Abstract
This paper presents a hybrid model combining the extreme gradient boosting machine (XGBoost) and the whale optimization algorithm (WOA) to predict the bearing capacity of concrete piles. The XGBoost provides the ultimate prediction from a set of explanatory experiment variables. The WOA, which is configured to search for an optimal set of XGBoost parameters, helps increase the model’s accuracy and robustness. The hybrid method is constructed by a dataset of 472 samples collected from static load tests in Vietnam. The results indicate that the hybrid model consistently outperforms the default XGBoost model and deep neural network (DNN) regression. In an experiment of 20 runs, the proposed model has gained roughly 12, 11.7, 9, and 12% reductions in root mean square error compared to the DNN with 2, 3, 4, and 5 hidden layers, respectively. The Wilcoxon signed-rank tests confirm that the proposed model is highly suitable for concrete pile capacity prediction.
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Data availability
The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request.
References
Alkroosh IS, Bahadori M, Nikraz H, Bahadori A (2015) Regressive approach for predicting bearing capacity of bored piles from cone penetration test data. J Rock Mech Geotech Eng 7:584–592. https://doi.org/10.1016/j.jrmge.2015.06.011
Aoki N, Velloso DDA (1975) An approximate method to estimate the bearing capacity of piles. International Society of Soil Mechanics and Geotechnical Engineering Buenos, pp 367–376
Baghbani A, Choudhury T, Costa S, Reiner J (2022) Application of artificial intelligence in geotechnical engineering: a state-of-the-art review. Earth Sci Rev 228:103991. https://doi.org/10.1016/j.earscirev.2022.103991
Bazaraa AR, Kurkur MM (1986) N-values used to predict settlements of piles in Egypt. ASCE, pp 462–474
Benali A, Nechnech A, Bouafia A (2013) Bored pile capacity by direct SPT methods applied to 40 case histories. Civ Environ Res 5:118–122
Bishop CM (2011) Pattern recognition and machine learning (Information Science and Statistics). Springer (ISBN-10: 0387310738)
Bouafia A, Derbala A (2002) Assessment of SPT-based method of pile bearing capacity–analysis of a database, pp 369–374
Briaud JL, Tucker LM (1988) Measured and predicted axial capacity of 98 piles. ASCE J Geotech Eng 114:984–1001
Bryngelson SH, Colonius T (2020) Simulation of humpback whale bubble-net feeding models. J Acoust Soc Am 147:1126–1135. https://doi.org/10.1121/10.0000746
Bui D-K, Nguyen T, Chou J-S, Nguyen-Xuan H, Ngo TD (2018) A modified firefly algorithm-artificial neural network expert system for predicting compressive and tensile strength of high-performance concrete. Constr Build Mater 180:320–333. https://doi.org/10.1016/j.conbuildmat.2018.05.201
Cao M-T, Nguyen N-M, Wang W-C (2022) Using an evolutionary heterogeneous ensemble of artificial neural network and multivariate adaptive regression splines to predict bearing capacity in axial piles. Eng Struct 268:114769. https://doi.org/10.1016/j.engstruct.2022.114769
Chen T, Guestrin C (2016) XGBoost: a scalable tree boosting system. In: Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, San Francisco, California, USA, pp 785–794. https://doi.org/10.1145/2939672.2939785
Chen W, Sarir P, Bui X-N, Nguyen H, Tahir MM, Jahed Armaghani D (2020) Neuro-genetic, neuro-imperialism and genetic programing models in predicting ultimate bearing capacity of pile. Eng Comput 36:1101–1115. https://doi.org/10.1007/s00366-019-00752-x
Cheng M-Y, Cao M-T, Tsai P-K (2020) Predicting load on ground anchor using a metaheuristic optimized least squares support vector regression model: a Taiwan case study. J Comput Des Eng 8:268–282. https://doi.org/10.1093/jcde/qwaa077
Cheng M-Y, Prayogo D, Wu Y-W (2018) Prediction of permanent deformation in asphalt pavements using a novel symbiotic organisms search–least squares support vector regression. Neural Comput Appl. https://doi.org/10.1007/s00521-018-3426-0
Chow YK, Chan WT, Liu LF, Lee SL (1995) Prediction of pile capacity from stress-wave measurements: a neural network approach. Int J Numer Anal Meth Geomech 19:107–126. https://doi.org/10.1002/nag.1610190204
Das SK, Basudhar PK (2006) Undrained lateral load capacity of piles in clay using artificial neural network. Comput Geotech 33:454–459. https://doi.org/10.1016/j.compgeo.2006.08.006
Decourt L (1995) Prediction of load settlement relationships for foundations on the basis of the SPT-T. In: Ciclo de conferencias Inter“Leonardo Zeevaert”, UNAM, Mexico, pp 85–104
Dong W, Huang Y, Lehane B, Ma G (2020) XGBoost algorithm-based prediction of concrete electrical resistivity for structural health monitoring. Autom Constr 114:103155. https://doi.org/10.1016/j.autcon.2020.103155
Duan J, Asteris PG, Nguyen H, Bui X-N, Moayedi H (2021) A novel artificial intelligence technique to predict compressive strength of recycled aggregate concrete using ICA-XGBoost model. Eng Comput 37:3329–3346. https://doi.org/10.1007/s00366-020-01003-0
Friedman JH (2001) Greedy function approximation: a gradient boosting machine. Ann Stat 29:1189–1232. https://doi.org/10.1214/aos/1013203451
Gharehchopogh FS, Gholizadeh H (2019) A comprehensive survey: whale optimization algorithm and its applications. Swarm Evol Comput 48:1–24. https://doi.org/10.1016/j.swevo.2019.03.004
Hoang N-D (2019) Estimating punching shear capacity of steel fibre reinforced concrete slabs using sequential piecewise multiple linear regression and artificial neural network. Measurement 137:58–70. https://doi.org/10.1016/j.measurement.2019.01.035
Hoang N-D, Tran X-L, Huynh T-C (2022) Prediction of pile bearing capacity using opposition-based differential flower pollination-optimized least squares support vector regression (ODFP-LSSVR). Adv Civ Eng 2022:7183700. https://doi.org/10.1155/2022/7183700
Hoang N-D, Tran X-L, Nguyen H (2020) Predicting ultimate bond strength of corroded reinforcement and surrounding concrete using a metaheuristic optimized least squares support vector regression model. Neural Comput Appl 32:7289–7309. https://doi.org/10.1007/s00521-019-04258-x
Jahed Armaghani D, Shoib RS, Faizi K, Rashid AS (2017) Developing a hybrid PSO–ANN model for estimating the ultimate bearing capacity of rock-socketed piles. Neural Comput Appl 28:391–405. https://doi.org/10.1007/s00521-015-2072-z
Kingma DP, Ba J (2015) Adam: a method for stochastic optimization. arXiv:14126980. https://doi.org/10.48550/arXiv.1412.6980
Kurtoglu AE, Gulsan ME, Abdi HA, Kamil MA, Cevik A (2017) Fiber reinforced concrete corbels: modeling shear strength via symbolic regression. Comput Concr 20:065–075
Le LT, Nguyen H, Zhou J, Dou J, Moayedi H (2019) Estimating the heating load of buildings for smart city planning using a novel artificial intelligence technique PSO-XGBoost. Appl Sci 9:2714. https://doi.org/10.3390/app9132714
Lee I-M, Lee J-H (1996) Prediction of pile bearing capacity using artificial neural networks. Comput Geotech 18:189–200. https://doi.org/10.1016/0266-352X(95)00027-8
Liu W, Liu WD, Gu J (2020) Predictive model for water absorption in sublayers using a joint distribution adaption based XGBoost transfer learning method. J Petrol Sci Eng 188:106937. https://doi.org/10.1016/j.petrol.2020.106937
