Multi-gene Genetic Programming Research Articles

A penta-linear backbone model for unbonded post-tensioned concrete masonry shear walls

Unbonded post-tensioned concrete masonry (UPCM) shear walls are an effective seismic force resisting system due to their ability to mitigate expected damage through their self-centering capabilities. A few design procedures were proposed to predict the in-plane flexural response of UPCM walls, albeit following only basic mechanics and/or extensive iterative methods. Such procedures, however, may not be capable of capturing the complex nonlinear relationships between different parameters that affect UPCM walls’ behavior or are tedious to be adopted for design practice. In addition, the limited datasets used to validate these procedures may cast doubt on their accuracy and validity for new, unseen data, further hindering their adoption by practitioners and design standards. To address these issues, an experimentally-validated nonlinear numerical model was adopted in this study and subsequently employed to simulate 95 UPCM walls with different design parameters to compensate for the lack of relevant experimental data in the current literature. Guided by mechanics and using this database, an evolutionary algorithm, multigene genetic programming (MGGP), was adopted to uncover the relationships controlling the response of UPCM walls, and subsequently develop simplified closed-form wall behavior prediction expressions. Specifically, through integrating MGGP and basic mechanics, a penta-linear backbone model was developed to predict the load-displacement backbone for UPCM walls up to 20% strength degradation. Compared to existing predictive procedures, the prediction accuracy of the developed model and its closed-form nature are expected to facilitate better understanding of such wall behaviors and subsequently open the gate to further incorporate other experimental and numerical results to ultimately incorporate UPCM in mainstream designs in the near future.

Unconfined compressive strength of geopolymers based soil: Model development using gene expression programming and a comparison with other computer-based approaches

The present work aims to propose a model for calculating unconfined compressive strength (UCS) of soil stabilized by geopolymer based on gene expression programming (GEP). The new model is compared with earlier models developed from artificial neural networks (ANN), multivariable regression analysis (MVR), multi-gene genetic programming (MGGP), and support vector machines (SVM) models. The results indicate that the GEP model is superior to MVR and MGGP models and quite comparable to ANN and SVM models. The study demonstrates the robustness of the GEP model through parametric analysis. Additionally, this model is simpler and easier to use in practice.

Integrated approach to assessing strength in slag-based geopolymer mortars: experimental study and modeling with advanced techniques

The study consists of two main parts. In the initial phase, a variety of slag-based geopolymer mortars with different activator concentrations were prepared. These mortars underwent curing in both water and air environments for periods of 3, 7, 28, and 90 days, after which their compressive strength was evaluated at the conclusion of each curing interval. The second phase of the study is dedicated to the development of innovative models for estimating the compressive strength based on the data gathered. To achieve this, a range of techniques including multi-gene genetic programming (MGGP), artificial neural networks (ANN), XGBoost, SVM-Gauss, long short-term memory (LSTM), and convolutional neural networks (CNN) were employed to formulate a model capable of estimating compressive strength accurately. The study made use of various performance evaluation metrics such as mean squared error (MSE), root mean squared error (RMSE), R-squared, mean absolute error (MAE), and scatter index (SI) to assess the precision of the MGGP method in evaluating slag-based geopolymer mortars under both water and air curing conditions. The findings indicate that the equations generated by the MGGP method exhibit a high level of precision when juxtaposed with experimental outcomes. This research endeavors to enhance the prediction of compressive strength in geopolymer mortars, a subject that has garnered significant interest in scholarly literature.

