtained through the Buckingham theorem in order to control local scour depth. In this way, the ratio of spacing between piles to pile diameter (G/D), sediment number (Ns), pile arrangement number (ratio of the number of piles parallel to the flow; m; to the number of piles normal to the flow; n), Shields parameter (θ), and Keulegan-Carpenter (KC) were considered. From the training and testing stages of Artificial Intelligence Models (AIMs), it was found that regression equation given by MARS model provided better performance (e.g., Correlation Coefficient [R] = 0.9297, Root Mean Square Error [RMSE] = 0.3489, and Scatter Index [SI] = 0.2765) than other AI models’ efficiency. Additionally, the performance of AI models was assessed for various ranges of dimensionless parameters (i.e., G/D, Ns, and θ) versus KC variation. MARS model had the best performance for G/D = 0–1 and KC < 10 (R = 0.9917 and RMSE = 0.2198) and 0.4≤θ < 0.5 and 15 < KC < 25 whereas EPR model had promising efficiency in the its highest level for Ns = 1–3 and 10 < KC ≤ 15 (R = 0.9941 and RMSE = 0.0771) than other AI models. For the sensitivity analysis, the mutation rate and number of chromosomes are examined for the GEP model, the K-fold number and number of parameters for the MARS model, and the number of algebraic terms and number of chromosomes for the EPR model. Furthermore, ranking analysis of AI models indicated that MARS model had the best performance (Ranking Performance Index [RPI] = 0.7348) and followed by GEP (0.3412), M5MT (0.2999), and EPR (0.2848).

EVOLUTIONARY POLYNOMIAL REGRESSION FOR PREDICTING THE UNCONFINED COMPRESSIVE STRENGTH OF LIME-STABILIZED

Peat has a lack of strength, making it unsuitable for foundations or road construction. The results of 104 unconfined compressive strength (UCS) on peat stabilized with various lime percentages are presented in this article. The strength performance of peat soils stabilized with lime in soaked and unsoaked conditions was also evaluated. Evolutionary Polynomial Regression (EPR) models are constructed to predict the UCS values using the data obtained from the tests. After evaluating the various functions, the EPR model with a hyperbolic tangent function is the best model for predicting the UCS. The proposed EPR models may benefit practitioners when determining the optimal amount of stabilizers to utilize in soft-ground improvement projects. In this section, we show and expand on applying the suggested models to the design of soft ground mixing. Additionally, by applying the created relationships to a new instance, the comprehensiveness of the established relationships is shown, as is the practicality of the suggested relationships for usage during the preliminary design stage. The findings of this study have important implications for estimating the quantity of lime required in ground peat improvement work.

Unconfined compressive strength of MICP and EICP treated sands subjected to cycles of wetting-drying, freezing-thawing and elevated temperature: Experimental and EPR modelling

Microbial-induced carbonate precipitation (MICP) and enzyme-induced carbonate precipitation (EICP) are two bio-cementation techniques, which are relatively new methods of ground improvement. While both techniques share some similarities, they can exhibit different overall behaviours due to the differences in urease enzyme sources and treatment methods. This paper presented 40 unconfined compressive strength (UCS) tests of MICP and EICP treated sand specimens with similar average calcium carbonate (CaCO3) content subjected to cycles of wetting-drying (WD), freezing-thawing (FT) and elevated temperature (fire resistance test – FR and thermogravimetric analysis – TG). The average CaCO3 content after a certain number of WD or FT cycles (ACn) and their corresponding UCS (qn) reduced while the mass loss increased. The EICP treated sand specimens appeared to exhibit a lower resistance to WD and FT cycles than MICP treated specimens possibly due to the presence of unbonded or loosely bonded CaCO3 within the soil matrix, which was subsequently removed during the wetting (during WD) or thawing (during FT) process. FR test and TG analysis showed a significant loss of mass and reduction in CaCO3 content with increased temperatures, possibly due to the thermal decomposition of CaCO3. A complete deterioration of the MICP and EICP treated sand specimens was observed for temperatures above 600 °C. The observed behaviours are complex and theoretical understanding is far behind to develop a constitutive model to predict qn. Therefore, a multi-objective evolutionary genetic algorithm (GA) that deals with pseudo-polynomial structures, known as evolutionary polynomial regression (EPR), was used to seek three choices from millions of polynomial models. The best EPR model produced an excellent prediction of qn with a minimum sum of squares error (SSE) of 2.392, mean squared error (MSE) of 0.075, root mean square error (RMSE) of 0.273 and a maximum coefficient of determination of 0.939.