Mafarja MM, Mirjalili S (2017) Hybrid whale optimization algorithm with simulated annealing for feature selection. Neurocomputing 260:302–312. https://doi.org/10.1016/j.neucom.2017.04.053
Mason L, Baxter J, Bartlett P, Frean M (1999) Boosting algorithms as gradient descent in function space, pp 512–518
Meyerhof GG (1976) Bearing capacity and settlement of pile foundations. J Geotech Eng Div 102:197–228. https://doi.org/10.1061/AJGEB6.0000243
Mirjalili S, Lewis A (2016) The whale optimization algorithm. Adv Eng Softw 95:51–67. https://doi.org/10.1016/j.advengsoft.2016.01.008
Mirrashid M, Naderpour H (2021) Recent trends in prediction of concrete elements behavior using soft computing (2010–2020). Arch Comput Methods Eng 28:3307–3327. https://doi.org/10.1007/s11831-020-09500-7
Moayedi H, Jahed Armaghani D (2018) Optimizing an ANN model with ICA for estimating bearing capacity of driven pile in cohesionless soil. Eng Comput 34:347–356. https://doi.org/10.1007/s00366-017-0545-7
Mohanty R, Suman S, Das SK (2018) Prediction of vertical pile capacity of driven pile in cohesionless soil using artificial intelligence techniques. Int J Geotech Eng 12:209–216. https://doi.org/10.1080/19386362.2016.1269043
Momeni E, Nazir R, Jahed Armaghani D, Maizir H (2014) Prediction of pile bearing capacity using a hybrid genetic algorithm-based ANN. Measurement 57:122–131. https://doi.org/10.1016/j.measurement.2014.08.007
Murlidhar BR, Sinha RK, Mohamad ET, Sonkar R, Khorami M (2020) The effects of particle swarm optimisation and genetic algorithm on ANN results in predicting pile bearing capacity. Int J Hydromechatronics 3:69–87. https://doi.org/10.1504/IJHM.2020.105484
Nguyen H (2020) PSO-XGBoost hybrid model to predict long-term deflection of reinforced concrete members. 10.5281/zenodo.3932822
Nguyen H, Nguyen N-M, Cao M-T, Hoang N-D, Tran X-L (2021) Prediction of long-term deflections of reinforced-concrete members using a novel swarm optimized extreme gradient boosting machine. Eng Comput. https://doi.org/10.1007/s00366-020-01260-z
Nguyen H, Vu T, Vo TP, Thai H-T (2021) Efficient machine learning models for prediction of concrete strengths. Constr Build Mater 266:120950. https://doi.org/10.1016/j.conbuildmat.2020.120950
Nguyen T-D, Tran T-H, Hoang N-D (2020) Prediction of interface yield stress and plastic viscosity of fresh concrete using a hybrid machine learning approach. Adv Eng Inform 44:101057. https://doi.org/10.1016/j.aei.2020.101057
Nguyen T-D, Tran T-H, Nguyen H, Nhat-Duc H (2021) A success history-based adaptive differential evolution optimized support vector regression for estimating plastic viscosity of fresh concrete. Eng Comput 37:1485–1498. https://doi.org/10.1007/s00366-019-00899-7
Nhu V-H et al (2020) Effectiveness assessment of Keras based deep learning with different robust optimization algorithms for shallow landslide susceptibility mapping at tropical area. CATENA 188:104458. https://doi.org/10.1016/j.catena.2020.104458
Park HI, Cho CW (2010) Neural network model for predicting the resistance of driven piles. Mar Georesour Geotechnol 28:324–344. https://doi.org/10.1080/1064119X.2010.514232
Pedregosa F et al (2011) Scikit-learn: machine learning in python. J Mach Learn Res 12:2825–2830
Pham TA, Ly H-B, Tran VQ, Giap LV, Vu H-LT, Duong H-AT (2020) Prediction of pile axial bearing capacity using artificial neural network and random forest. Appl Sci 10:1871
Pham TA, Tran VQ, Vu H-LT, Ly H-B (2020) Design deep neural network architecture using a genetic algorithm for estimation of pile bearing capacity. PLoS ONE 15:e0243030. https://doi.org/10.1371/journal.pone.0243030
Pirotta V, Owen K, Donnelly D, Brasier MJ, Harcourt R (2021) First evidence of bubble-net feeding and the formation of ‘super-groups’ by the east Australian population of humpback whales during their southward migration. Aquat Conserv Mar Freshw Ecosyst 31:2412–2419. https://doi.org/10.1002/aqc.3621
Prayogo D, Cheng M-Y, Wu Y-W, Tran D-H (2020) Combining machine learning models via adaptive ensemble weighting for prediction of shear capacity of reinforced-concrete deep beams. Eng Comput 36:1135–1153. https://doi.org/10.1007/s00366-019-00753-w
Rausche F, Goble GG, Likins GE (1985) Dynamic determination of pile capacity. J Geotech Eng 111:367–383. https://doi.org/10.1061/(ASCE)0733-9410(1985)111:3(367)
Rokach L, Maimon OZ (2007) Data mining with decision trees: theory and applications. World Scientific
Samui P (2011) Prediction of pile bearing capacity using support vector machine. Int J Geotech Eng 5:95–102. https://doi.org/10.3328/IJGE.2011.05.01.95-102
Shahin MA, Jaksa MB (2005) Neural network prediction of pullout capacity of marquee ground anchors. Comput Geotech 32:153–163. https://doi.org/10.1016/j.compgeo.2005.02.003
Shariatmadari N, Eslami A, Karimpour-Fard M (2008) Bearing capacity of driven piles in sands from SPT-applied to 60 case histories Iranian Journal of Science & Technology. Trans B Eng 32:125–140
Shioi Y, Fukui J (1982) Application of N-value to design of foundations in Japan. In: Proceedings of the second European symposium on penetration testing, Amsterdam, 24–27 May 1982. Routledge, pp 159–164
Shooshpasha I, Hasanzadeh A, Taghavi A (2013) Prediction of the axial bearing capacity of piles by SPT-based and numerical design methods. Int J GEOMATE 4:560–565
Tang J, Zheng L, Han C, Liu F, Cai J (2020) traffic incident clearance time prediction and influencing factor analysis using extreme gradient boosting model. J Adv Transp 2020:e6401082. https://doi.org/10.1155/2020/6401082
Teh CI, Wong KS, Goh ATC, Jaritngam S (1997) Prediction of pile capacity using neural networks. J Comput Civ Eng 11:129–138. https://doi.org/10.1061/(ASCE)0887-3801(1997)11:2(129)
Thieu NV (2021) A collection of the state-of-the-art meta-heuristics algorithms in PYTHON: mealpy. Zenodo. https://doi.org/10.5281/zenodo.3711948
Tien Bui D, Hoang N-D, Nguyen H, Tran X-L (2019) Spatial prediction of shallow landslide using Bat algorithm optimized machine learning approach: a case study in Lang Son Province. Vietnam Adv Eng Inform 42:100978. https://doi.org/10.1016/j.aei.2019.100978
XGBoost-Documentation (2021) XGBoost documentation—xgboost 1.5.0-dev documentation. https://xgboost.readthedocs.io/en/latest/index.html
XGBoost (2021) XGBoost: scalable and flexible gradient boosting. https://xgboost.ai/
Xia D, Zheng Y, Bai Y, Yan X, Hu Y, Li Y, Li H (2022) A parallel grid-search-based SVM optimization algorithm on Spark for passenger hotspot prediction. Multimed Tools Appl 81:27523–27549. https://doi.org/10.1007/s11042-022-12077-x
Zhang W, Wu C, Zhong H, Li Y, Wang L (2021) Prediction of undrained shear strength using extreme gradient boosting and random forest based on Bayesian optimization. Geosci Front 12:469–477. https://doi.org/10.1016/j.gsf.2020.03.007
Zhang X, Nguyen H, Bui X-N, Tran Q-H, Nguyen D-A, Bui DT, Moayedi H (2020) Novel soft computing model for predicting blast-induced ground vibration in open-pit mines based on particle swarm optimization and XGBoost. Nat Resour Res 29:711–721. https://doi.org/10.1007/s11053-019-09492-7
Acknowledgements
The work of Hieu Nguyen was supported by the Vietnam National Foundation for Science and Technology Development (NAFOSTED) under grant number 101.99-2019.326. Part of his work was carried out at Duy Tan University, his previous institution.