Data-Driven Identification of Crane Dynamics Using Regularized Genetic Programming

The meaningful problem of improving crane safety, reliability, and efficiency is extensively studied in the literature and targeted via various model-based control approaches. In recent years, crane data-driven modeling has attracted much attention compared to physics-based models, particularly due to its potential in real-time crane control applications, specifically in model predictive control. This paper proposes grammar-guided genetic programming with sparse regression (G3P-SR) to identify the nonlinear dynamics of an underactuated crane system. G3P-SR uses grammars to bias the search space and produces a fixed number of candidate model terms, while a local search method based on an l0-regularized regression results in a sparse solution, thereby also reducing model complexity as well as reducing the probability of overfitting. Identification is performed on experimental data obtained from a laboratory-scale overhead crane. The proposed method is compared with multi-gene genetic programming (MGGP), NARX neural network, and Takagi-Sugeno fuzzy (TSF) ARX models in terms of model complexity, prediction accuracy, and sensitivity. The G3P-SR algorithm evolved a model with a maximum mean square error (MSE) of crane velocity and sway prediction of 1.1860 × 10−4 and 4.8531 × 10−4, respectively, in simulations for different testing data sets, showing better accuracy than the TSF ARX and MGGP models. Only the NARX neural network model with velocity and sway maximum MSEs of 1.4595 × 10−4 and 4.8571 × 10−4 achieves a similar accuracy or an even better one in some testing scenarios, but at the cost of increasing the total number of parameters to be estimated by over 300% and the number of output lags compared to the G3P-SR model. Moreover, the G3P-SR model is proven to be notably less sensitive, exhibiting the least deviation from the nominal trajectory for deviations in the payload mass by approximately a factor of 10.

Development of multiple explicit data-driven models for accurate prediction of CO2 minimum miscibility pressure

This study presents utilization of multiple data-driven models for predicting CO2 minimum miscibility pressure (MMP). The aim is to address the issue of existing models lacking explicit presentation. With a database of 155 data points, five models were developed using artificial neural network (ANN), multigene genetic programming (MGGP), support vector regression (SVR), multivariate adaptive regression splines (MARS), and multiple linear regression (MLR). Comparative analysis was conducted using statistical metrics (R2, MSE, MAE, RMSE), and sensitivity analysis was performed on input variables. The results showed that ANN and SVR had comparable predictive performance (ANN: R2 = 0.982, MSE = 0.00676, MAE = 0.9765, RMSE = 0.082), SVR (R2 = 0.935, MSE = 0.0041, MAE = 0.72, RMSE = 0.064) followed by MARS, MLR, and MGGP. Sensitivity analysis revealed that reservoir temperature was the most influential parameter across all models, except for the MLR algorithm where injected CO2 amount was crucial. These models can be used for a wide range of CO2 MMP ranging from 940 psi to 5830 psi, thus rendering them useful for any reservoir globally. These models offer improved accuracy and computational efficiency compared to existing ones, potentially reducing costs associated with laboratory experiments and providing rapid and precise CO2 MMP predictions.

Prediction of effective equivalent linear temperature gradients in bonded concrete overlays of asphalt pavements

PurposeThe time-varying equivalent linear temperature gradient (ELTG) significantly affects the development of faulting and must therefore be accounted for in pavement design. The same is true for faulting of bonded concrete overlays of asphalt (BCOA) with slabs larger than 3 x 3 m. However, the evaluation of ELTG in Mechanistic-Empirical (ME) BCOA design is highly time-consuming. The use of an effective ELTG (EELTG) is an efficient alternative to calculating ELTG. In this study, a model to quickly evaluate EELTG was developed for faulting in BCOA for panels 3 m or longer in size, whose faulting is sensitive to ELTG.Design/methodology/approachA database of EELTG responses was generated for 144 BCOAs at 169 locations throughout the continental United States, which was used to develop a series of prediction models. Three methods were evaluated: multiple linear regression (MLR), artificial neural networks (ANNs), and multi-gene genetic programming (MGGP). The performance of each method was compared, considering both accuracy and model complexity.FindingsIt was shown that ANNs display the highest accuracy, with an R2 of 0.90 on the validation dataset. MLR and MGGP models achieved R2 of 0.73 and 0.71, respectively. However, these models consisted of far fewer free parameters as compared to the ANNs. The model comparison performed in this study highlights the need for researchers to consider the complexity of models so that their direct implementation is feasible.Originality/valueThis research produced a rapid EELTG prediction model for BCOAs that can be incorporated into the existing faulting model framework.

