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Trend analysis of temperature data using innovative polygon trend analysis and modeling by gene expression programming

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ENVIRONMENTAL MONITORING AND ASSESSMENT 2022年第8期194卷 543-543页

作者： Yenice, Ali Can Yaqub, Muhammad Sakarya Univ Res Ctr Islamic Econ & Finance Sakarya Turkey Kumoh Natl Inst Technol Dept Environm Engn Gumi South Korea

Presenting temperature data using recently introduced innovative polygon trend analysis (IPTA) can improve our understanding of the effects of climate change. This method was applied to analyze temperature trends at six stations in Turkey: Istanbul (17,064), Ankara (17,131), Bursa (17,116), Iznik (17,661), Gemilik (17,663), and Sakarya (17,069). At station 17,064, there was an increasing trend in temperature data for seven months, while only one month showed a decreasing trend, and the remainder presented no trend. For station 17,131, there was a decreasing trend for two months, an increasing trend for five months, and no trend for the remaining months. At station 17,116, an increasing trend was present for nine months, with a decreasing trend for two months and only one month indicating no trend. An increasing trend over seven months was noted at station 17,661, while two and three months showed a decreasing and no trend, respectively. For station 17,663, there was an increasing trend for nine months, one month showed no trend, and two months presented a decreasing trend. At station 17,069, five, four, and three months showed increasing, decreasing, and no trends, respectively. The gene expression programming (GEP) model was tested to predict the short-term monthly average temperature for this dataset. The proposed GEP model presented good prediction results for all selected stations by tracing the relationship with a coefficient of determination (R-Sq) >= 0.90. Trend analysis by IPTA can help understand temperature trends better, aiding future decision-making, and the GEP model can effectively predict short-term values.

关键词： Climate change gene expression programming Innovative polygon trend analysis Temperature

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Mathematical prediction models for inactivation of antibiotic-resistant bacteria in kitchen wastewater by bimetallic bionanoparticles using machine learning with gene expression programming

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JOURNAL OF CLEANER PRODUCTION 2022年 333卷

作者： Noman, Efaq Ali Al-Gheethi, Adel Ali Saphira, Radin Mohamed Radin Maya Talip, Balkis A. Al-Sahari, Mohammed Ismail, Norli Univ Tun Hussein Onn Malaysia UTHM Fac Appl Sci & Technol Pagoh Higher Educ HubKM 1Jalan Panchor Johor Baharu 84000 Kagawa Malaysia Taiz Univ Fac Sci Appl Dept Appl Microbiol Taizi Yemen Univ Tun Hussein Onn Malaysia Fac Civil Engn & Built Environm Micropollutant Res Ctr MPRC Dept Civil Engn Batu Pahat 86400 Kagawa Malaysia Univ Sains Malaysia USM Sch Ind Technol Gelugor 11800 Penang Malaysia

The current study investigated the mathematical prediction model for the inactivation of antibiotic-resistant bacteria (B. subtilis, S. aureus, and E. coli) in kitchen wastewater by bimetallic bionanoparticles (Zn/Cu NPs) using gene expression programming (GEP). The interaction between Zn/Cu NP concentration, time, pH, initial cell concentration, and agitation speed was analysed based on the prediction tree. The best prediction model was obtained with GEP2 with root-mean-square error (RMSE) values of 0.2943 vs. 0.2988 for E. coli and 0.4059 vs. 0.4267 for S. aureus, and GEP4 for B. subtilis with RMSE value of 0.5123 vs. 0.4582 for the training and testing phase, respectively. The best log reductions were obtained with 15 mu g mL(-1) NPs for 60 min at pH 8 and 180 rpm;the predicted and actual log reductions of S. aureus, B. subtilis, and E. coli were 5.19 vs. 5.8, 5.73 vs. 5.67, and 6.00 vs. 5.99, respectively. These findings indicate that S. aureus exhibited more deviation in the predicted and actual results compared to B. subtilis and E. coli, which might be related to the behaviour of S. aureus towards the NPs based on their cell shape and their distribution in the real kitchen wastewater. The NP concentration and time are the main factors in the inactivation process. The NPs act on the bacterial cells externally by damaging the bacterial cell surface and transportation systems. Nps also act internally by altering the metabolic pathway in the bacterial cytoplasm. These findings confirm the effectiveness of GEP in predicting bacterial inactivation in kitchen wastewater.