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Appendix
Appendix
Sample | Influencing factors | Pile bearing capacity | |||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|
X1 | X2 | X3 | X4 | X5 | X6 | X7 | X8 | X9 | X10 | Y | |
1 | 400.00 | 4.35 | 8.00 | 1.00 | 2.05 | 3.48 | 2.08 | 15.40 | 13.35 | 7.63 | 1395.00 |
2 | 300.00 | 3.40 | 5.25 | 0.00 | 3.40 | 3.47 | 3.42 | 12.05 | 8.65 | 6.75 | 559.80 |
3 | 300.00 | 3.40 | 5.30 | 0.00 | 3.40 | 3.52 | 3.42 | 12.10 | 8.70 | 6.76 | 508.90 |
4 | 400.00 | 4.25 | 8.00 | 0.90 | 2.15 | 3.56 | 2.26 | 15.30 | 13.15 | 7.61 | 1395.00 |
5 | 400.00 | 3.40 | 7.30 | 0.00 | 3.40 | 3.49 | 3.39 | 14.10 | 10.70 | 7.28 | 1068.80 |
6 | 300.00 | 3.40 | 5.30 | 0.00 | 3.40 | 3.50 | 3.40 | 12.10 | 8.70 | 6.76 | 661.60 |
7 | 400.00 | 4.35 | 8.00 | 1.06 | 2.05 | 3.55 | 2.09 | 15.46 | 13.41 | 7.66 | 1321.00 |
8 | 400.00 | 3.85 | 7.55 | 0.00 | 2.95 | 3.63 | 3.28 | 14.35 | 11.40 | 7.14 | 1440.00 |
9 | 400.00 | 4.65 | 7.35 | 0.00 | 2.15 | 3.55 | 3.40 | 14.15 | 12.00 | 6.79 | 1392.00 |
10 | 400.00 | 4.35 | 8.00 | 1.06 | 2.05 | 3.56 | 2.10 | 15.46 | 13.41 | 7.66 | 1321.00 |
11 | 400.00 | 3.85 | 7.30 | 0.00 | 2.95 | 3.70 | 3.60 | 14.10 | 11.15 | 7.08 | 1440.00 |
12 | 300.00 | 3.40 | 5.25 | 0.00 | 3.40 | 3.49 | 3.44 | 12.05 | 8.65 | 6.75 | 559.80 |
13 | 300.00 | 3.40 | 5.25 | 0.00 | 3.40 | 3.47 | 3.42 | 12.05 | 8.65 | 6.75 | 585.40 |
14 | 400.00 | 3.40 | 7.22 | 0.00 | 3.40 | 3.45 | 3.43 | 14.02 | 10.62 | 7.26 | 1240.00 |
15 | 300.00 | 3.40 | 5.27 | 0.00 | 3.40 | 3.51 | 3.44 | 12.07 | 8.67 | 6.75 | 661.60 |
16 | 400.00 | 4.10 | 2.08 | 0.00 | 2.70 | 3.63 | 2.75 | 8.88 | 6.18 | 4.86 | 432.00 |
17 | 400.00 | 3.45 | 8.00 | 0.30 | 2.95 | 3.65 | 2.95 | 14.70 | 11.75 | 7.59 | 1152.00 |
18 | 400.00 | 4.75 | 7.40 | 0.00 | 2.05 | 3.55 | 3.35 | 14.20 | 12.15 | 6.76 | 1440.00 |
19 | 400.00 | 4.10 | 1.71 | 0.00 | 2.70 | 3.26 | 2.75 | 8.51 | 5.81 | 4.56 | 423.90 |
20 | 400.00 | 4.65 | 7.50 | 0.00 | 2.15 | 3.59 | 3.29 | 14.30 | 12.15 | 6.82 | 1551.00 |
21 | 400.00 | 3.40 | 7.28 | 0.00 | 3.40 | 3.48 | 3.40 | 14.08 | 10.68 | 7.27 | 1318.00 |
22 | 300.00 | 3.40 | 5.22 | 0.00 | 3.40 | 3.45 | 3.43 | 12.02 | 8.62 | 6.74 | 559.80 |
23 | 300.00 | 3.40 | 5.20 | 0.00 | 3.40 | 3.40 | 3.40 | 12.00 | 8.60 | 6.73 | 559.00 |
24 | 400.00 | 3.45 | 8.00 | 0.19 | 2.95 | 3.56 | 2.97 | 14.59 | 11.64 | 7.52 | 1318.00 |
25 | 400.00 | 5.40 | 6.30 | 0.00 | 2.15 | 3.52 | 1.06 | 13.10 | 14.70 | 5.50 | 1056.00 |
26 | 400.00 | 4.45 | 8.00 | 1.04 | 1.95 | 3.44 | 2.00 | 15.44 | 13.49 | 7.61 | 1128.60 |
27 | 400.00 | 4.35 | 8.00 | 0.10 | 2.05 | 3.48 | 2.98 | 14.50 | 12.45 | 7.10 | 1392.00 |
28 | 400.00 | 4.25 | 8.00 | 0.40 | 2.15 | 3.59 | 2.79 | 14.80 | 12.65 | 7.32 | 1551.00 |
29 | 300.00 | 3.40 | 5.20 | 0.00 | 3.40 | 3.42 | 3.42 | 12.00 | 8.60 | 6.73 | 661.60 |
30 | 400.00 | 3.45 | 8.00 | 0.07 | 2.95 | 3.42 | 2.95 | 14.47 | 11.52 | 7.44 | 1240.00 |
31 | 300.00 | 3.40 | 5.25 | 0.00 | 3.40 | 3.46 | 3.41 | 12.05 | 8.65 | 6.75 | 610.70 |
32 | 300.00 | 3.40 | 5.20 | 0.00 | 3.40 | 3.40 | 3.40 | 12.00 | 8.60 | 6.73 | 559.80 |
33 | 400.00 | 4.25 | 8.00 | 0.91 | 2.15 | 3.56 | 2.25 | 15.31 | 13.16 | 7.62 | 1473.00 |
34 | 400.00 | 4.35 | 8.00 | 0.60 | 2.05 | 3.50 | 2.50 | 15.00 | 12.95 | 7.40 | 1297.80 |
35 | 400.00 | 3.85 | 7.20 | 0.00 | 2.95 | 3.57 | 3.57 | 14.00 | 11.05 | 7.06 | 1440.00 |
36 | 300.00 | 3.40 | 5.22 | 0.00 | 3.40 | 3.44 | 3.42 | 12.02 | 8.62 | 6.74 | 610.70 |
37 | 300.00 | 3.40 | 5.28 | 0.00 | 3.40 | 3.50 | 3.42 | 12.08 | 8.68 | 6.76 | 661.60 |
