Use of Evolutionary Polynomial Regression (EPR) for Prediction of Total Sediment Load of Malaysian Rivers

This study investigates the use of Evolutionary Polynomial Regression (EPR) for predicting the total sediment load of Malaysian rivers. EPR is a data-driven modelling hybrid technique, based on evolutionary computing, that has been recently used successfully in solving many problems in civil engineering. In order to apply the method for modelling the total sediment of Malaysian rivers, an extensive database obtained from the Department of Irrigation and Drainage (DID),Ministry of Natural Resources & Environment, Malaysia was sought, and unrestricted access was granted. A robustness study was performed in order to confirm the generalisation ability of the developed EPR model, and a sensitivity analysis was also conducted to determine the relative importance of model inputs. The results obtained from the EPR model were compared with those obtained from six other available sediment load prediction models. The performance of the EPR model demonstrates its predictive capability and generalisation ability to solve highly nonlinear problems of river engineering applications, such as sediment. Moreover, the EPR model produced reasonably improved results compared to those obtained from the other available sediment load methods

A new model based on evolutionary computing for predicting ultimate pure bending of steel circular tubes

In this study, the feasibility of using evolutionary computing for modelling ultimate pure bending of steel circular tubes was investigated. The behaviour of steel circular tubes under pure bending is complex and highly non-linear, and the literature has a number of solutions, most of which are difficult to use in routine design practice as they do not provide a closed-form solution. This work presents a new approach, based on evolutionary polynomial regression (EPR), for developing a simple and easy-to-use formula for prediction of ultimate pure bending of steel circular tubes. The EPR model was calibrated and verified using a large database that was obtained from the literature and comprises a series of 104 pure bending tests conducted on fabricated and cold-formed tubes. The predicted ultimate pure bending of steel circular tubes using this model can be obtained from a number of inputs including the tube thickness, tube diameter, steel yield strength and modulus of elasticity of steel. A sensitivity analysis was carried out on the developed EPR model to investigate the model generalisation ability (or robustness) and relative importance of model inputs to its output. Predictions from the EPR model were compared with those obtained from artificial neural network (ANN) models previously developed by the authors, as well as most available codes and standards.The results indicate that the EPR model is capable of predicting the ultimate pure bending of steel circular tubes with a high degree of accuracy and outperforms most available codes and standards. The results also indicate that the performance of the EPR model agrees well with that of the previously developed ANN models. It was also shown that the EPR model was able to learn the complex relationship between the ultimate pure bending and most influencing factors, and render this knowledge in the form of a simple and transparent function that can be readily used by practising engineers. The advantages of the proposed EPR technique over the ANN approach were also addressed

Multiscale soft computing-based model of shear strength of steel fibre-reinforced concrete beams

Concrete is weak in tension, so steel fibres are added to the concrete members to increase shear capability. The shear capacity of steel fibre-reinforced concrete (SFRC) beams is crucial when building reinforced concrete structures. Creating a precise equation to determine the shear resistance of SFRC beams is challenging since many factors can influence the shear capacity of these beams. In addition, the precision available equations to predict the shear capacity are examined. The current research aims to examine the available equations and propose novel and more accurate model to predict the shear capacity of SFRC beams. An innovative evolutionary polynomial regression analysis (EPR- MOGA) is utilized to propose the new equation. The proposed equation offered improved prediction and increased accuracy compared to available equations, where it scored a lower mean absolute error (MAE) and root mean square error (RMSE), a mean (μ) close to the optimum value of 1.0 and a higher coefficient of determination (R 2) when a comparison with literature was conducted. Therefore, the new equation can be employed to assure more resilient and optimized design calculations due to their improved performance.</p

Evaluation of Strength behaviour of Cement-RHA Stabilized and Polypropylene Fiber Reinforced Clay-Sand Mixtures

