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Evaluating the impact of data preprocessing to develop a robust MEP-based forecasting model for building integrated with PCM

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ENERGY 2025年 324卷

作者： Nazir, Kashif Memon, Shazim Ali Nazarbayev Univ Sch Engn & Digital Sci Dept Civil & Environm Engn Nur Sultan Kazakhstan

Data quality is a crucial aspect to accurately predict the energy use of buildings utilizing machine learning methods. Data preprocessing can ensure data quality when a database does not match the criteria for evolving a robust prediction model. Regarding phase change material (PCM)-incorporated buildings, there was no study before this research evaluating the impact of data preprocessing for establishing a robust machine learning based model to forecast their energy consumption (EC). Therefore, for the first time, this research presents an application of the data preprocessing process to compare the results of the formulated multi-expression programming (MEP)-based prediction model's accuracy for predicting the EC of PCM-integrated buildings using processed with actual databases. Data cleaning, outlier detection and removal, and data smoothing were performed on the actual EC database during the data preprocessing process. Results of model evaluation and validation processes for the articulated prediction models showed that the data preprocessing improved the MEP-based prediction model by 33 % to predict the EC precisely. Conclusively, model interpretability (sensitivity, parametric, and energy saving analysis) demonstrated that the developed more reliable prediction model provides energy savings of approximately 20 % by integrating optimum PCM.

关键词： Phase change material Data preprocessing multi-expression programming Energy consumption Model interpretability

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Toward sustainability: Integrating experimental study and data-driven modeling for eco-friendly paver blocks containing plastic waste

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REVIEWS ON ADVANCED MATERIALS SCIENCE 2024年第1期63卷

作者： Asif, Usama Javed, Muhammad Faisal Alsekait, Deema Mohammed Abdelminaam, Diaa Salama Alabduljabbar, Hisham Nazarbayev Univ Sch Engn & Digital Sci Dept Civil & Environm Engn Nur Sultan 010000 Kazakhstan Princess Nourah Bint Abdulrahman Univ Appl Coll Dept Comp Sci & Informat Technol POB 84428 Riyadh 11671 Saudi Arabia Ghulam Ishaq Khan Inst Engn Sci & Technol Dept Civil Engn Topi Pakistan Western Caspian Univ Baku Azerbaijan Middle East Univ MEU Res Unit Amman 11831 Jordan Jadara Univ Jadara Res Ctr Irbid 21110 Jordan Benha Univ Fac Comp & Artificial Intelligence Informat Syst Dept Banha 13511 Egypt Prince Sattam Bin Abdulaziz Univ Coll Engn Al Kharj Dept Civil Engn Al Kharj 11942 Saudi Arabia

Plastic waste (PW) poses a significant threat as a hazardous material, while the production of cement raises environmental concerns. It is imperative to urgently address and reduce both PW and cement usage in concrete products. Recently, several experimental studies have been performed to incorporate PW into paver blocks (PBs) as a substitute for cement. However, the experimental testing is not enough to optimize the use of waste plastic in pavers due to resource and time limitations. This study proposes an innovative approach, integrating experimental testing with machine learning to optimize PW ratios in PBs efficiently. Initially, experimental investigations are performed to examine the compressive strength (CS) of plastic sand paver blocks (PSPBs). Varied mix proportions of plastic and sand with different sizes of sand are employed. Moreover, to enhance the CS and meet the minimum requirements of ASTM C902-15 for light traffic, basalt fibers, a sustainable industrial material, are also utilized in the manufacturing process of environmentally friendly PSPB. The highest CS of 17.26 MPa is achieved by using the finest-size sand particles with a plastic-to-sand ratio of 30:70. Additionally, the inclusion of 0.5% basalt fiber, measuring 4 mm in length, yields further enhancement in outcome by significantly improving CS by 25.4% (21.65 MPa). Following that, an extensive experimental record is established, and multi-expression programming (MEP) is used to forecast the CS of PSPB. The model's projected results are confirmed by using various statistical procedures and external validation methods. Furthermore, comprehensive parametric and sensitivity studies are conducted to assess the effectiveness of the MEP-based proposed models. The sensitivity analysis demonstrates that the size of the sand particles and the fiber content are the primary factors contributing to more than 50% of the CS in PSPB. The parametric analysis confirmed the model's accuracy by demonstrating a