38 | 400.00 | 4.35 | 8.00 | 1.09 | 2.05 | 3.58 | 2.09 | 15.49 | 13.44 | 7.68 | 1224.80 |
39 | 400.00 | 4.35 | 8.00 | 0.95 | 2.05 | 3.44 | 2.09 | 15.35 | 13.30 | 7.60 | 1152.00 |
40 | 400.00 | 4.35 | 8.00 | 1.02 | 2.05 | 3.48 | 2.06 | 15.42 | 13.37 | 7.64 | 1248.00 |
41 | 400.00 | 3.40 | 7.30 | 0.00 | 3.40 | 3.54 | 3.44 | 14.10 | 10.70 | 7.28 | 967.00 |
42 | 300.00 | 3.40 | 5.30 | 0.00 | 3.40 | 3.52 | 3.42 | 12.10 | 8.70 | 6.76 | 610.70 |
43 | 400.00 | 3.40 | 7.30 | 0.00 | 3.40 | 3.51 | 3.41 | 14.10 | 10.70 | 7.28 | 1068.80 |
44 | 400.00 | 3.45 | 8.00 | 0.13 | 2.95 | 3.48 | 2.95 | 14.53 | 11.58 | 7.48 | 1240.00 |
45 | 300.00 | 3.40 | 5.30 | 0.00 | 3.40 | 3.51 | 3.41 | 12.10 | 8.70 | 6.76 | 661.60 |
46 | 400.00 | 3.45 | 8.00 | 0.25 | 2.95 | 3.64 | 2.99 | 14.65 | 11.70 | 7.55 | 1119.70 |
47 | 300.00 | 3.40 | 5.32 | 0.00 | 3.40 | 3.55 | 3.43 | 12.12 | 8.72 | 6.77 | 661.60 |
48 | 400.00 | 4.20 | 8.00 | 0.80 | 2.20 | 3.49 | 2.29 | 15.20 | 13.00 | 7.58 | 1395.00 |
49 | 400.00 | 4.35 | 8.00 | 0.96 | 2.05 | 3.43 | 2.07 | 15.36 | 13.31 | 7.61 | 1119.70 |
50 | 400.00 | 4.35 | 8.00 | 0.75 | 2.05 | 3.45 | 2.30 | 15.15 | 13.10 | 7.49 | 1323.20 |
51 | 300.00 | 3.40 | 5.30 | 0.00 | 3.40 | 3.50 | 3.40 | 12.10 | 8.70 | 6.76 | 559.80 |
52 | 400.00 | 4.35 | 8.00 | 1.05 | 2.05 | 3.50 | 2.05 | 15.45 | 13.40 | 7.66 | 1344.00 |
53 | 400.00 | 4.35 | 8.00 | 0.90 | 2.05 | 3.42 | 2.12 | 15.30 | 13.25 | 7.58 | 1395.00 |
54 | 400.00 | 4.75 | 7.25 | 0.00 | 2.05 | 3.62 | 3.57 | 14.05 | 12.00 | 6.73 | 1425.00 |
55 | 400.00 | 4.35 | 8.00 | 0.99 | 2.05 | 3.49 | 2.10 | 15.39 | 13.34 | 7.63 | 1224.80 |
56 | 400.00 | 3.40 | 7.30 | 0.00 | 3.40 | 3.50 | 3.40 | 14.10 | 10.70 | 7.28 | 1056.00 |
57 | 400.00 | 3.40 | 7.26 | 0.00 | 3.40 | 3.46 | 3.40 | 14.06 | 10.66 | 7.27 | 1152.00 |
58 | 300.00 | 3.40 | 5.26 | 0.00 | 3.40 | 3.47 | 3.41 | 12.06 | 8.66 | 6.75 | 559.80 |
59 | 300.00 | 3.40 | 5.20 | 0.00 | 3.40 | 3.43 | 3.43 | 12.00 | 8.60 | 6.73 | 661.60 |
60 | 400.00 | 4.35 | 8.00 | 0.95 | 2.05 | 3.41 | 2.06 | 15.35 | 13.30 | 7.60 | 1323.20 |
61 | 300.00 | 3.40 | 5.25 | 0.00 | 3.40 | 3.49 | 3.44 | 12.05 | 8.65 | 6.75 | 559.80 |
62 | 400.00 | 4.35 | 8.00 | 1.08 | 2.05 | 3.56 | 2.08 | 15.48 | 13.43 | 7.67 | 1344.00 |
63 | 300.00 | 3.40 | 5.22 | 0.00 | 3.40 | 3.44 | 3.42 | 12.02 | 8.62 | 6.74 | 661.60 |
64 | 400.00 | 4.35 | 8.00 | 1.08 | 2.05 | 3.53 | 2.05 | 15.48 | 13.43 | 7.67 | 1248.00 |
65 | 400.00 | 4.35 | 8.00 | 1.20 | 2.05 | 3.62 | 2.02 | 15.60 | 13.55 | 7.74 | 1119.70 |
66 | 400.00 | 4.45 | 8.00 | 1.10 | 1.95 | 3.50 | 2.00 | 15.50 | 13.55 | 7.65 | 1128.60 |
67 | 300.00 | 3.40 | 5.23 | 0.00 | 3.40 | 3.47 | 3.44 | 12.03 | 8.63 | 6.74 | 559.80 |
68 | 400.00 | 4.35 | 8.00 | 1.00 | 2.05 | 3.55 | 2.15 | 15.40 | 13.35 | 7.63 | 1344.00 |
69 | 400.00 | 4.25 | 8.00 | 1.02 | 2.15 | 3.58 | 2.16 | 15.42 | 13.27 | 7.68 | 1248.00 |
70 | 300.00 | 3.40 | 5.20 | 0.00 | 3.40 | 3.42 | 3.42 | 12.00 | 8.60 | 6.73 | 559.80 |
71 | 400.00 | 3.45 | 8.00 | 0.14 | 2.95 | 3.52 | 2.98 | 14.54 | 11.59 | 7.48 | 885.00 |
72 | 300.00 | 3.40 | 5.20 | 0.00 | 3.40 | 3.42 | 3.42 | 12.00 | 8.60 | 6.73 | 610.70 |
73 | 400.00 | 3.40 | 7.32 | 0.00 | 3.40 | 3.57 | 3.45 | 14.12 | 10.72 | 7.28 | 1032.40 |
74 | 300.00 | 3.40 | 5.20 | 0.00 | 3.40 | 3.43 | 3.43 | 12.00 | 8.60 | 6.73 | 585.35 |
75 | 400.00 | 4.35 | 8.00 | 1.22 | 2.05 | 3.67 | 2.05 | 15.62 | 13.57 | 7.75 | 1248.00 |
76 | 400.00 | 4.10 | 1.90 | 0.00 | 2.70 | 3.43 | 2.73 | 8.70 | 6.00 | 4.72 | 620.00 |
77 | 400.00 | 4.25 | 8.00 | 1.00 | 2.15 | 3.55 | 2.15 | 15.40 | 13.25 | 7.67 | 1248.00 |