In this paper, regarding the high availability of rice husk ash (RHA) in Guilan province, also, to decrease the geo-environmental issues caused by dumping RHA in the environment, different clay-sand mixtures are stabilized using the combination of cement and RHA. Polypropylene (PP) fibers are also used to decrease the growth of tensile cracks and increase the overall strength of samples. As the main scope, effect of sand content (in different conditions: with and without presence of RHA) on the compressive strength of stabilized and reinforced samples is investigated. In this regard, 28 day cured clay-sand samples are prepared and unconfined compressive strength (UCS) tests are conducted and the results are compared. It is obtained that with addition of 20% sand to the clay samples, their UCS increases in both cases of non-RHA and RHA-stabilized samples. Moreover, such behavior has been observed with the length of studied PP fibers. As the second scope, based on the conducted UCS tests on the 7-, 28- and 90- day cured clay samples, compressive strength of non-RHA samples are almost completely achieved in a 28-day curing period, while samples containing RHA continue to strengthening after such a period toward a 90-day curing period. Next, a simple relationship for the prediction of UCS of cement-RHA stabilized and PP reinforced clay is presented based on the evolutionary polynomial regression (EPR) technique. This relationship can be efficiently applied by construction engineers to obtain the appropriate mixture design for the stabilization of clay with cement, RHA and PP fibers

Statistical investigation on plastic waste recycling by reusing in soil

Recikliranje i ponovna uporaba plastičnog otpada ključna je za ublažavanje njegovog negativnog utjecaja na okoliš i gospodarstvo. Ovaj rad predlaže evolucijsku polinomnu regresiju (eng. Evolutionary Polynomial Regression - EPR)) kao snažnu metodu za predviđanje stišljivosti pijeska pomiješanog s polietilenom visoke gustoće (HDPE). U svrhu istraživanja proveden je niz opsežnih pokusa pomoću velikog edometra. Za izradu EPR modela korišteni su rezultati koeficijenta bočnog pritiska tla i modula promjene volumena mješavina. Kako bi se ocijenili parametri modela, provedene su analize osjetljivosti. Rezultati su pokazali da je EPR model učinkovit za procjenu karakteristika mješavine.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

Nonparametric liquefaction triggering and postliquefaction deformations

This study evaluates granular liquefaction triggering case-history data using a nonparametric approach. This approach assumes no functional form in the relationship between liquefied and nonliquefied cases as measured using cone penetration test (CPT) data. From a statistical perspective, this allows for an estimate of the threshold of liquefaction triggering unbiased by prior functional forms, and also provides a platform for testing existing published methods for accuracy and precision. The resulting threshold exhibits some unique trends, which are then interpreted based on postliquefaction deformation behavior. The range of postliquefaction deformations are differentiated into three zones: (1) large deformations associated with metastable conditions; (2) medium deformations associated with cyclic strain failure; and (3) small deformations associated with cyclic stress failure. Deformations are further defined based on the absence or presence of static driving shear stresses. This work presents a single simplified framework that provides quantitative guidance on triggering and qualitative guidance on deformation potential for quick assessment of risks associated with seismic soil liquefaction failure

Lateral load bearing capacity modelling of piles in cohesive soils in undrained conditions; an intelligent evolutionary approach

The complex behaviour of fine-grained materials in relation with structural elements has received noticeable attention from geotechnical engineers and designers in recent decades. In this research work an evolutionary approach is presented to create a structured polynomial model for predicting the undrained lateral load bearing capacity of piles. The proposed evolutionary polynomial regression (EPR) technique is an evolutionary data mining methodology that generates a transparent and structured representation of the behaviour of a system directly from raw data. It can operate on large quantities of data in order to capture nonlinear and complex relationships between contributing variables. The developed model allows the user to gain a clear insight into the behaviour of the system. Field measurement data from literature was used to develop the proposed EPR model. Comparison of the proposed model predictions with the results from two empirical models currently being implemented in design works, a neural network-based model from literature and also the field data shows that the EPR model is capable of capturing, predicting and generalising predictions to unseen data cases, for lateral load bearing capacity of piles with very high accuracy. A sensitivity analysis was conducted to evaluate the effect of individual contributing parameters and their contribution to the predictions made by the proposed model. The merits and advantages of the proposed methodology are also discussed

A surrogate model for simulation–optimization of aquifer systems subjected to seawater intrusion