Evaluation of the Compressive Strength of CFRP-Wrapped Circular Concrete Columns Using Artificial Intelligence Techniques

The wrapping of concrete structures with fiber polymers has been an essential part of concrete technology aimed at the improvement of concrete performance indices during the construction and lifelong usage of the structures. In this paper, a universal representative database was collected from multiple literature materials on the effect of different fiber-reinforced polymers on the confined compressive strength of wrapped concrete columns (Fcc). The collected data show that the Fcc value depends on the FRP thickness (t), tensile strength (Ftf), and elastic modulus (Ef), in addition to the column diameter (d) and the confined compressive strength of concrete (Fco). Five AI techniques were applied on the collected database, namely genetic programming (GP), three artificial neural networks (ANN) trained using three different algorithms, “back Propagation BP, gradually reduced gradient GRG and genetic algorithm GA”, and evolutionary polynomial regression (EPR). The results of the five developed predictive models show that (t) and Ftf have a major impact on the Fcc value, which presents the effect of confinement stress (t. Ftf/d) on the confined compressive strength (Fcc). Comparing the predicted values with the experimental ones showed that the GP model is the least accurate one, and the EPR model is the next least accurate, while the three ANN models have almost the same level of high accuracy, with an average error percentage of 5.8% and a coefficient of determination R2 of 0.961. The ANN model is more accurate than the EPR and GP predictive models, but they are suitable for manual calculation because they are closed-form equations.

Statističko istraživanje recikliranja plastičnog otpada ponovnom uporabom u tlu

Recycling and reusing plastic waste are key options for ameliorating its negative effects on the environment and the economy. This paper proposes evolutionary polynomial regression (EPR) as a powerful technique to predict the compressibility behavior of sand and high-density polyethylene (HDPE) mixtures. In the investigation, a series of large-scale oedometer experiments were conducted. The results of the coefficients of lateral earth pressure and volume compressibility coefficients of different mixtures were used to develop EPR models. The model parameters were evaluated by sensitivity analyses. The results showed that the best developed EPR model is robust for estimating the characteristics of sand and HDPE mixtures.

Techno-economic estimation of a non-cover box solar still with thermoelectric and antiseptic nanofluid using machine learning models

• Robust Machine Learning models were used to study the performance of PDCBSSTCDAN. • The energy effiicncy prediction with high accuracy was obtained by MARS technique. • The EPR model was the best result in exergy efficiency estimation of PDCBSSTCDAN. • The ML models could effectively detect the variations in effective variables of PDCBSSTCDAN. • The mathematical expressions of ML models could cover limitations of previous studies. With rapid rise of advancement in soft computing models, application of Machine Learning (ML) techniques has increasingly grown to successfully evaluate thermal characterizations of solar systems in the last decade. Compared with related literature, this research aimed to obtain accurate relationships based on the ML techniques for predicting the energy and exergy efficiencies of the parabolic dish concentrator box solar still fitted with thermoelectric condensing duct and antiseptic nanofluid (PDCBSSTCDAN) system. Effective variables that affect energy and exergy efficiencies are listed as the nanoparticle volume fraction, fan power, solar radiation, basin temperature, nanofluid temperature, ambient temperature, wind velocity, and productivity. By adding 0.05% and 0.1% by volume of Fe 3 O 4 @Ag nanoparticles to the basin water, the maximum production of distilled water has increased by 410 (ml/m 2 ) and 580 (ml/m 2 ), respectively, compared to the pure fluid. At the most optimal case, the cost of producing distilled water and the payback period are 0.0072 ($/L/m2) and 141 day, respectively. After having completed the development of ML techniques, empirical equations based on nature-inspired properties of ML models were obtained to estimate energy and exergy efficiencies with a reasonable degree of accuracy level. Results of ML models indicated that Evolutionary Polynomial Regression (EPR) technique yielded comparatively performance in the prediction of energy (Root Mean Square Error [RMSE] = 0.827) and exergy (Root Mean Square Error [RMSE] = 0.1078) efficiencies than Multivariate Adaptive Regression Analysis (MARS), Gene-Expression Programming (GEP), and M5Model Tree (MT). ML techniques could outperform the results of the previous investigations in terms of precision level and applicability of proposed empirical equations. Overall, the proposed equations can be conveniently utilized to perceive the physical characterizations of solar still systems.