78 | 300.00 | 3.40 | 5.23 | 0.00 | 3.40 | 3.45 | 3.42 | 12.03 | 8.63 | 6.74 | 559.80 |
79 | 300.00 | 3.40 | 5.25 | 0.00 | 3.40 | 3.48 | 3.43 | 12.05 | 8.65 | 6.75 | 610.70 |
80 | 300.00 | 3.40 | 5.20 | 0.00 | 3.40 | 3.40 | 3.40 | 12.00 | 8.60 | 6.73 | 610.70 |
81 | 400.00 | 4.10 | 2.00 | 0.00 | 2.70 | 3.54 | 2.74 | 8.80 | 6.10 | 4.80 | 712.50 |
82 | 400.00 | 3.85 | 7.20 | 0.00 | 2.95 | 3.56 | 3.56 | 14.00 | 11.05 | 7.06 | 1440.00 |
83 | 400.00 | 3.45 | 8.00 | 0.11 | 2.95 | 3.46 | 2.95 | 14.51 | 11.56 | 7.46 | 1240.00 |
84 | 400.00 | 4.35 | 8.00 | 0.95 | 2.05 | 3.48 | 2.13 | 15.35 | 13.30 | 7.60 | 1392.00 |
85 | 400.00 | 4.05 | 8.00 | 0.80 | 2.35 | 3.56 | 2.36 | 15.20 | 12.85 | 7.64 | 1318.00 |
86 | 400.00 | 4.75 | 7.20 | 0.00 | 2.05 | 3.68 | 3.68 | 14.00 | 11.95 | 6.72 | 1344.00 |
87 | 400.00 | 4.35 | 8.00 | 1.01 | 2.05 | 3.46 | 2.05 | 15.41 | 13.36 | 7.64 | 1473.00 |
88 | 300.00 | 3.40 | 5.30 | 0.00 | 3.40 | 3.52 | 3.42 | 12.10 | 8.70 | 6.76 | 508.90 |
89 | 300.00 | 3.40 | 5.20 | 0.00 | 3.40 | 3.41 | 3.41 | 12.00 | 8.60 | 6.73 | 610.70 |
90 | 400.00 | 4.35 | 8.00 | 1.08 | 2.05 | 3.58 | 2.10 | 15.48 | 13.43 | 7.67 | 1224.80 |
91 | 400.00 | 4.35 | 8.00 | 0.90 | 2.05 | 3.40 | 2.10 | 15.30 | 13.25 | 7.58 | 1395.00 |
92 | 400.00 | 4.35 | 8.00 | 0.97 | 2.05 | 3.47 | 2.10 | 15.37 | 13.32 | 7.61 | 1395.00 |
93 | 300.00 | 3.40 | 5.25 | 0.00 | 3.40 | 3.48 | 3.43 | 12.05 | 8.65 | 6.75 | 610.70 |
94 | 300.00 | 3.40 | 5.25 | 0.00 | 3.40 | 3.46 | 3.41 | 12.05 | 8.65 | 6.75 | 559.80 |
95 | 300.00 | 3.40 | 5.20 | 0.00 | 3.40 | 3.48 | 3.48 | 12.00 | 8.60 | 6.73 | 611.60 |
96 | 300.00 | 3.40 | 5.25 | 0.00 | 3.40 | 3.49 | 3.44 | 12.05 | 8.65 | 6.75 | 610.70 |
97 | 400.00 | 4.35 | 8.00 | 0.60 | 2.05 | 3.40 | 2.40 | 15.00 | 12.95 | 7.40 | 1473.00 |
98 | 400.00 | 4.35 | 8.00 | 1.02 | 2.05 | 3.47 | 2.05 | 15.42 | 13.37 | 7.64 | 1473.00 |
99 | 400.00 | 4.35 | 8.00 | 0.30 | 2.05 | 3.45 | 2.75 | 14.70 | 12.65 | 7.22 | 1473.00 |
100 | 300.00 | 3.40 | 5.20 | 0.00 | 3.40 | 3.42 | 3.42 | 12.00 | 8.60 | 6.73 | 610.70 |
101 | 400.00 | 4.35 | 8.00 | 0.90 | 2.05 | 3.46 | 2.16 | 15.30 | 13.25 | 7.58 | 1395.00 |
102 | 400.00 | 4.35 | 8.00 | 1.05 | 2.05 | 3.53 | 2.08 | 15.45 | 13.40 | 7.66 | 1473.00 |
103 | 400.00 | 3.45 | 8.00 | 0.15 | 2.95 | 3.50 | 2.95 | 14.55 | 11.60 | 7.49 | 1240.00 |
104 | 400.00 | 3.50 | 8.00 | 0.17 | 2.90 | 3.47 | 2.90 | 14.57 | 11.67 | 7.48 | 960.00 |
105 | 400.00 | 4.35 | 8.00 | 1.00 | 2.05 | 3.46 | 2.06 | 15.40 | 13.35 | 7.63 | 1344.00 |
106 | 400.00 | 4.75 | 7.45 | 0.00 | 2.05 | 3.67 | 3.42 | 14.25 | 12.20 | 6.77 | 1440.00 |
107 | 400.00 | 4.35 | 8.00 | 1.06 | 2.05 | 3.56 | 2.10 | 15.46 | 13.41 | 7.66 | 1224.80 |
108 | 400.00 | 3.45 | 8.00 | 0.20 | 2.95 | 3.52 | 2.92 | 14.60 | 11.65 | 7.52 | 967.00 |
109 | 400.00 | 4.35 | 8.00 | 0.94 | 2.05 | 3.49 | 2.15 | 15.34 | 13.29 | 7.60 | 1395.00 |
110 | 300.00 | 3.40 | 5.20 | 0.00 | 3.40 | 3.40 | 3.40 | 12.00 | 8.60 | 6.73 | 559.80 |
111 | 300.00 | 3.40 | 5.20 | 0.00 | 3.40 | 3.42 | 3.42 | 12.00 | 8.60 | 6.73 | 661.60 |
112 | 400.00 | 4.10 | 2.00 | 0.00 | 2.70 | 3.56 | 2.76 | 8.80 | 6.10 | 4.80 | 620.00 |
113 | 400.00 | 4.05 | 8.00 | 0.66 | 2.35 | 3.46 | 2.40 | 15.06 | 12.71 | 7.56 | 1318.00 |
114 | 400.00 | 3.40 | 7.30 | 0.00 | 3.40 | 3.50 | 3.40 | 14.10 | 10.70 | 7.28 | 960.00 |
115 | 300.00 | 3.40 | 5.20 | 0.00 | 3.40 | 3.42 | 3.42 | 12.00 | 8.60 | 6.73 | 660.60 |
116 | 400.00 | 4.10 | 1.95 | 0.00 | 2.70 | 3.49 | 2.74 | 8.75 | 6.05 | 4.76 | 712.50 |
117 | 300.00 | 3.40 | 5.25 | 0.00 | 3.40 | 3.48 | 3.43 | 12.05 | 8.65 | 6.75 | 508.90 |