关键词： compressive strength plastic waste basalt fiber multi-expression programming paver blocks graphical user interface

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Compressive strength of waste-derived cementitious composites using machine learning

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REVIEWS ON ADVANCED MATERIALS SCIENCE 2024年第1期63卷

作者： Tian, Qiong Lu, Yijun Zhou, Ji Song, Shutong Yang, Liming Cheng, Tao Huang, Jiandong Hunan Univ Sci & Engn Coll Civil & Environm Engn Yongzhou 425199 Peoples R China Hubei Polytech Univ Sch Civil Engn Huangshi 435003 Peoples R China Guangzhou Univ Sch Civil Engn Guangzhou Peoples R China China Construct Railway Investment Rail Traff Cons Guangzhou Peoples R China

Marble cement (MC) is a new binding material for concrete, and the strength assessment of the resulting materials is the subject of this investigation. MC was tested in combination with rice husk ash (RHA) and fly ash (FA) to uncover its full potential. Machine learning (ML) algorithms can help with the formulation of better MC-based concrete. ML models that could predict the compressive strength (CS) of MC-based concrete that contained FA and RHA were built. Gene expression programming (GEP) and multi-expression programming (MEP) were used to build these models. Additionally, models were evaluated by calculating R 2 values, carrying out statistical tests, creating Taylor's diagram, and comparing theoretical and experimental readings. When comparing the MEP and GEP models, MEP yielded a slightly better-fitted model and better prediction performance (R 2 = 0.96, mean absolute error = 0.646, root mean square error = 0.900, and Nash-Sutcliffe efficiency = 0.960). According to the sensitivity analysis, the prediction of CS was most affected by curing age and MC content, then by FA and RHA contents. Incorporating waste materials such as marble powder, RHA, and FA into building materials can help reduce environmental impacts and encourage sustainable development.

关键词： marble cement gene expression programming multi-expression programming

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Innovative modeling techniques including MEP, ANN and FQ to forecast the compressive strength of geopolymer concrete modified with nanoparticles

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NEURAL COMPUTING & APPLICATIONS 2023年第17期35卷 12453-12479页

作者： Ahmed, Hemn Unis Mohammed, Ahmed S. S. Faraj, Rabar H. H. Abdalla, Aso A. A. Qaidi, Shaker M. A. Sor, Nadhim Hamah Mohammed, Azad A. A. Univ Sulaimani Coll Engn Civil Engn Dept Sulaimaniyah Kurdistan Regio Iraq Komar Univ Sci & Technol Civil Engn Dept Sulaimani Kurdistan Regio Iraq Univ Halabja Civil Engn Dept Halabja Kurdistan Regio Iraq Univ Duhok Coll Engn Civil Engn Dept Duhok Kurdistan Regio Iraq Univ Garmian Civil Engn Dept Kalar Kurdistan Regio Iraq Nawroz Univ Coll Engn Dept Civil Engn Duhok 42001 Iraq

The use of nano-materials to improve the engineering properties of different types of concrete composites including geopolymer concrete (GPC) has recently gained popularity. Numerous programs have been executed to investigate the mechanical properties of GPC. In general, compressive strength (CS) is an essential mechanical indicator for judging the quality of concrete. Traditional test methods for determining the CS of GPC are expensive, time-consuming and limiting due to the complicated interplay of a wide variety of mixing proportions and curing regimes. Therefore, in this study, artificial neural network (ANN), multi-expression programming, full quadratic, linear regression and M5P-tree machine learning techniques were used to predict the CS of GPC. In this instance, around 207 tested CS values were extracted from the literature and studied to promote the models. During the process of modeling, eleven effective variables were utilized as input model parameters, and one variable was utilized as an output. Four statistical indicators were used to judge how well the models worked, and the sensitivity analysis was carried out. According to the results, the ANN model calculated the CS of GPC with greater precision than the other models. On the other hand, the ratio of alkaline solution to the binder, molarity, NaOH content, curing temperature and concrete age have substantial effects on the CS of GPC.