关键词： gene expression programming Antibiotic-resistant bacteria Mathematical models Kitchen wastewater

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A gene expression programming Inspired Evolution Symbiont Agent for Real-Time Strategy generation 22

A Gene Expression Programming Inspired Evolution Symbiont Ag...

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Proceedings of the 2022 5th International Conference on Computational Intelligence and Intelligent Systems

作者： Siphesihle P. Sithungu Elizabeth M. Ehlers Academy of Computer Science and Software Engineering University of Johannesburg South Africa

ISBN: (纸本)9781450397612

AdaptiveSGA is a method for achieving Adaptive Game Artificial Intelligence-Based Dynamic Difficulty Balancing through the Symbiotic Game Agent Model. Previous work has shown that AdaptiveSGA can achieve Dynamic Difficulty Balancing in simulated soccer by effectively changing a team's strategy based on the opponent's performance. AdaptiveSGA pre-existing strategies and switches between them during runtime to increase the game's replayability by adapting the challenge it poses to the human player. Although this method works, its limitation is that if the human player surpasses the most intelligent strategy of the computer opponent, there is no way for the model to generate a new strategy during runtime that can potentially overcome the human player. AdaptiveSGA can only maintain engagement with the human player if the human player has not overcome the best strategy for the pool of pre-existing strategies. Current work addresses this limitation by introducing an Evolution Symbiont Agent whose purpose is to generate new strategies in real-time (during gameplay) through evolutionary mechanisms using gene expression programming. Experimental results show that the presence of the evolution symbiont agent can use gene expression programming to generate strategies capable of outperforming an opposing strategy.

关键词： gene expression programming dynamic difficulty balancing symbiotic game agent evolution symbiont agent

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Estimating the unconfined compression strength of low plastic clayey soils using gene-expression programming

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GEOMECHANICS AND ENGINEERING 2023年第1期33卷 1-9页

作者： Nawaz, Muhammad Naqeeb Chong, Song - Hun Nawaz, Muhammad Muneeb Haider, Safeer Hassan, Waqas Kim, Jin - Seop Sunchon Natl Univ Dept Civil Engn 255Jungang Ro Sunchon Si 57922 Jeonranam Do South Korea Natl Univ Sci & Technol NUST Inst Civil Engn Islamabad 44000 Pakistan Univ Padua Dept Civil Environm & Architectural Engn Padua Italy Korea Atom Energy Res Inst Radioact Waste Disposal Res Div Daejeon 34057 South Korea

The unconfined compression strength (UCS) of soils is commonly used either before or during the construction of geo-structures. In the pre-design stage, UCS as a mechanical property is obtained through a laboratory test that requires cumbersome procedures and high costs from in-situ sampling and sample preparation. As an alternative way, the empirical model established from limited testing cases is used to economically estimate the UCS. However, many parameters affecting the 1D soil compression response hinder employing the traditional statistical analysis. In this study, gene expression programming (GEP) is adopted to develop a prediction model of UCS with common affecting soil properties. A total of 79 undisturbed soil samples are collected, of which 54 samples are utilized for the generation of a predictive model and 25 samples are used to validate the proposed model. Experimental studies are conducted to measure the unconfined compression strength and basic soil index properties. A performance assessment of the prediction model is carried out using statistical checks including the correlation coefficient (R), the root mean square error (RMSE), the mean absolute error (MAE), the relatively squared error (RSE), and external criteria checks. The prediction model has achieved excellent accuracy with values of R, RMSE, MAE, and RSE of 0.98, 10.01, 7.94, and 0.03, respectively for the training data and 0.92, 19.82, 14.56, and 0.15, respectively for the testing data. From the sensitivity analysis and parametric study, the liquid limit and fine content are found to be the most sensitive parameters whereas the sand content is the least critical parameter.