118 | 400.00 | 4.10 | 2.20 | 0.00 | 2.70 | 3.72 | 2.72 | 9.00 | 6.30 | 4.94 | 610.70 |
119 | 300.00 | 3.40 | 5.20 | 0.00 | 3.40 | 3.45 | 3.45 | 12.00 | 8.60 | 6.73 | 610.70 |
120 | 400.00 | 3.45 | 8.00 | 0.09 | 2.95 | 3.44 | 2.95 | 14.49 | 11.54 | 7.45 | 1318.00 |
121 | 400.00 | 4.35 | 8.00 | 1.00 | 2.05 | 3.48 | 2.08 | 15.40 | 13.35 | 7.63 | 1395.00 |
122 | 300.00 | 3.40 | 5.27 | 0.00 | 3.40 | 3.50 | 3.43 | 12.07 | 8.67 | 6.75 | 610.70 |
123 | 400.00 | 4.10 | 2.00 | 0.00 | 2.70 | 3.52 | 2.72 | 8.80 | 6.10 | 4.80 | 610.70 |
124 | 300.00 | 3.40 | 5.25 | 0.00 | 3.40 | 3.51 | 3.46 | 12.05 | 8.65 | 6.75 | 559.80 |
125 | 400.00 | 3.45 | 8.00 | 0.20 | 2.95 | 3.58 | 2.98 | 14.60 | 11.65 | 7.52 | 1068.80 |
126 | 400.00 | 3.40 | 7.35 | 0.00 | 3.40 | 3.56 | 3.41 | 14.15 | 10.75 | 7.29 | 1052.40 |
127 | 300.00 | 3.40 | 5.22 | 0.00 | 3.40 | 3.44 | 3.42 | 12.02 | 8.62 | 6.74 | 559.80 |
128 | 400.00 | 4.05 | 8.00 | 0.70 | 2.35 | 3.47 | 2.37 | 15.10 | 12.75 | 7.58 | 1318.00 |
129 | 400.00 | 4.35 | 8.00 | 0.05 | 2.05 | 3.58 | 3.13 | 14.45 | 12.40 | 7.07 | 1344.00 |
130 | 300.00 | 3.40 | 5.26 | 0.00 | 3.40 | 3.51 | 3.45 | 12.06 | 8.66 | 6.75 | 610.70 |
131 | 400.00 | 4.05 | 8.00 | 0.70 | 2.35 | 3.48 | 2.38 | 15.10 | 12.75 | 7.58 | 1240.00 |
132 | 300.00 | 3.40 | 5.25 | 0.00 | 3.40 | 3.47 | 3.42 | 12.05 | 8.65 | 6.75 | 559.80 |
133 | 400.00 | 3.40 | 7.24 | 0.00 | 3.40 | 3.44 | 3.40 | 14.04 | 10.64 | 7.26 | 1395.00 |
134 | 300.00 | 3.40 | 5.25 | 0.00 | 3.40 | 3.48 | 3.43 | 12.05 | 8.65 | 6.75 | 508.90 |
135 | 300.00 | 3.40 | 5.30 | 0.00 | 3.40 | 3.50 | 3.40 | 12.10 | 8.70 | 6.76 | 610.70 |
136 | 400.00 | 4.35 | 8.00 | 1.05 | 2.05 | 3.54 | 2.09 | 15.45 | 13.40 | 7.66 | 1224.80 |
137 | 300.00 | 3.40 | 5.20 | 0.00 | 3.40 | 3.42 | 3.42 | 12.00 | 8.60 | 6.73 | 610.70 |
138 | 300.00 | 3.40 | 5.30 | 0.00 | 3.40 | 3.51 | 3.41 | 12.10 | 8.70 | 6.76 | 559.80 |
139 | 400.00 | 4.45 | 8.00 | 1.04 | 1.95 | 3.43 | 1.99 | 15.44 | 13.49 | 7.61 | 1128.60 |
140 | 300.00 | 3.40 | 5.20 | 0.00 | 3.40 | 3.38 | 3.38 | 12.00 | 8.60 | 6.73 | 610.70 |
141 | 300.00 | 3.40 | 5.25 | 0.00 | 3.40 | 3.48 | 3.43 | 12.05 | 8.65 | 6.75 | 559.80 |
142 | 400.00 | 4.35 | 8.00 | 1.01 | 2.05 | 3.46 | 2.05 | 15.41 | 13.36 | 7.64 | 1318.00 |
143 | 400.00 | 4.75 | 7.31 | 0.00 | 2.05 | 3.61 | 3.50 | 14.11 | 12.06 | 6.74 | 1440.00 |
144 | 300.00 | 3.40 | 5.22 | 0.00 | 3.40 | 3.45 | 3.43 | 12.02 | 8.62 | 6.74 | 559.80 |
145 | 400.00 | 4.35 | 8.00 | 1.00 | 2.05 | 3.45 | 2.05 | 15.40 | 13.35 | 7.63 | 1248.00 |
146 | 400.00 | 4.45 | 8.00 | 1.16 | 1.95 | 3.55 | 1.99 | 15.56 | 13.61 | 7.68 | 1224.80 |
147 | 300.00 | 3.40 | 5.22 | 0.00 | 3.40 | 3.44 | 3.42 | 12.02 | 8.62 | 6.74 | 610.70 |
148 | 400.00 | 4.35 | 8.00 | 0.98 | 2.05 | 3.48 | 2.10 | 15.38 | 13.33 | 7.62 | 1224.80 |
149 | 300.00 | 3.40 | 5.25 | 0.00 | 3.40 | 3.47 | 3.42 | 12.05 | 8.65 | 6.75 | 610.70 |
150 | 300.00 | 3.40 | 5.25 | 0.00 | 3.40 | 3.48 | 3.43 | 12.05 | 8.65 | 6.75 | 559.80 |
151 | 300.00 | 3.40 | 5.25 | 0.00 | 3.40 | 3.49 | 3.44 | 12.05 | 8.65 | 6.75 | 555.30 |
152 | 300.00 | 3.40 | 5.20 | 0.00 | 3.40 | 3.40 | 3.40 | 12.00 | 8.60 | 6.73 | 559.80 |
153 | 400.00 | 4.35 | 8.00 | 1.05 | 2.05 | 3.55 | 2.10 | 15.45 | 13.40 | 7.66 | 1224.80 |
154 | 300.00 | 3.40 | 5.24 | 0.00 | 3.40 | 3.45 | 3.41 | 12.04 | 8.64 | 6.75 | 661.60 |
155 | 400.00 | 4.35 | 8.00 | 0.30 | 2.05 | 3.50 | 2.80 | 14.70 | 12.65 | 7.22 | 1297.80 |
156 | 400.00 | 4.25 | 8.00 | 1.00 | 2.15 | 3.56 | 2.16 | 15.40 | 13.25 | 7.67 | 1344.00 |
157 | 300.00 | 3.40 | 5.20 | 0.00 | 3.40 | 3.41 | 3.41 | 12.00 | 8.60 | 6.73 | 610.70 |