Genetic programming expressions for effluent quality prediction: Towards AI-driven monitoring and management of wastewater treatment plants

Continuous effluent quality prediction in wastewater treatment processes is crucial to proactively reduce the risks to the environment and human health. However, wastewater treatment is an extremely complex process controlled by several uncertain, interdependent, and sometimes poorly characterized physico-chemical-biological process parameters. In addition, there are substantial spatiotemporal variations, uncertainties, and high non-linear interactions among the water quality parameters and process variables involved in the treatment process. Such complexities hinder efficient monitoring, operation, and management of wastewater treatment plants under normal and abnormal conditions. Typical mathematical and statistical tools most often fail to capture such complex interrelationships, and therefore data-driven techniques offer an attractive solution to effectively quantify the performance of wastewater treatment plants. Although several previous studies focused on applying regression-based data-driven models (e.g., artificial neural network) to predict some wastewater treatment effluent parameters, most of these studies employed a limited number of input variables to predict only one or two parameters characterizing the effluent quality (e.g., chemical oxygen demand (COD) and/or suspended solids (SS)). Harnessing the power of Artificial Intelligence (AI), the current study proposes multi-gene genetic programming (MGGP)-based models, using a dataset obtained from an operational wastewater treatment plant, deploying membrane aerated biofilm reactor, to predict the filtrated COD, ammonia (NH4), and SS concentrations along with the carbon-to-nitrogen ratio (C/N) within the effluent. Input features included a set of process variables characterizing the influent quality (e.g., filtered COD, NH4, and SS concentrations), water physics and chemistry parameters (e.g., temperature and pH), and operation conditions (e.g., applied air pressure). The developed MGGP-based models accurately reproduced the observations of the four output variables with correlation coefficient values that ranged between 0.98 and 0.99 during training and between 0.96 and 0.99 during testing, reflecting the power of the developed models in predicting the quality of the effluent from the treatment system. Interpretability analyses were subsequently deployed to confirm the intuitive understanding of input-output interrelations and to identify the governing parameters of the treatment process. The developed MGGP-based models can facilitate the AI-driven monitoring and management of wastewater treatment plants through devising optimal rapid operation and control schemes and assisting the plants’ operators in maintaining proper performance of the plants under various normal and disruptive operational conditions.

Process optimization, multi-gene genetic programming modeling and reliability assessment of bioactive extracts recovery from Phyllantus emblica

This study investigates the feasibility of extracting bioactive antioxidants from Phyllantus emblica leaves using a combination of ethanol-water mixture (0-100%) and heat-assisted extraction technology (HAE-T). Operating temperature (30-50°C), solid-to-liquid ratio (1:20-1:60 g/mL), and extraction time (45-180 min) were varied to determine their effects on extract total phenolic content (TPC), yield (EY), and antioxidant activity (AA). The Box-Behnken experimental design (BBD) within response surface methodology (RSM) was employed, with multi-objective process optimization using the desirability function algorithm to find the optimal process variables for maximizing TPC, EY, and AA simultaneously. The extraction process was modeled using BBD-RSM and multi-gene genetic programming (MGGP) algorithm, with model reliability assessed via Monte Carlo simulation. HPLC characterization identified betulinic acid, gallic acid, chlorogenic acid, caffeic acid, ellagic acid, and ferulic acid as bioactive constituents in the extract. The study found that a 50% ethanol solution yielded the best extraction efficiency. The optimal process parameters for maximum EY (21.6565%), TPC (67.116 mg GAE/g), and AA (3.68583 µM AAE/g) were determined as OT of 41.61°C, S:L of 1:60 g/mL, and ET of 180 min. Both BBD-RSM and MGGP-based models satisfactorily predicted the observed process responses, with BBD-RSM models showing slightly better performance. Reliability analysis indicated high certainty in the predictions, with BBD-RSM models achieving 99.985% certainty for TPC, 97.569% for EY, and 98.661% for AA values.