关键词： Geopolymer concrete Modeling Compressive strength Artificial neural network multi-expression programming Sensitivity analysis

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Investigating the feasibility of genetic algorithms in predicting the properties of eco-friendly alkali-based concrete

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CONSTRUCTION AND BUILDING MATERIALS 2023年 409卷

作者： Jin, Conghe Qian, Yongjiu Khan, Suleman Ayub Ahmad, Waqas Althoey, Fadi Alotaibi, Badr Saad Abuhussain, Mohammed Awad Southwest Jiaotong Univ Sch Civil Engn Chengdu 610031 Peoples R China COMSATS Univ Islamabad Dept Civil Engn Abbottabad 22060 Pakistan Najran Univ Coll Engn Dept Civil Engn Najran Saudi Arabia Najran Univ Coll Engn Architectural Engn Dept Najran Saudi Arabia Najran Univ Sci & Engn Res Ctr Najran Saudi Arabia

This study employed an AI-based strategy to determine the accurate parameters in alkali-activated concrete (AAC) mix design that contribute to optimal performance. The data mining approach was used to generate a dataset comprising seven crucial input parameters and three outputs. Gene expression programming (GEP) models and multi-expression programming (MEP) models were developed to predict the rheological characteristics (in terms of static/dynamic yield stress and plastic viscosity) of AAC. R-2 values, statistical checks, Taylor's diagram, and the difference between experimental and anticipated rheological parameters were used to assess the suitability of the created models. All prediction models developed using the MEP approach were found to be highly accurate (R-2 > 0.90), while all GEP models were found to be in the acceptable range of accuracy (R-2 near 0.90). Moreover, the greater accuracy of MEP models over GEP models was also confirmed by error evaluation by statistical tests. The empirical equations presented by the models might be useful for comprehending the mix design of AAC and the effect of each input parameter. Precursor content was found to have a significantly positive impact on ACC's rheological properties, as determined by the sensitivity analysis.

关键词： Alkali-activated materials Rheological properties multi-expression programming Gene-expression programming Prediction models

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Energy consumption predictions by genetic programming methods for PCM integrated building in the tropical savanna climate zone

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JOURNAL OF BUILDING ENGINEERING 2023年 68卷

作者： Nazir, Kashif Memon, Shazim Ali Saurbayeva, Assemgul Ahmad, Abrar Nazarbayev Univ Sch Engn & Digital Sci Dept Civil & Environm Engn Nur Sultan Kazakhstan

The development of energy-efficient buildings by considering early-stage design parameters can help reduce buildings' energy consumption. Machine learning tools are getting popular for forecasting the energy demand of buildings, which play a vital role in improving building energy efficiency. In this research, multi-expression and genetic expression programming were utilized to anticipate the energy consumption of PCM-integrated buildings by taking early-stage design parameters into consideration. The prediction models were developed using the data generated by energy simulations for the PCM-integrated building in eight cities within a tropical savanna climate. The statical parameters were used to evaluate and externally validate the proposed prediction model. The statistical evaluation reveals that the genetic expression programmingbased predictive model gave more accurate energy consumption predictions for PCMintegrated buildings than multi-expression programming. The performance indices of the statistically analyzed gene expression programming-based prediction model (GEP7) showed excellent values: correlation coefficient (R) = 0.961, performance index (& rho;) = 0.169, and Nash-Sutcliffe efficiency (NSE) = 0.108. Thereafter, the sensitivity and parametric analyses were performed. It was unearthed that the roof solar absorptance, window visible transmittance, wall solar absorptance, and the melting temperature of PCM were the influential early-stage design parameters for PCM-integrated buildings. In conclusion, the gene-expression programming-based predictive model can be utilized to predict the influence of early-stage design parameters on the energy consumption of PCM-integrated buildings.