158 | 400.00 | 4.10 | 1.66 | 0.00 | 2.70 | 3.21 | 2.75 | 8.46 | 5.76 | 4.52 | 423.90 |
159 | 400.00 | 4.75 | 7.25 | 0.00 | 2.05 | 3.65 | 3.60 | 14.05 | 12.00 | 6.73 | 1425.00 |
160 | 300.00 | 3.40 | 5.25 | 0.00 | 3.40 | 3.48 | 3.43 | 12.05 | 8.65 | 6.75 | 559.80 |
161 | 300.00 | 3.40 | 5.25 | 0.00 | 3.40 | 3.47 | 3.42 | 12.05 | 8.65 | 6.75 | 559.80 |
162 | 300.00 | 3.40 | 5.20 | 0.00 | 3.40 | 3.43 | 3.43 | 12.00 | 8.60 | 6.73 | 610.70 |
163 | 400.00 | 4.75 | 7.60 | 0.00 | 2.05 | 3.49 | 3.09 | 14.40 | 12.35 | 6.81 | 1473.00 |
164 | 300.00 | 3.40 | 5.20 | 0.00 | 3.40 | 3.42 | 3.42 | 12.00 | 8.60 | 6.73 | 610.70 |
165 | 400.00 | 3.45 | 8.00 | 0.13 | 2.95 | 3.51 | 2.98 | 14.53 | 11.58 | 7.48 | 1344.00 |
166 | 400.00 | 3.45 | 8.00 | 0.15 | 2.95 | 3.51 | 2.96 | 14.55 | 11.60 | 7.49 | 1017.90 |
167 | 400.00 | 4.35 | 8.00 | 1.00 | 2.05 | 3.50 | 2.10 | 15.40 | 13.35 | 7.63 | 1224.80 |
168 | 400.00 | 3.45 | 8.00 | 0.13 | 2.95 | 3.48 | 2.95 | 14.53 | 11.58 | 7.48 | 1318.00 |
169 | 400.00 | 4.35 | 8.00 | 1.02 | 2.05 | 3.47 | 4.05 | 15.42 | 13.37 | 7.64 | 1318.00 |
170 | 400.00 | 4.35 | 8.00 | 1.00 | 2.05 | 3.55 | 2.15 | 15.40 | 13.35 | 7.63 | 1344.00 |
171 | 400.00 | 3.50 | 8.00 | 0.20 | 2.90 | 3.50 | 2.90 | 14.60 | 11.70 | 7.50 | 1056.00 |
172 | 300.00 | 3.40 | 5.20 | 0.00 | 3.40 | 3.42 | 3.42 | 12.00 | 8.60 | 6.73 | 610.70 |
173 | 400.00 | 4.35 | 8.00 | 0.90 | 2.05 | 3.38 | 2.08 | 15.30 | 13.25 | 7.58 | 1395.00 |
174 | 400.00 | 4.45 | 8.00 | 1.10 | 1.95 | 3.46 | 1.96 | 15.50 | 13.55 | 7.65 | 1248.00 |
175 | 300.00 | 3.40 | 5.25 | 0.00 | 3.40 | 3.47 | 3.42 | 12.05 | 8.65 | 6.75 | 610.70 |
176 | 400.00 | 3.40 | 7.31 | 0.00 | 3.40 | 3.56 | 3.45 | 14.11 | 10.71 | 7.28 | 1224.80 |
177 | 300.00 | 3.40 | 5.26 | 0.00 | 3.40 | 3.47 | 3.41 | 12.06 | 8.66 | 6.75 | 559.80 |
178 | 400.00 | 4.35 | 8.00 | 0.98 | 2.05 | 3.53 | 2.15 | 15.38 | 13.33 | 7.62 | 1395.00 |
179 | 400.00 | 4.45 | 8.00 | 0.99 | 1.95 | 3.38 | 1.99 | 15.39 | 13.44 | 7.59 | 1128.60 |
180 | 400.00 | 3.45 | 8.00 | 0.30 | 2.95 | 3.65 | 2.95 | 14.70 | 11.75 | 7.59 | 1017.90 |
181 | 400.00 | 4.35 | 8.00 | 1.11 | 2.05 | 3.60 | 2.09 | 15.51 | 13.46 | 7.69 | 1128.60 |
182 | 400.00 | 5.72 | 8.00 | 1.69 | 0.68 | 4.12 | 1.03 | 16.09 | 15.41 | 7.50 | 1344.00 |
183 | 400.00 | 4.35 | 8.00 | 1.05 | 2.05 | 3.55 | 2.10 | 15.45 | 13.40 | 7.66 | 1473.00 |
184 | 400.00 | 4.35 | 8.00 | 0.95 | 2.05 | 3.45 | 2.10 | 15.35 | 13.30 | 7.60 | 1395.00 |
185 | 300.00 | 3.40 | 5.25 | 0.00 | 3.40 | 3.46 | 3.41 | 12.05 | 8.65 | 6.75 | 407.20 |
186 | 400.00 | 3.45 | 8.00 | 0.17 | 2.95 | 3.54 | 2.97 | 14.57 | 11.62 | 7.50 | 1056.00 |
187 | 300.00 | 3.40 | 5.20 | 0.00 | 3.40 | 3.43 | 3.43 | 12.00 | 8.60 | 6.73 | 661.60 |
188 | 400.00 | 3.45 | 8.00 | 0.27 | 2.95 | 3.63 | 2.96 | 14.67 | 11.72 | 7.57 | 1152.00 |
189 | 400.00 | 4.35 | 8.00 | 1.05 | 2.05 | 3.51 | 2.06 | 15.45 | 13.40 | 7.66 | 1248.00 |
190 | 300.00 | 3.40 | 5.25 | 0.00 | 3.40 | 3.49 | 3.44 | 12.05 | 8.65 | 6.75 | 610.70 |
191 | 400.00 | 4.35 | 8.00 | 1.06 | 2.05 | 3.56 | 2.10 | 15.46 | 13.41 | 7.66 | 1224.80 |
192 | 400.00 | 4.35 | 8.00 | 1.11 | 2.05 | 3.60 | 2.09 | 15.51 | 13.46 | 7.69 | 1128.60 |
193 | 300.00 | 3.40 | 5.22 | 0.00 | 3.40 | 3.44 | 3.42 | 12.02 | 8.62 | 6.74 | 610.70 |
194 | 300.00 | 3.40 | 5.20 | 0.00 | 3.40 | 3.38 | 3.38 | 12.00 | 8.60 | 6.73 | 610.70 |
195 | 300.00 | 3.40 | 5.22 | 0.00 | 3.40 | 3.44 | 3.42 | 12.02 | 8.62 | 6.74 | 610.70 |
196 | 400.00 | 4.75 | 7.60 | 0.00 | 2.05 | 3.65 | 3.25 | 14.40 | 12.35 | 6.81 | 1440.00 |
