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Forecasting Construction and Demolition Waste Using gene expression programming

JOURNAL OF COMPUTING IN CIVIL ENGINEERING 2015年第5期29卷 04014059-04014059页

作者： Wu, Zezhou Fan, Hongqin Liu, Guiwen Hong Kong Polytech Univ Dept Bldg & Real Estate Kowloon Hong Kong Peoples R China Chongqing Univ Sch Construct Management & Real Estate Chongqing 400045 Peoples R China

Accurate forecasting of construction and demolition waste (CDW) generation could provide valuable information for the planning, design, and management of CDW at municipal levels. However, the lack of reliable forecasting approaches and historical records makes it difficult to predict the amount of CDW for a long-or short-term plan. To effectively tackle the CDW forecasting problem, a novel computer-based prediction model, gene expression programming (GEP), is introduced and tested. With the CDW and other data on predictor variables from the last two decades, the amount of CDW is forecasted in this study. Results and findings obtained from this research show that GEP is an effective model for predicting waste generation, with lower average forecasting error than the multiple linear model and the artificial neural network. Research issues related to model selection, training, and validation are also discussed in the paper. (C) 2014 American Society of Civil Engineers.

关键词： Construction and demolition waste gene expression programming Time series analysis Forecasting

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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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Improved shear strength model for exterior reinforced concrete beam-column joints using gene expression programming

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ENGINEERING STRUCTURES 2021年 228卷 111563-111563页

作者： Mansouri, Iman Guneyisi, Esra Mete Mosalam, Khalid M. Birjand Univ Technol Dept Civil Engn Bijand 97175569 Iran Gaziantep Univ Dept Civil Engn TR-27310 Gaziantep Turkey Univ Calif Berkeley Dept Civil & Environm Engn 733 Davis Hall Berkeley CA 94720 USA Univ Calif Berkeley Pacific Earthquake Engn PEER Ctr Berkeley CA 94720 USA

Beam-column joints without transverse reinforcement can be considered as a weak link in reinforced concrete (RC) moment-resisting framed structural systems, particularly when subjected to seismic lateral loads. It is essential to fully understand the shear behavior of RC beam-column joints to avoid significant damage and eventual failure of RC structures in the case of joint panels excessively damaged. This study aims at developing a framework for systematic understanding of the shear behavior of RC beam-column joints where a novel joint shear strength model is presented. Totally, 56 test results on exterior RC beam-column joints without transverse reinforcement from various sources were used. The proposed model was generated by the utilization of the gene expression programming (GEP) approach. Thus, statistically, significant tests were applied for evaluating the different parameters influencing the shear strength of the joint, e.g., material features, design variables, and joint geometrical and detailing configurations. The proposed model was able to appropriately reflect the contribution of each variable of the beam-column joints involved in the GEP. The presented findings also include a comparison of the developed GEP-model with the existing analytical models, i.e., the conjectures of these available models were compared with the achieved results of this study. It is demonstrated that the presented GEP-model more accurately predicted the shear strength of RC beam-column joints than the existing models, having a much smaller coefficient of variation of 0.093 for the ratio between the predicted and experimentally determined shear strength values, compared to other models available in the literature.

关键词： Beam-column joints gene expression programming Reinforced concrete Seismic loading Shear strength

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A differential evolutionary chromosomal gene expression programming technique for electronic nose applications

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APPLIED SOFT COMPUTING 2023年 136卷

作者： Ari, Davut Alagoz, Baris Baykant Bitlis Eren Univ Dept Comp Engn Bitlis Turkiye Inonu Univ Dept Comp Engn Malatya Turkiye