关键词： Phase change materials Early-stage design parameters Energy consumption Genetic programming multi-expression programming Gene-expression programming

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Prediction of the effective moment of inertia for concrete beams reinforced with FRP bars using an evolutionary algorithm

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STRUCTURES 2022年 35卷 684-705页

作者： Arabshahi, Alireza Tavakol, Masoumeh Sabzi, Javad Gharaei-Moghaddam, Nima Ferdowsi Univ Mashhad Sch Engn Civil Engn Dept Mashhad Razavi Khorasan Iran

The previous studies on the bending behavior of Fiber Reinforced Polymers (FRP) Reinforced Concrete (RC) beams proved that there is a distinct difference between the cracking and deflection behavior of these structural members and conventional RC beams with steel reinforcing rebar. Accordingly, the existing relations for the prediction of deflection and crack width of RC members cannot be used for serviceability controls of FRP-RC beams. The present study proposes new and more accurate predictive relations for the effective moment of inertia and the crack width of GFRP-RC beams using a collection of experimental test results from literature an Evolutionary Algorithm (EA) called multi-expression programming (MEP). An EA is a subset of generic population-based metaheuristic optimization, which performs mechanisms inspired by biological operations to find the best solutions for different types of approximating problems. Numerical evaluations proved that the proposed relation for the effective moment of inertia has higher accuracy than the other existing relations with an R-2 value of 0.49 and RMSE, MAE, and IAE error indices of 0.31, 0.72, and 0.51. The proposed relation for the crack width also outperforms the other existing counterparts. Since the proposed relations are developed based on the results of the four-point bending test on simple beams, the accuracy of the proposed relation for the effective moment of inertia was also compared with the experimental and numerical results for continuous beams, which again verified the high accuracy of the proposed model. In addition to the experimental database, a numerical model is also developed to verify the accuracy of the suggested models. The attained results demonstrate considerably higher accuracy of the proposed relations in comparison with the other existing counterparts.

关键词： GFRP RC beam Deflection Crack width Serviceability Evolutionary algorithm multi-expression programming Effective moment of inertia

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Prediction of collapsibility of loess site based on artificial intelligence: comparison of different algorithms

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ENVIRONMENTAL EARTH SCIENCES 2024年第3期83卷 101-101页

作者： Zhu, Xueliang Shao, Shuai Shao, Shengjun Xian Univ Technol Inst Geotech Engn Xian 710048 Shaanxi Peoples R China Xian Univ Technol Dept Architecture & Urban Planning Xian 710048 Shaanxi Peoples R China Xian Univ Technol Shaanxi Prov Key Lab Loess Mech & Engn Xian 710048 Shaanxi Peoples R China

Collapsibility affects loess engineering stability;the straightforward prediction for the self-weight collapsibility coefficient of loess is useful to determine the collapsibility type of loess site. In this study, three representative machine learning algorithms: multi-expression programming (MEP), random forest (RF) and support vector machine (SVM) are used to develop three straightforward prediction models for the loess self-weight collapsibility coefficient values, aiming to evaluate the collapsibility of loess sites according to the basic physical properties. Considering soil depth and compression modulus, a large database including five input variables, i.e., initial water content, initial void ratio, plasticity index, soil depth and compression modulus is established. Genetic algorithm (GA) is used to optimize the hyper-parameters of the RF and SVM models. The results show that the three models developed for the training set and the test set have high prediction accuracy for the self-weight collapsibility coefficient of loess. The monotonicity, sensitivity and robustness of the three prediction models are analyzed, showing the consistency between different models, but slightly different, which verifies the feasibility of the model. On the whole, the high prediction accuracy of the RF model is first recommended, but no explicit expression. The MEP model with explicit expression is also recommended for ease of application. The SVM model is the last option according to the situation.

关键词： Loess collapsibility parameters Machine learning multi-expression programming Random forest Support vector machine Genetic algorithm

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Intelligent prediction modeling for flexural capacity of FRP-strengthened reinforced concrete beams using machine learning algorithms

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HELIYON 2024年第1期10卷 e23375页

作者： Khan, Majid Khan, Adil Khan, Asad Ullah Shakeel, Muhammad Khan, Khalid Alabduljabbar, Hisham Najeh, Taoufik Gamil, Yaser COMSATS Univ Islamabad Abbottabad Campus Abbottabad 22060 Pakistan Univ Bradford Civil & Struct Engn Dept Bradford BD7 1DP West Yorkshire England Univ Engn & Technol Dept Civil Engn Peshawar 25120 Pakistan Prince Sattam Bin Abdulaziz Univ Coll Engn Al Kharj Dept Civil Engn Al Kharj 11942 Saudi Arabia Lulea Univ Technol Dept Civil Environm & Nat Resources Engn Operat Maintenance & Acoust Lulea Sweden Monash Univ Malaysia Sch Engn Dept Civil Engn Jalan Lagoon Selatan Bandar Sunway 47500 Selangor Malaysia