Estimating the density of hybrid nanofluids for thermal energy application: Application of non-parametric and evolutionary polynomial regression data-intelligent techniques

There is no doubt that density is one of the most crucial thermophysical properties of hybrid nanofluids in thermal energy applications. Various research papers have been devoted to thermophysical properties of various hybrid nanofluids. However, a few of them focused on the simultaneous effects of nanoparticles, base fluids, and other factors on the density of hybrid nanofluids. In this research, a comparative study was conducted on non-parametric and evolutionary machine learning paradigms, namely, Multivariate Adaptive Regression Spline (MARS) and Evolutionary Polynomial Regression (EPR) models to accurately predict the density of a wide variety type of nanofluids in thermal energy applications. Here, for providing the predictive models, 501 data points were collected from the reliable recent literature. Besides, the Gene Expression Programming (GEP) and Multivariate Linear Regression (MLR) models were examined for validating the outcomes of MARS and EPR models. The comprehensive assessment demonstrated that the MARS outperformed the other models.

A dual search-based EPR with self-adaptive offspring creation and compromise programming model selection

Evolutionary polynomial regression (EPR) is extensively used in engineering for soil properties modeling. This grey-box technique uses evolutionary computing to produce simple, transparent and well-structured models in the form of polynomial equations that best explain the observed data. A key task is then to determine mathematical structures for modeling physical phenomena and to select the optimal EPR model. This requires an algorithm to search through the model structure space and successfully produce feasible solutions that honor a set of statistical metrics. The complexity of EPR models increases greatly, however, with the number of polynomial terms used to tune these models. In this paper, we propose an alternative EPR for modeling complex soil properties. We implement a dual search-based EPR with self-adaptive offspring creation as model structure search strategy and couple a compromise programming tool to select a model that is preferred statistically relative to models with different polynomial terms. We illustrate our method using real-world data to improve predictions of optimal moisture content and creep index for soils. Our results demonstrate that the models derived using the proposed methodology can predict soil properties with adequate accuracy, physical meaning and lower number of parameters and input variables.

More reliable predictions of clear-water scour depth at pile groups by robust artificial intelligence techniques while preserving physical consistency

This paper presents the results of an investigation on scour around pile groups under steady currents using artificial intelligence (AI) models. Namely, EPR (Evolutionary Polynomial Regression), GEP (Gene-Expression Programming), MARS (Multivariate Adaptive Regression Spline), and M5MT (M5 Model Tree) approaches were used to develop nonlinear regression equations for estimating the maximum equilibrium clear-water scour depth. In total, 321 datasets were collected from various literature sources for different pile group configurations also including the gap between piles and pile groups non-aligned with the flow direction. Results through training and testing phases showed that the MARS technique with Index of Agreement (IOA) of 0.984, Root Mean Square Error (RMSE) of 0.483, and Mean Absolute Error (MAE) of 0.250 provides more accurate estimates of the scour depth (normalized by the pile diameter) than EPR (IOA = 0.976, RMSE = 0.579, and MAE = 0.195), GEP (IOA = 0.972, RMSE = 0.628, and MAE = 0.295), and M5MT (IOA = 0.965, RMSE = 0.704, and MAE = 0.259) models. Conversely, the most frequently used literature formulas demonstrate unconvincing efficiency when wide range experimental data are considered. The sensitivity analysis, in terms of Sobol’s index, revealed that the ratio U/Uc, between the approach flow velocity, U, and the flow velocity, Uc, at the inception of sediment motion, is the most influential parameter with Total Sobol Index (TSI) of 0.514 and an opposite trend of scour with the ratio m/n (TSI = 0.023), between the number, m, of piles inline with the flow and that, n, of piles normal to the flow, was found.

Straightforward prediction for air-entry value of compacted soils using machine learning algorithms

The straightforward prediction for the air-entry value of compacted soils is practically useful, but the investigation on this issue is scarce. This study presents three alternative straightforward prediction models for the air-entry value of compacted soils using the representative machine learning algorithms of multi expression programming (MEP), evolutionary polynomial regression (EPR) and random forest (RF). Five known soil properties (i.e. sand content, fines content, plasticity index, initial water content and initial void ratio) are used as input variables. All models are developed based on a large database, covering a wide range of soil classifications. The results show that all the three proposed models are appropriate to predict the air-entry values of different compacted soils, with high prediction accuracies for both the training and the testing data. The monotonicity, the sensitivity and the robustness of the three prediction models are evaluated, showing consistency among different models with a slight difference and providing a strong support for the model feasibility. On the whole, the MEP and the EPR models are recommended for more convenient applications with explicit expression, while higher prediction accuracy may require the RF model although no explicit expression can be derived.