Application of multi-gene genetic programming to the prognosis prediction of COVID-19 using routine hematological variables

Identifying patients who may develop severe COVID-19 has been of interest to clinical physicians since it facilitates personalized treatment and optimizes the allocation of medical resources. In this study, multi-gene genetic programming (MGGP), as an advanced artificial intelligence (AI) tool, was used to determine the importance of laboratory predictors in the prognosis of COVID-19 patients. The present retrospective study was conducted on 1455 patients with COVID-19 (727 males and 728 females), who were admitted to Allameh Behlool Gonabadi Hospital, Gonabad, Iran in 2020–2021. For each patient, the demographic characteristics, common laboratory tests at the time of admission, duration of hospitalization, admission to the intensive care unit (ICU), and mortality were collected through the electronic information system of the hospital. Then, the data were normalized and randomly divided into training and test data. Furthermore, mathematical prediction models were developed by MGGP for each gender. Finally, a sensitivity analysis was performed to determine the significance of input parameters on the COVID-19 prognosis. Based on the achieved results, MGGP is able to predict the mortality of COVID-19 patients with an accuracy of 60–92%, the duration of hospital stay with an accuracy of 53–65%, and admission to the ICU with an accuracy of 76–91%, using common hematological tests at the time of admission. Also, sensitivity analysis indicated that blood urea nitrogen (BUN) and aspartate aminotransferase (AST) play key roles in the prognosis of COVID-19 patients. AI techniques, such as MGGP, can be used in the triage and prognosis prediction of COVID-19 patients. In addition, due to the sensitivity of BUN and AST in the estimation models, further studies on the role of the mentioned parameters in the pathophysiology of COVID-19 are recommended.

Multigene genetic programming approach for modelling and optimisation of removal of heavy metals from ash pond water using cyanobacterial-microalgal consortium

ABSTRACT Heavy metals such as Lead(II), Nickel(II), Manganese(II), Cadmium(II), Chromium(VI), and others are leached from coal ash in thermal power plants, contaminating ash pond water and subsequently contaminating groundwater. Phycoremediation using microalgae or cyanobacteria is an emerging technology to remove such heavy metals from ash pond water. The present study aims at development of an accurate data-driven Genetic Programing (GP) approach for modelling of the phycoremediation process for abatement of the above-mentioned heavy metals using a consortium comprising of a cyanobacterium Synechococcus sp. and green algae Chlorella sp. The developed model was used to find a relation between the average percentage removal of metals with all input parameters such as the initial metal concentrations, pH, and days. To maximise metal removal, Genetic Algorithm (GA) optimisation technique was applied to determine optimal values of input parameters. These optimum input parameters are difficult to get through experimentation using the trial and error method. The established modelling and optimisation technique is generic and can be applied to any other experimental study.

Application of multigene genetic programming and water evaporation optimization technique for modeling and optimization of removal of heavy metals from ash pond water using cyanobacterial consortium

Abstract Heavy metals such as Lead(II), Nickel(II), Manganese(II), Cadmium(II), Chromium(VI), etc., are leached from the coal ash of thermal power plants. These metals contaminate ash pond water and subsequently contaminate groundwater. Phycoremediation using microalgae/cyanobacteria is an emerging technology for removal of heavy metals. The present study aims at phycoremediation of the said heavy metals from ash pond water using cyanobacterial consortium of Limnococcus limneticus and Leptolyngbya subtilis followed by the development of an accurate data-driven Multigene Genetic Programing (MGGP) approach for modeling and optimization of the process. The developed model was used to obtain a correlation between the average removal of metals and biomass production with all input factors such as the initial metal concentrations, pH, and days of incubation. To maximize metal removal and biomass production, the Water Evaporation Optimization (WEO) technique was applied to determine optimal values of input parameters. The application of WEO for the optimization of the phycoremediation process is the first of its kind and here lies the novelty of the study.