The intelligent system applications require automated data-driven modeling tools. The performance consistency of modeling tools is very essential to reduce the need for human intervention. Classical gene expression programmings (GEPs) employ predefined genetic rules for the node-based evolution of expression trees in the absence of optimal numerical values of constant terminals, and these shortcomings can limit the search efficiency of expression trees. To alleviate negative impacts of these limitations on the data-driven GEP modeling performance, a Differential Evolutionary Chromosomal GEP (DEC-GEP) algorithm is suggested. The DEC-GEP utilizes the Differential Evolution (DE) algorithm for the optimization of a complete genotype of expression trees. For this purpose, a modifier gene container, which stores numerical values of constant terminals, is appended to the frame of GEP chromosome, and this modified chromosome structure enables simultaneous optimization of expression tree genotypes together with numerical values of constant terminals. Besides, the DEC-GEP algorithm can benefit from exploration and exploitation capabilities of the DE algorithm for more efficient evolution of GEP expression trees. To investigate consistency of the DEC-GEP algorithm in a data-driven modeling application, an experimental study was conducted for soft calibration of the low-cost, solid-state sensor array measurements, and results indicated that the DEC-GEP could yield dependable CO concentration estimation models for electronic nose applications.(c) 2023 Elsevier B.V. All rights reserved.

关键词： Air quality electronic nose gene expression programming Differential evolution Sensor calibration

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Improved Consistent Interpretation Approach of Fault Type within Power Transformers Using Dissolved Gas Analysis and gene expression programming

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ENERGIES 2019年第4期12卷 730-730页

作者： Abu-Siada, Ahmed Curtin Univ Dept Elect & Comp Engn Perth WA 6102 Australia

Dissolved gas analysis (DGA) of transformer oil is considered to be the utmost reliable condition monitoring technique currently used to detect incipient faults within power transformers. While the measurement accuracy has become relatively high since the development of various off-line and on-line measuring sensors, interpretation techniques of DGA results still depend on the level of personnel expertise more than analytical formulation. Therefore, various interpretation techniques may lead to different conclusions for the same oil sample. Moreover, ratio-based interpretation techniques may fail in interpreting DGA data in case of multiple fault conditions and when the oil sample comprises insignificant amount of the gases used in the specified ratios. This paper introduces an improved approach to overcome the limitations of conventional DGA interpretation techniques, automate and standardize the DGA interpretation process. The approach is built based on incorporating all conventional DGA interpretation techniques in one expert system to identify the fault type in a more consistent and reliable way. gene expression programming is employed to establish this expert system. Results show that the proposed approach provides more reliable results than using individual conventional methods that are currently adopted by industry practice worldwide.

关键词： transformer diagnosis condition monitoring dissolved gas analysis gene expression programming

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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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Development and validation of an artificial intelligence platform for characterization of the exergy-emission-stability profiles of the PPCI-RCCI regimes in a diesel-methanol operation under varying injection phasing strategies: A gene expression programming approach

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FUEL 2021年 299卷 120864-120864页

作者： Kakati, Dipankar Roy, Sumit Banerjee, Rahul NIT Agartala Dept Mech Engn Agartala 799046 India BML Munjal Univ Dept Mech Engn Gurgaon 122413 India

The present study leverages gene expression programming as the AI platform to characterize the performanceemission-stability profiles in the parametric interaction realms of PPCI-RCCI and its interspersed regimes of a diesel engine operating at a constant speed of 1500 rpm (+/- 2%) under varying injection phasing and methanolinduced reactivity strategies. Exergy efficiency, NOx, UHC, Opacity(%) and COVimep were chosen as the responses to be emulated with Overall reactivity, pilot-mass percentage, pilot-injection angle and main injection angle as the input parameters. The competency of GEP technique is examined through diverse correlation, absolute and relative error-based goodness-of-fit metrics. The quality of system information gained by the metamodels was evaluated through Theil Uncertainty(II)and Kullback-Leibler divergence metric, while generalization competency was evaluated across a test dataset, kept blind to the training phase. Analysis demonstrated significantly low footprints of error metrics along with negligible to low information loss across the entire response spectra emulated during both the training and testing phases. The GEP technique manifested commendable precision and reliability in adaptation to the actual experimental domain knowledge which endows it with a promising potential for deployment in model-in-loop based EMS frameworks for real-time decision-making in the complex control paradigms of such emerging engine combustion strategies.