Intelligent modelling of clay compressibility using hybrid meta-heuristic and machine learning algorithms

Compression index Cc is an essential parameter in geotechnical design for which the effectiveness of correlation is still a challenge. This paper suggests a novel modelling approach using machine learning (ML) technique. The performance of five commonly used machine learning (ML) algorithms, i.e. back-propagation neural network (BPNN), extreme learning machine (ELM), support vector machine (SVM), random forest (RF) and evolutionary polynomial regression (EPR) in predicting Cc is comprehensively investigated. A database with a total number of 311 datasets including three input variables, i.e. initial void ratio e0, liquid limit water content wL, plasticity index Ip, and one output variable Cc is first established. Genetic algorithm (GA) is used to optimize the hyper-parameters in five ML algorithms, and the average prediction error for the 10-fold cross-validation (CV) sets is set as the fitness function in the GA for enhancing the robustness of ML models. The results indicate that ML models outperform empirical prediction formulations with lower prediction error. RF yields the lowest error followed by BPNN, ELM, EPR and SVM. If the ranges of input variables in the database are large enough, BPNN and RF models are recommended to predict Cc. Furthermore, if the distribution of input variables is continuous, RF model is the best one. Otherwise, EPR model is recommended if the ranges of input variables are small. The predicted correlations between input and output variables using five ML models show great agreement with the physical explanation.

An intelligent multi-objective EPR technique with multi-step model selection for correlations of soil properties

Current multi-objective evolutionary polynomial regression (EPR) methodology has difficulties on decision-making of optimal EPR model. This paper proposes an intelligent multi-objective optimization-based EPR technique with multi-step automatic model selection procedure. A newly developed multi-objective differential evolution algorithm (MODE) is adopted to improve the optimization performance. The proposed EPR process is composed of two stages: (1) intelligent roughing model selection and (2) model delicacy identification. In the first stage, besides two objectives (model accuracy and model complexity), the model robustness measured by robustness ratio is considered as an additional objective in the multi-objective optimization. In the second stage, a new indicator named selection index is proposed and incorporated to find the optimal model. After intelligent roughing selection and delicacy identification, the optimal EPR model is obtained considering the combined effects of correlation coefficient, size of polynomial terms, number of involved variables, robustness ratio and monotonicity. To show the practicality of the proposed EPR technique, three illustrative cases helpful for geotechnical design are presented: (a) modelling of compressibility, (b) modelling of undrained shear strength and (c) modelling of hydraulic conductivity. For each case, a practical formula with better performance in comparison with various existing empirical equations is finally provided. All results demonstrate that the proposed intelligent MODE-based EPR technique is efficient and effective.

Development of robust evolutionary polynomial regression network in the estimation of stable alluvial channel dimensions

Understanding morphological changes of a river system is a necessary part of river management plans. Accurate assessment of the stability of alluvial channels is a significant issue in the design and retrofit of urban streams adversely impacted by rapid urbanization and climate change. In the present study, an evolutionary polynomial regression (EPR) model was designed to estimate the width (W), depth (D), and slope (S) of a stable channel based on a wide range of field and experimental datasets in sand, gravel and mountain rivers. Furthermore, the effect of 4 influential input parameters of mean sediment size (d50), flow discharge (Q), the Shields parameter (τ) and free water surface slope (S) of channel on the dimensions of stable alluvial channels is investigated using EPR model and a novel designed Gene Expression Programming (GEP) model. In addition, in the present study, the performance of 15 previous popular traditional methods in estimating stable channel dimensions is reviewed and compared with the results of the proposed models. The results show that the EPR model (Root Mean Square Error (RMSE)=0.4, Bias = -0.04 and determination coefficient (R2)=0.925) has less error than GEP model (RMSE = 0.45, Bias = -0.08, R2 = 0.918) and by providing a simpler polynomial relationship than the GEP model, it is more accurate. The EPR and GEP models in addition to suggesting robust and straightforward relationships have higher accuracy than traditional models, especially in predicting the width and depth of the flow. The parametric analysis shows that Shields parameter (τ) is an effective parameter in predicting the channel slope. The developed GEP and EPR models with similar physical behavior to the best traditional methods have nonlinear variations respect to τ. The EPR relationships presented in this study can be used as an alternative to traditional relationships with higher accuracy and easier usability in the design and implementation of stable channels.