A Survey on Lung Cancer Prediction

Cancerous tissue that develops in lung is called a lung cancer. It's the primary reason people die from cancer, everywhere, with smoking being the primary cause of the disease. Lung cancer prediction involves using various methods and technologies is to identify the individuals who are at higher risk of developing the disease. The goal of lung cancer prediction is to detect the disease early, when it is most treatable, or to prevent it from developing altogether. Machine learning may be used to forecast the risk that someone will acquire lung cancer by analysing vast volumes of data to find patterns. In this study, a comparison of various ML-based algorithms for detecting lung cancer has been published. The techniques were used to look for cancer. There has been an explosion in the number of techniques available in recent years for diagnosing lung cancer. The majority of these techniques make use of CT scan pictures, while some make use of x-ray images. In addition, many different classifier strategies are combined with a wide variety of segmentation techniques in order to employ image recognition for the purpose of locating lung cancer nodules. According to the findings of this research, multi-gene genetic programming is superior to other approaches in terms of producing reliable outcomes.

Genetic atom search-optimized in vivo bioelectricity harnessing from live dragon fruit plant based on intercellular two-electrode placement

Bioelectricity is a promising alternative renewable energy source that can be produced from live plants and trees. However, previous experimental studies mostly applied non-sustainable bioelectricity extraction techniques from cut-off stem or leaves and neglected the optimum placement of electrodes for maximizing energy extraction without impeding plant growth. Electrode placement and penetration are crucial in energy extraction since they greatly influence electrical generated output enhancement. Relatively, along with the common plants used for bioelectricity extraction, the dragon fruit tree has the potential to be explored as an alternative bioelectricity source since it is widely abundant in many regions. With that, this work introduced a novel integrated genetic-population metaheuristic-based optimization model that was developed centered on in vivo stem bioelectricity extraction from dragon fruit tree to determine the exact optimum distance of silver-coated copper pin-type anodes and cathodes for maximum bioelectricity extraction through intercellular across vascular bundle (icVB) and inter-parenchymal cells (iPC) electrode penetration techniques, and incorporated the cradle-to-gate Life Cycle Assessment methodology to properly account the environmental impacts of the two intercellular penetration approaches. Multigene genetic programming was performed to formulate the fitness function followed by a comparative atom search (ASO), shuffle frog-leaping, and elephant herding-based bioelectricity harnessing optimization. Thus, ASO demonstrated the highest attainable fitness value and conformed well with both electrode placement treatments. This subsequently verified that ASO-based iPC penetration, yielding 58.923 J, surpasses icVB, which only yielded 13.909 J in terms of the total harnessed energy stored throughout the 30-day experiment. Overall, the genetic ASO-iPC with an electrode distance of 4.488 inches produced a higher yield of harnessed bioelectricity while incurring no significant damage and causing fewer environmental impacts compared to the ASO-icVB treatment. This developed technique can minimize greenhouse gas emissions while also expanding the application of evolutionary computing in agriculture and alternative energy domains.

A New Multi-Objective Genetic Programming Model for Meteorological Drought Forecasting

Drought forecasting is a vital task for sustainable development and water resource management. Emerging machine learning techniques could be used to develop precise drought forecasting models. However, they need to be explicit and simple enough to secure their implementation in practice. This article introduces a novel explicit model, called multi-objective multi-gene genetic programming (MOMGGP), for meteorological drought forecasting that addresses both the accuracy and simplicity of the model applied. The proposed model considers two objective functions: (i) root mean square error and (ii) expressional complexity during its evolution. While the former is used to increase the model accuracy at the training phase, the latter is assigned to decrease the model complexity and achieve parsimony conditions. The model evolution and verification procedure were demonstrated using the standardized precipitation index obtained for Burdur City, Turkey. The comparison with benchmark genetic programming (GP) and multi-gene genetic programming (MGGP) models showed that MOMGGP provides the same forecasting accuracy with more parsimony conditions. Thus, it is suggested to utilize the model for practical meteorological drought forecasting.