关键词： Methanol-diesel dual fuel operation PPCI-RCCI System characterization gene expression programming Overall reactivity Exergy efficiency

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Predicting mechanical properties of sustainable green concrete using novel machine learning: Stacking and gene expression programming

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REVIEWS ON ADVANCED MATERIALS SCIENCE 2024年第1期63卷

作者： Ashraf, Muhammad Waqas Khan, Adnan Tu, Yongming Wang, Chao Ben Kahla, Nabil Javed, Muhammad Faisal Ullah, Safi Tariq, Jawad Southeast Univ Sch Civil Engn Key Lab Concrete & Prestressed Concrete Struct Minist EducNatl Engn Res Ctr Prestressing Technol Nanjing 211189 Peoples R China Southeast Univ Sch Transportat Jiulonghu Campus Nanjing 211189 Jiangsu Peoples R China Lulea Univ Technol Dept Civil Environm & Nat Resources Engn Div Struct & Fire Engn SE-97187 Lulea Sweden Bath Spa Univ Global Banking Sch Dept Construct Management Devonshire St North Manchester M12 6JH England King Khalid Univ Dept Civil Engn Coll Engn POB 394 Abha 61411 Saudi Arabia COMSATS Univ Islamabad Dept Civil Engn Abbottabad Campus Abbottabad Pakistan Western Caspian Univ Dept Tech Sci Baku Azerbaijan Henan Univ Technol Sch Civil Engn Zhengzhou 450001 Henan Peoples R China

Using rice husk ash (RHA) as a cement substitute in concrete production has potential benefits, including cement consumption and mitigating environmental effects. The feasibility of RHA on concrete strength was investigated in this research by predicting the split tensile strength (SPT) and flexural strength (FS) of RHA concrete (RHAC). The study used machine learning (ML) methods such as ensemble stacking and gene expression programming (GEP). The stacking model was improved using base learner configurations ML models, such as, random forest (RF), support vector regression, and gradient boosting regression. The proposed models were validated by statistical tests and external validation criteria. Moreover, the effect of input parameters was investigated using Shapley adaptive exPlanations (SHAP) for RF and parametric analysis for GEP-based models. The analysis revealed that the stacking ensemble integrates base learner predictions and demonstrated superior performance, with R values greater than 0.98 and 0.96. Mean absolute error and root mean square error values for both SPT and FS were 0.23, 0.3, 0.5, and 0.7 MPA, respectively. The SHAP analysis demonstrated water, cement, superplasticizer, and age as influential parameters for the RHAC strength. Furthermore, the SPT and FS of RHAC can be predicted with an acceptable error using the GEP expressions in the standard design procedure.

关键词： rice husk ash stacking gene expression programming tensile and flexural strength parametric analysis SHAP analysis

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GPR: A Python implementation of an extremely simple classifier based on fuzzy logic and gene expression programming

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SOFTWAREX 2023年 22卷

作者： Czmil, Anna Kluska, Jacek Czmil, Sylwester Rzeszow Univ Technol Fac Elect & Comp Engn Powstancow Warszawy 12 PL-35959 Rzeszow Poland

In this work, we present a Python-based implementation of an extremely simple classifier (GPR), which combines gene expression programming (GEP) features and the algebraic representation of the 'if-then' fuzzy rules theory of the Takagi-Sugeno fuzzy inference system. generated fuzzy metarules are highly interpretable and suitable for many applications. We provide an open-source Python implementation of the GPR algorithm to enable the use of the algorithm without any commercial software tools and open access to the research community. We also added enhancements to improve the readability and interpretability of the rules.(c) 2023 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY license (http://***/licenses/by/4.0/).

关键词： Fuzzy rule-based classifier gene expression programming Interpretability

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