Modeling Construction labour productivity using Evolutionary Polynomial Regression

Construction projects are labour-intensive and labour costs are a substantial percentage of total budget. Impaired labour productivity causes an increase in construction project schedule. Labour productivity is one of the most frequently discussed topics in the construction industry, and modelling labour productivity by utilising different techniques has been getting more attention. It is a challenging task as it requires identifying the influencing factors as well as considering the associated interdependencies. This paper investigates the application of evolutionary polynomial regression (EPR) for modelling labour productivity in formwork installation. EPR is a data-driven hybrid modelling technique based on evolutionary computing and has been successfully applied to solving civil engineering problems. Results obtained from the EPR model were compared with the outcomes of three other methods: best subset, stepwise, and general regression neural network (GRNN). Results demonstrate the predictive superiority of the developed EPR model for nonlinear problems based on statistical performance indicators.

A new approach to modeling the behavior of frozen soils

In this paper a new approach is presented for modeling the behavior of frozen soils. A data-mining technique, Evolutionary Polynomial Regression (EPR), is used for modeling the thermo-mechanical behavior of frozen soils including the effects of confining pressure, strain rate and temperature. EPR enables to create explicit and well-structured equations representing the mechanical and thermal behavior of frozen soil using experimental data.A comprehensive set of triaxial tests were carried out on samples of a frozen soil and the data were used for training and verification of the EPR model. The developed EPR model was also used to simulate the entire stress-strain curve of triaxial tests, the data for which were not used during the training of the EPR model. The results of the EPR model predictions were compared with the actual data and it was shown that the proposed methodology can extract and reproduce the behavior of the frozen soil with a very high accuracy. It was also shown that the EPR model is able to accurately generalize the predictions to unseen cases. A sensitivity analysis revealed that the model developed from raw experimental data is able to extract and effectively represent the underlying mechanics of the behavior of frozen soils. The proposed methodology presents a unified approach to modeling of materials that can also help the user gain a deeper insight into the behavior of the materials. The main advantages of the proposed technique in modeling the complex behavior of frozen soil have been highlighted.

Pipeline failure prediction in water distribution networks using weather conditions as explanatory factors

Abstract This paper examines the impact of weather conditions on pipe failure in water distribution networks using artificial neural network (ANN) and evolutionary polynomial regression (EPR). A number of weather-related factors over 4 consecutive days are the input of the binary ANN model while the output is the occurrence or not of at least a failure during the following 2 days. The model is able to correctly distinguish the majority (87%) of the days with failure(s). The EPR is employed to predict the annual number of failures. Initially, the network is divided into six clusters based on pipe diameter and age. The last year of the monitoring period is used for testing while the remaining years since the beginning are retained for model development. An EPR model is developed for each cluster based on the relevant training data. The results indicate a strong relationship between the annual number of failures and frequency and intensity of low temperatures. The outputs from the EPR models are used to calculate the failures of the homogenous groups within each cluster proportionally to their length.

Evolutionary polynomial regression approach to predict longitudinal dispersion coefficient in rivers

The fate of pollutants in rivers is mainly affected by the longitudinal dispersion coefficient (Kx). Thus, improved Kx estimation could greatly enhance the water quality management of rivers. In this regard, evolutionary polynomial regression (EPR) was used to accurately predict Kx in rivers as a function of flow depth, channel width, and average and shear velocities. The predicted Kx by EPR modelling was compared with results obtained by more conventional Kx estimation formulas. Initial data analyses using general linear models of variance revealed that all input variables were statistically significant for Kx estimation. The calibrated EPR model showed good performance with coefficient of determination and root mean square error of 0.82 and 79 m2/s, respectively. This is better that other more conventional estimation methods. Application of sensitivity analysis for the EPR model indicated that channel width, average velocity, shear velocity, and flow depth were the main variables in descending order that affected Kx variability. The introduced EPR estimation model for Kx can be incorporated in one-dimensional water quality models for improved simulation of solute concentration in natural rivers. (Less)