Machine learning-based prediction of scour depth around different-shaped bridge abutments

Accurate assessment of scour depth around bridge abutments is crucial to reasonable design of abutment structures. In this study, machine learning (ML) models are implemented, including M5′ model tree (M5′MT), multivariate adaptive regression spline (MARS), locally weighted polynomial regression (LWPR) and multigene genetic programming (MGGP) to predict scour depth around vertical-wall, 45° wing-wall and semicircular bridge abutments. Published experimental data are adopted, with four input parameters considered for the prediction of relative scour depth. The optimal input combination for each model is first determined using correlation and sensitivity analyses; results reveal that MGGP exhibits the best agreement with experimental data for vertical-wall and semicircular abutments, whereas LWPR outperforms the other models for the 45° wing-wall abutment. In addition, compared with the empirical equations and ML models employed in the literature, the accuracy of scour depth prediction is significantly improved with the ML models used in this study. Considering the comprehensive performance for all types of abutments in terms of accuracy, reliability and interpretability, MGGP is recommended as the representative of the implemented ML models with its mean absolute percentage error of 2.40% for a vertical-wall abutment, 3.95% for a 45° wing-wall abutment and 3.85% for a semicircular abutment.

Modeling, optimization and comparative study on abatement of fluoride from synthetic solution using activated laterite soil and fly ash

The supply of potable water by proper treatment of fluoride laden ground water is a challenging task. Batch experiments were executed with activated laterite soil and fly ash individually for removal of fluoride. The process parameters such as particle size (100–620 μm), dose of adsorbent (10–60 g/L), initial concentration of fluoride (2–12 mg/L), agitation speed (20–120 rpm), contact time (0.5–10 h) and pH (4.5–9.5) were investigated and it was observed that activated laterite soil had better fluoride removal efficiency than fly ash. At pH 4.5, contact time 10 h, particle size 100 μm, adsorbent dose 60 g/L, initial concentration of fluoride 12 mg/L, and agitation speed 120 rpm, maximum removal efficiencies of fluoride using activated laterite soil and fly ash were found as 85.91 ± 0.62% and 77.1 ± 0.39%, respectively. Kinetic study was performed and Pseudo 2nd order kinetic model was found to fit the kinetic data best. The Freundlich isotherm model fit the equilibrium data fairly well. The values of adsorption equilibrium constant as used in Freundlich isotherm model vis-à-vis adsorption capacity (KF) for activated laterite soil and fly ash were obtained as 0.1331 and 0.0772 ((mgg−1)(Lmg−1)1/n). A thermodynamic analysis was conducted to examine the process behaviour. In order to determine whether the adsorbent may be used for further cases, regeneration of the adsorbents was also done. Generally, two types of adsorption mechanisms happen like electrostatic attraction and ion exchange. The electrostatic force of attraction favours the adsorption of negatively charged fluoride ions on positively charged adsorbent surfaces. In ion exchange, fluoride ions are exchanged by hydroxyl and hydronium ion. Based on experimental data, Multi-Gene Genetic Programming (MGGP) model could accurately predict the removal efficiency of fluoride under various operating situations. Finally, using Genetic Algorithm (GA) optimization the maximum output values for both adsorbents were estimated as 99.14% and 99.02%.

Prediction of tapered steel plate girders shear strength using multigene genetic programming

Tapered steel plate girders have been widely used in the construction of bridges and heavy industry structures. However, predicting their structural behavior, particularly in shear, is challenging due to their non-prismatic sections and ill-understood influencing factors. Therefore, simplified analytical and classic regression techniques may not be able to capture the underlying nonlinear relationships controlling the shear behavior of tapered plate girders. In this study, multigene genetic programming (MGGP) was utilized to explore such complex relationships, and subsequently develop robust predictive expressions for the tapered steel plate girders shear strength. Attributed to the lack of a large experimental dataset, a nonlinear finite element model (FEM) was first developed and validated against available experimental results in literature. The FEM was subsequently employed to generate a matrix of 211 numerical results to augment 200 more FEM results compiled from previous studies, to cover a wider range of design parameters. The entire dataset was then used in the training and testing of the MGGP predictive expressions. The prediction accuracy of the developed expressions was evaluated against that of other existing expressions. The results showed that the adopted MGGP approach, guided be mechanics understanding, produced elegant predictive expressions with high level of accuracy and generalizability compared to other existing ones examined herein. As such, the developed expressions present an efficient prediction tool that can be adopted by design standards for estimating the ultimate shear strength of tapered steel girders. Finally, reliability analysis is performed on the developed expressions to introduce strength reduction factors in order to satisfy target design conservatism.