关键词： gene expression programming external criteria parametric study sensitivity analysis statistical check unconfined compression strength

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Evaluation of gene expression programming and artificial neural networks in PyTorch for the prediction of local scour depth around a bridge pier

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RESULTS IN ENGINEERING 2022年 13卷

作者： Hassan, Waqed H. Hussein, H. H. Alshammari, Musa H. Jalal, Halah K. Rasheed, Sajjad E. Univ Kerbala Coll Engn Civil Engn Dept Kerbala Iraq Univ Warith Al Anbiyaa Coll Engn Civil Engn Dept Kerbala Iraq

Local scouring around the piers of bridges has been identified as one of the main problems contributing to bridge failure globally. As such, the accurate prediction of safe scouring depths is crucial to assure safety and to develop effective maintenance routines. This study was thus intended to develop a new empirical equation and models to expect the depth of scour occurring around the pier of a bridge by utilising and assessing a variety of modelling proposals to develop the best possible performance. Three methods were used for this purpose: artificial neural networks (ANN), gene expression programming (GEP), and statistical non-linear regression (NLR). The ANN model used in this study was coded in Python, a major modern coding language, being based on the PyTorch interface. The empirical equations and models derived to predict local scour depth were all amended to incorporate the shape of the pier, flow depth, flow intensity, pier width and the attack angle based on data computed within a numerical simulation model in Flow-3D software. The performance of the functional relationship derived from the GEP approach was then compared to the results computed from both the ANN model and NLR empirical equation, with three statistical indices: root mean squared error (RMSE), mean absolute error (MAE) and coefficient of determination (R-2), as well as appropriate scatter plots used to identify the most efficient of the three models. Based on this comparison, the ANN model performed more accurately than the other two empirical equations derived from the NLR and GEP models based on its smaller values for MAE (0.012) and RMSE (0.029), and its greater R-2 value (0.969). Sensitivity analysis results from the model also suggest that flow depth has the most significant impact on depth of scour predictions for compared to different input variables in this model. Based on the results, it can be concluded that the performance of the equation obtained from the ANN-based PyTorch tech

关键词： Artificial neural networks Dimensional analysis gene expression programming Non-linear regression (NLR) Python PyTorch Scour depth prediction

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Improved Shear Strength Prediction Model of Steel Fiber Reinforced Concrete Beams by Adopting gene expression programming

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MATERIALS 2022年第11期15卷 3758页

作者： Tariq, Moiz Khan, Azam Ullah, Asad Shayanfar, Javad Niaz, Momina Natl Univ Sci & Technol NUST Sch Civil & Environm Engn NUST Inst Civil Engn NICE Sect H-12 Islamabad 44000 Pakistan Univ Minho Dept Civil Engn Azurem Guimaraes Portugal Univ Engn & Technol Dept Civil Engn Peshawar 25130 Pakistan

In this study, an artificial intelligence tool called gene expression programming (GEP) has been successfully applied to develop an empirical model that can predict the shear strength of steel fiber reinforced concrete beams. The proposed genetic model incorporates all the influencing parameters such as the geometric properties of the beam, the concrete compressive strength, the shear span-to-depth ratio, and the mechanical and material properties of steel fiber. Existing empirical models ignore the tensile strength of steel fibers, which exercise a strong influence on the crack propagation of concrete matrix, thereby affecting the beam shear strength. To overcome this limitation, an improved and robust empirical model is proposed herein that incorporates the fiber tensile strength along with the other influencing factors. For this purpose, an extensive experimental database subjected to four-point loading is constructed comprising results of 488 tests drawn from the literature. The data are divided based on different shapes (hooked or straight fiber) and the tensile strength of steel fiber. The empirical model is developed using this experimental database and statistically compared with previously established empirical equations. This comparison indicates that the proposed model shows significant improvement in predicting the shear strength of steel fiber reinforced concrete beams, thus substantiating the important role of fiber tensile strength.

关键词： gene expression programming reinforced concrete steel fiber reinforced concrete shear strength

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Predicting the Lateral Load Carrying Capacity of Reinforced Concrete Rectangular Columns: gene expression programming