This study presents the application of Evolutionary Polynomial Regression (EPR) as a pattern recognition system to predicate the behavior of nonlinear and computationally complex aquifer systems subjected to seawater intrusion (SWI). The developed EPR models are integrated with a multi objective genetic algorithm to examine the efficiency of different arrangements of hydraulic barriers in controlling SWI. The objective of the optimization is to minimize the economic and environmental costs. The developed EPR model is trained and tested for different control scenarios, on sets of data including different pumping patterns as inputs and the corresponding set of numerically calculated outputs. The results are compared with those obtained by direct linking of the numerical simulation model with the optimization tool. The results of the two above-mentioned simulation–optimization (S/O) strategies are in excellent agreement. Three management scenarios are considered involving simultaneous use of abstraction and recharge to control SWI. Minimization of cost of the management process and the salinity levels in the aquifer are the two objective functions used for evaluating the efficiency of each management scenario. By considering the effects of the unsaturated zone, a subsurface pond is used to collect the water and artificially recharge the aquifer. The distinguished feature of EPR emerges in its application as the metamodel in the S/O process where it significantly reduces the overall computational complexity and time. The results also suggest that the application of other sources of water such as treated waste water (TWW) and/or storm water, coupled with continuous abstraction of brackish water and its desalination and use is the most cost effective method to control SWI. A sensitivity analysis is conducted to investigate the effects of different external sources of recharge water and different recovery ratios of desalination plant on the optimal results

Development and Applications of Self-learning Simulation in Finite Element Analysis

Numerical analysis such as the finite element analysis (FEA) have been widely used to solve many engineering problems. Constitutive modelling is an important component of any numerical analysis and is used to describe the material behaviour. The accuracy and reliability of numerical analysis is greatly reliant on the constitutive model that is integrated in the finite element code. In recent years, data mining techniques such as artificial neural network (ANN), genetic programming (GP) and evolutionary polynomial regression (EPR) have been employed as alternative approach to the conventional constitutive modelling. In particular, EPR offers great advantages over other data mining techniques. However, these techniques require a large database to learn and extract the material behaviour. On the other hand, the link between laboratory or field tests and numerical analysis is still weak and more investigation is needed to improve the way that they matched each other. Training a data mining technique within the self-learning simulation framework is currently considered as one of the solutions that can be utilised to accurately represent the actual material behaviour. In this thesis an EPR based machine learning technique is utilised in the heart of the self-learning framework with an automation process which is coded in MATLAB environment. The methodology is applied to simulate different material behaviour in a number of structural and geotechnical applications. Two training strategies are used to train the EPR in the developed framework, total stress-strain and incremental stress-strain strategies. The results show that integrating EPR based models in the framework allows to learn the material response during the self-learning process and provide accurate predictions to the actual behaviour. Moreover, for the first time, the behaviour of a complex material, frozen soil, is modelled based on the EPR approach. The results of the EPR model predictions are compared with the actual data and it is shown that the proposed model can capture and reproduce the behaviour of the frozen soil with a very high accuracy. The developed EPR based self-learning methodology presents a unified approach to material modelling that can also help the user to gain a deeper insight into the behaviour of the materials. The methodology is generic and can be extended to modelling different engineering materials

An evolutionary based approach for assessment of earthquake-induced soil liquefaction and lateral displacement

Prediction of liquefaction and the resulting lateral displacement is a complex engineering problem due to heterogeneous nature of soils and participation of a large number of factors involved. In this paper new models are developed, based on evolutionary polynomial regression (EPR), for assessment of liquefaction potential and lateral spreading. The models developed for liquefaction and lateral spreading are compared to those obtained from neural network and linear regression based techniques. It is shown that the developed models are able to learn the complex relationship between either of these problems and their contributing factors in the form of a function with high level of accuracy (mostly in excess of 90%). The results of the EPR model developed for the liquefaction determination are used to find a novel 3-D boundary surface that discriminates between the cases of occurrence and non-occurrence of liquefaction. The developed boundary surface is employed to calculate the factor of safety against liquefaction occurrence