Experimental study and gradient-based ensemble intelligent computing to investigate effect of ultrasound on rheological behavior of bio-based phase change materials

The influence of ultrasound on the rheological behavior of new phase change material (PCM) is investigated. The PCMs are made from natural biological materials containing silicon carbide (SiC) nanoparticles or expanded graphite (EG) powders with weight fractions of 0, 0.05, 0.1, and 0.2 %. The rheological behavior of PCM composites is tested by a rheometer with shear rates of 10 up to 250 rpm. Surveys showed the smallest PCMs' viscosity at shear rates over 100 rpm is obtained for the highest sonication time. Results depict an ignorable change in the dynamic viscosity of PCMs with shear rate, and the shear thinning behavior reduces the PCMs' viscosity by raising the shear rate by 10–140 rpm. Results for EG/PCM and SiC/PCM composites showed non-Newtonian behavior at low shear rates. However, steadiness in viscosity at a shear rate higher than 140 rpm is observed for the Newtonian behavior of PCMs. The other outstanding consequence of this study is the development of novel robust machine learning, namely CatBoost, to simulate the rheological behavior of understudy nano-PCMs based on the weight fraction, sonication time, and shear rate. The multigene genetic programming (MGGP) as a comparative model is adopted to validate the primary model and provide a relationship for estimating the viscosity of each nano-PCMs. Simulation results revealed the superiority of the CatBoost (PCM-EG: R = 0.978 and RMSE = 1.739 mPa·s; PCM-SiC: R = 0.996 and RMSE = 0.374 mPa·s) to the MGGP (PCM-EG: R = 0.967 and RMSE = 2.103 mPa·s; PCM-SiC: R = 0.992 and RMSE = 2.368) in PCM composites.

Developing deterministic and probabilistic prediction models to evaluate high-temperature performance of modified bitumens

This study aims to develop deterministic and probabilistic prediction models for the multiple stress creep and recovery (MSCR) test. For this purpose, crumb rubber, polyphosphoric acid, and styrene–butadiene–styrene bitumen modifiers have been used with different dosages to modify high-temperature performance of PG 58–28 and PG 64–22 base bitumens. The MSCR test has been performed at different temperatures. Deterministic models are developed by the multi-gene genetic programming technique for each modifier individually, and the non-recoverable creep compliance (Jnr) and percent recovery (R) parameters are predicted. The accuracy of deterministic models is suitable and the performance of R models has been better than Jnr models. Furthermore, a comprehensive probabilistic model has been developed by using the logistic regression technique to predict different traffic levels. The accuracy of the probabilistic model is 0.85. The sensitivity analysis has been performed on this model and the effect of changes in the modifier dosage and temperature on the traffic levels have been investigated. Results show that using the probabilistic model, it is possible to find a range of modifier’s dosage in which the traffic level is desired.

Probabilistic assessment of heavy-haul railway track using multi-gene genetic programming

This study presented a probabilistic assessment of heavy-haul railway track using a high-performance computational model called multi-gene genetic programming (MGGP). A reliability analysis (RA) method based on MGGP and the first-order second-moment method (FOSM) has been proposed in this study. First, GP was used to map the implicit performance functions; therefore, arriving at GP-based explicit performance functions. Subsequently, the developed GP model was used to perform RA of a soil slope of heavy-haul railway track under both seismic and non-seismic conditions. Using the FOSM, soil uncertainties were mapped based on the concepts of probability theory and statistics, and a ready-made expression was developed. Simulated results demonstrate that the GP-based FOSM approach can predict the probability of failure (POF) of slope with rational accuracy. The probabilistic analysis against bearing capacity failure was also investigated in this study to ensure serviceability of the soil slope. Based on the outcomes, it can be deduced that the coefficient of variation of soil properties affects the POF of slope significantly. With the aid of the developed expression, the POF of the soil slope of heavy-haul railway track can be assessed rationally and efficiently.