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MATERIALS 2022年第7期15卷 2673页

作者： Asghar, Raheel Javed, Muhammad Faisal Alrowais, Raid Khalil, Alamgir Mohamed, Abdeliazim Mustafa Mohamed, Abdullah Vatin, Nikolai Ivanovich COMSATS Univ Islamabad Dept Civil Engn Abbottabad Campus Abbottabad 22060 Pakistan Jouf Univ Dept Civil Engn Al Jouf 72388 Saudi Arabia Univ Engn & Technol Dept Civil Engn Peshawar 25120 Pakistan Prince Sattam Bin Abdulaziz Univ Coll Engn Dept Civil Engn Alkharj 16273 Saudi Arabia Bayan Coll Sci & Technol Bldg & Construct Technol Dept Khartoum 210 Sudan Future Univ Egypt Res Ctr New Cairo 11835 Egypt Peter Great St Petersburg Polytech Univ St Petersburg 195291 Russia

This research presents a novel approach of artificial intelligence (AI) based gene expression programming (GEP) for predicting the lateral load carrying capacity of RC rectangular columns when subjected to earthquake loading. To achieve the desired research objective, an experimental database assembled by the Pacific Earthquake Engineering Research (PEER) center consisting of 250 cyclic tested samples of RC rectangular columns was employed. Seven input variables of these column samples were utilized to develop the coveted analytical models against the established capacity outputs. The selection of these input variables was based on the linear regression and cosine amplitude method. Based on the GEP modelling results, two analytical models were proposed for computing the flexural and shear capacity of RC rectangular columns. The performance of both these models was evaluated based on the four key fitness indicators, i.e., coefficient of determination (R-2), root mean squared error (RMSE), mean absolute error (MAE), and root relative squared error (RRSE). From the performance evaluation results of these models, R-2, RMSE, MAE, and RRSE were found to be 0.96, 53.41, 38.12, and 0.20, respectively, for the flexural capacity model, and 0.95, 39.47, 28.77, and 0.22, respectively, for the shear capacity model. In addition to these fitness criteria, the performance of the proposed models was also assessed by making a comparison with the American design code of concrete structures ACI 318-19. The ACI model reported R-2, RMSE, MAE, and RRSE to be 0.88, 101.86, 51.74, and 0.39, respectively, for flexural capacity, and 0.87, 238.74, 183.66, and 1.35, respectively, for shear capacity outputs. The comparison depicted a better performance and higher accuracy of the proposed models as compared to that of ACI 318-19.

关键词： reinforced concrete columns lateral load carrying capacity bearing capacity flexural capacity shear capacity gene expression programming

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Prediction of Rapid Chloride Penetration Resistance to Assess the Influence of Affecting Variables on Metakaolin-Based Concrete Using gene expression programming

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MATERIALS 2022年第19期15卷 6959页

作者： Amin, Muhammad Nasir Raheel, Muhammad Iqbal, Mudassir Khan, Kaffayatullah Qadir, Muhammad Ghulam Jalal, Fazal E. Alabdullah, Anas Abdulalim Ajwad, Ali Al-Faiad, Majdi Adel Abu-Arab, Abdullah Mohammad King Faisal Univ Coll Engn Dept Civil & Environm Engn Al Hufuf 31982 Saudi Arabia Univ Engn & Technol Dept Civil Engn Mardan 23200 Pakistan Univ Engn & Technol Dept Civil Engn Peshawar 25120 Pakistan COMSATS Univ Islamabad Dept Environm Sci Abbottabad Campus Abbottabad 22060 Pakistan Shanghai Jiao Tong Univ Dept Civil Engn Shanghai 200240 Peoples R China Univ Management & Technol Civil Engn Dept Lahore 54770 Pakistan King Faisal Univ Coll Engn Dept Chem Engn Al Hasa 31982 Saudi Arabia

The useful life of a concrete structure is highly dependent upon its durability, which enables it to withstand the harsh environmental conditions. Resistance of a concrete specimen to rapid chloride ion penetration (RCP) is one of the tests to indirectly measure its durability. The central aim of this study was to investigate the influence of different variables, such as, age, amount of binder, fine aggregate, coarse aggregate, water to binder ratio, metakaolin content and the compressive strength of concrete on the RCP resistance using a genetic programming approach. The number of chromosomes (N-c), genes (N-g) and, the head size (H-s) of the gene expression programming (GEP) model were varied to study their influence on the predicted RCP values. The performance of all the GEP models was assessed using a variety of performance indices, i.e., R-2, RMSE and comparison of regression slopes. The optimal GEP model (Model T3) was obtained when the N-c = 100, H-s = 8 and N-g = 3. This model exhibits an R-2 of 0.89 and 0.92 in the training and testing phases, respectively. The regression slope analysis revealed that the predicted values are in good agreement with the experimental values, as evident from their higher R-2 values. Similarly, parametric analysis was also conducted for the best performing Model T3. The analysis showed that the amount of binder, compressive strength and age of the sample enhanced the RCP resistance of the concrete specimens. Among the different input variables, the RCP resistance sharply increased during initial stages of curing (28-d), thus validating the model results.