Fiber-reinforced polymers (FRP) are widely utilized to improve the efficiency and durability of concrete structures, either through external bonding or internal reinforcement. However, the response of FRP-strengthened reinforced concrete (RC) members, both in field applications and experimental settings, often deviates from the estimation based on existing code provisions. This discrepancy can be attributed to the limitations of code provisions in fully capturing the nature of FRP-strengthened RC members. Accordingly, machine learning methods, including gene expression programming (GEP) and multi-expression programming (MEP), were utilized in this study to predict the flexural capacity of the FRP-strengthened RC beam. To develop data-driven estimation models, an extensive collection of experimental data on FRP-strengthened RC beams was compiled from the experimental studies. For the assessment of the accuracy of developed models, various statistical indicators were utilized. The machine learning (ML) based models were compared with empirical and conventional linear regression models to substantiate their superiority, providing evidence of enhanced performance. The GEP model demonstrated outstanding predictive performance with a correlation coefficient (R) of 0.98 for both the training and validation phases, accompanied by minimal mean absolute errors (MAE) of 4.08 and 5.39, respectively. In contrast, the MEP model achieved a slightly lower accuracy, with an R of 0.96 in both the training and validation phases. Moreover, the ML-based models exhibited notably superior performances compared to the empirical models. Hence, the ML-based models presented in this study demonstrated promising prospects for practical implementation in engineering applications. Moreover, the SHapley Additive exPlanation (SHAP) method was used to interpret the feature's importance and influence on the flexural capacity. It was observed that beam width, section effective depth, and the tensile

关键词： Fiber-reinforced polymers Flexural capacity Machine learning Gene expression programming multi-expression programming

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Artificial intelligence-based optimized models for predicting the slump and compressive strength of sustainable alkali-derived concrete

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CONSTRUCTION AND BUILDING MATERIALS 2023年 409卷

作者： Zou, Baoping Wang, Yanbing Amin, Muhammad Nasir Iftikhar, Bawar Khan, Kaffayatullah Ali, Mujahid Althoey, Fadi Zhejiang Univ Sci & Technol Sch Civil Engn & Architecture Hangzhou 310023 Peoples R China China Univ Min & Technol Beijing Sch Mech & Architecture Engn Beijing 100083 Peoples R China King Faisal Univ Coll Engn Dept Civil & Environm Engn Al Hasa 31982 Saudi Arabia Univ Teknol Malaysia Sch Civil Engn Johor Baharu 81310 Johor Malaysia Silesian Tech Univ Fac Transport & Aviat Engn Dept Transport Syst Traff Engn & Logist Krasinskiego 8 St PL-40019 Katowice Poland Najran Univ Coll Engn Dept Civil Engn Najran Saudi Arabia

Alkali-activated materials (AAMs) are a potential class of construction materials that are well-known for their versatility and capacity for long-term sustainability. As a result of its ability to lessen the negative effects that the building industry has on the environment, AAMs have become increasingly popular in recent years. However, it can be difficult and time-consuming to figure out what proportions of alkali-activated concrete (AAC) would work best for a given project. Compressive strength (CS) and slump, both of which are important properties of AAC's viability in construction, were predicted using machine learning (ML) techniques, such as multi-expression programming (MEP) and gene expression programming (GEP) in this study. The mathematical formulations of AAC for both slump and CS for the AAC were effectively derived with the application of these ML approaches. According to the study's findings, MEP models performed better than GEP models in making accurate predictions, with MEP achieving R2 values of 0.92 and 0.93 for slump and CS in AAC, respectively, whereas GEP provided R2 values of 0.86 and 0.89. The hyper-parameters of the AI models were fine-tuned, and the models were verified with statistical measurements and Taylor diagrams. It's possible that using the findings from sensitivity analysis to estimate the relative importance of factors impacting the slump and CS of AAC might be helpful. The artificial intelligence-based models that were built showed a strong connection with the desired outcomes, suggesting that they might be used to estimate the slump and CS of AAC for different values of the input components.

关键词： Alkali-activated concrete Compressive strength Slump multi-expression programming Gene expression programming Sensitivity analysis Taylor diagram

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