关键词： rapid chloride penetration resistance metakaolin gene expression programming compressive strength of concrete sensitivity parametric analysis

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Prediction model for load effective distribution width of slab in composite box girders using gene expression programming

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ENGINEERING STRUCTURES 2022年 255卷 113930-113930页

作者： Zhu, Ying-Jie Nie, Xin Ma, Hua Su, Li-Chao North China Univ Technol Sch Civil Engn Beijing 100144 Peoples R China Tsinghua Univ Dept Civil Engn Key Lab Civil Engn Safety & Durabil China Educ Minist Beijing 100084 Peoples R China Steel Concrete Composite Bridge Technol Innovat C Xingtai 054001 Hebei Peoples R China Xingtai Rd Bridge Construct Corp Xingtai 054001 Hebei Peoples R China

To determine the loading condition of the deck slab in girder bridges, the concept of the load effective distribution width is widely accepted in practical design. However, effective models for calculating the load effective distribution width of deck slab in steel-concrete composite box girders (SCCBG) are still lacking, which may bring about significant errors in slab design. In this study, a rational model to calculate the load effective distribution width of deck slabs in SCCBG in both the elastic and elastoplastic stages is proposed using the gene expression programming (GEP). First, four loading tests were conducted and elaborate finite element (FE) models were developed to investigate the load distribution of the slab in SCCBG. The FE models were validated by the test results and then applied to conduct a comprehensive parametric study to obtain the influences of the design parameters and the database for the GEP model. On this basis, formulas for the load effective distribution width in the elastic and elastoplastic stages were proposed by using the GEP algorithm. Furthermore, extra testing data were applied to verify the validity of the proposed models. By comparing the results given by several bridge codes, it is demonstrated that the proposed models can provide more accurate and reliable predictions and are quite simple and useful for pre-design applications.

关键词： Composite box girder deck slab Load effective distribution width gene expression programming Prediction model Elastoplastic stage

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Application of gene expression programming and sensitivity analyses in analyzing effective parameters in gastric cancer tumor size and location

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SOFT COMPUTING 2020年第13期24卷 9943-9964页

作者： Dorosti, Shadi Ghoushchi, Saeid Jafarzadeh Sobhrakhshankhah, Elham Ahmadi, Mohsen Sharifi, Abbas UUT Dept Ind Engn POB 57166-419 Orumiyeh Iran Urmia Univ Med Sci Imam Khomeini Hosp Dept Internal Med Orumiyeh Iran UUT Dept Mech Engn Orumiyeh Iran

Gastric cancer (GC) is the third reason for cancer-related deaths in the world. The late referral of patients to medical centers in an advanced stage can make the treatment procedure more difficult. Accurate diagnosis of risk factors in GC tumor size and tumor location can lead to taking preventive measures or determining a suitable treatment strategy. This study aims to present a general model to identify the correlation of different parameters in a GC tumor place and tumor size. The medical documents of GC patients consist of the dataset of this study. The effect of seven main parameters, namely age, smoking, Helicobacter pylori (H. pylori) infection, job, surgical background, sex, and nodal stage is investigated in GC tumor location and tumor size. By considering all the medical documents, data modeling is conducted using gene expression programming because of the high precision of model output. In the following, three different sensitivity analysis methods (Morris, Distributed Evaluation of Local Sensitivity Analysis (DELSA), and Sobol'-Jansen) are applied to determine the influential factors in the tumor size and location. Results show that in sequence, sex, age, and H. pylori records mostly affect tumor location;the nodal stage, smoking, and surgery record mostly affect tumor size. This method can help in identifying effective parameters and prevention of patients' death in all types of diseases, even for terminal illnesses.

关键词： Gastric cancer Sensitivity analysis gene expression programming Sobol'-Jansen Morris Distributed Evaluation of Local Sensitivity Analysis

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