197 | 300.00 | 3.40 | 5.20 | 0.00 | 3.40 | 3.42 | 3.42 | 12.00 | 8.60 | 6.73 | 559.80 |
198 | 300.00 | 3.40 | 5.23 | 0.00 | 3.40 | 3.44 | 3.41 | 12.03 | 8.63 | 6.74 | 585.35 |
199 | 400.00 | 4.35 | 8.00 | 1.13 | 2.05 | 3.63 | 2.10 | 15.53 | 13.48 | 7.70 | 1224.80 |
200 | 300.00 | 3.40 | 5.20 | 0.00 | 3.40 | 3.40 | 3.40 | 12.00 | 8.60 | 6.73 | 559.80 |
201 | 300.00 | 3.40 | 5.25 | 0.00 | 3.40 | 3.47 | 3.42 | 12.05 | 8.65 | 6.75 | 610.70 |
202 | 400.00 | 4.75 | 7.60 | 0.00 | 2.05 | 3.46 | 3.06 | 14.40 | 12.35 | 6.81 | 1473.00 |
203 | 400.00 | 4.35 | 8.00 | 1.00 | 2.05 | 3.47 | 2.07 | 15.40 | 13.35 | 7.63 | 1344.00 |
204 | 400.00 | 3.45 | 8.00 | 0.16 | 2.95 | 3.53 | 2.97 | 14.56 | 11.61 | 7.50 | 1056.00 |
205 | 300.00 | 3.40 | 5.25 | 0.00 | 3.40 | 3.49 | 3.44 | 12.05 | 8.65 | 6.75 | 661.60 |
206 | 400.00 | 4.35 | 8.00 | 1.05 | 2.05 | 3.50 | 4.35 | 15.45 | 13.40 | 7.66 | 1297.80 |
207 | 400.00 | 3.45 | 8.00 | 0.22 | 2.95 | 3.57 | 2.95 | 14.62 | 11.67 | 7.53 | 1318.00 |
208 | 400.00 | 4.35 | 8.00 | 1.09 | 2.05 | 3.57 | 2.08 | 15.49 | 13.44 | 7.68 | 1395.00 |
209 | 400.00 | 4.75 | 7.50 | 0.00 | 2.05 | 3.45 | 3.15 | 14.30 | 12.25 | 6.79 | 1297.80 |
210 | 400.00 | 4.35 | 8.00 | 0.90 | 2.05 | 3.38 | 2.08 | 15.30 | 13.25 | 7.58 | 1395.00 |
211 | 300.00 | 3.40 | 5.23 | 0.00 | 3.40 | 3.44 | 3.41 | 12.03 | 8.63 | 6.74 | 610.70 |
212 | 300.00 | 3.40 | 5.35 | 0.00 | 3.40 | 3.57 | 3.42 | 12.15 | 8.75 | 6.78 | 661.60 |
213 | 300.00 | 3.40 | 5.25 | 0.00 | 3.40 | 3.46 | 3.41 | 12.05 | 8.65 | 6.75 | 559.80 |
214 | 300.00 | 3.40 | 5.25 | 0.00 | 3.40 | 3.46 | 3.41 | 12.05 | 8.65 | 6.75 | 610.70 |
215 | 300.00 | 3.40 | 5.35 | 0.00 | 3.40 | 3.57 | 3.42 | 12.15 | 8.75 | 6.78 | 661.60 |
216 | 400.00 | 4.10 | 2.00 | 0.00 | 2.70 | 3.55 | 2.75 | 8.80 | 6.10 | 4.80 | 610.70 |
217 | 300.00 | 3.40 | 5.24 | 0.00 | 3.40 | 3.49 | 3.45 | 12.04 | 8.64 | 6.75 | 610.70 |
218 | 300.00 | 3.40 | 5.24 | 0.00 | 3.40 | 3.46 | 3.42 | 12.04 | 8.64 | 6.75 | 610.70 |
219 | 400.00 | 4.35 | 8.00 | 1.07 | 2.05 | 3.56 | 2.09 | 15.47 | 13.42 | 7.67 | 1224.80 |
220 | 300.00 | 3.40 | 5.23 | 0.00 | 3.40 | 3.44 | 3.41 | 12.03 | 8.63 | 6.74 | 610.70 |
221 | 300.00 | 3.40 | 5.20 | 0.00 | 3.40 | 3.43 | 3.43 | 12.00 | 8.60 | 6.73 | 610.70 |
222 | 400.00 | 4.35 | 8.00 | 1.10 | 2.05 | 3.60 | 2.10 | 15.50 | 13.45 | 7.69 | 1224.80 |
223 | 400.00 | 4.05 | 8.00 | 0.70 | 2.35 | 3.47 | 2.37 | 15.10 | 12.75 | 7.58 | 1240.00 |
224 | 300.00 | 3.40 | 5.20 | 0.00 | 3.40 | 3.43 | 3.43 | 12.00 | 8.60 | 6.73 | 661.60 |
225 | 300.00 | 3.40 | 5.20 | 0.00 | 3.40 | 3.42 | 3.42 | 12.00 | 8.60 | 6.73 | 559.80 |
226 | 300.00 | 3.40 | 5.20 | 0.00 | 3.40 | 3.43 | 3.43 | 12.00 | 8.60 | 6.73 | 559.80 |
227 | 300.00 | 3.40 | 5.22 | 0.00 | 3.40 | 3.44 | 3.42 | 12.02 | 8.62 | 6.74 | 559.80 |
228 | 300.00 | 3.40 | 5.18 | 0.00 | 3.40 | 3.36 | 3.38 | 11.98 | 8.58 | 6.73 | 559.80 |
229 | 400.00 | 4.35 | 8.00 | 1.05 | 2.05 | 3.50 | 2.05 | 15.45 | 13.40 | 7.66 | 1128.60 |
230 | 300.00 | 3.40 | 5.30 | 0.00 | 3.40 | 3.52 | 3.42 | 12.10 | 8.70 | 6.76 | 508.90 |
231 | 400.00 | 3.40 | 7.27 | 0.00 | 3.40 | 3.48 | 3.41 | 14.07 | 10.67 | 7.27 | 1152.00 |
232 | 400.00 | 4.35 | 8.00 | 1.03 | 2.05 | 3.48 | 2.05 | 15.43 | 13.38 | 7.65 | 1318.00 |
233 | 400.00 | 4.35 | 8.00 | 1.01 | 2.05 | 3.46 | 2.05 | 15.41 | 13.36 | 7.64 | 1473.00 |
234 | 400.00 | 3.40 | 7.30 | 0.00 | 3.40 | 3.61 | 3.51 | 14.10 | 10.70 | 7.28 | 1115.20 |
235 | 400.00 | 4.35 | 8.00 | 1.18 | 2.05 | 3.66 | 2.08 | 15.58 | 13.53 | 7.73 | 1056.00 |
236 | 400.00 | 4.35 | 8.00 | 1.03 | 2.05 | 3.51 | 2.08 | 15.43 | 13.38 | 7.65 | 1224.80 |
237 | 400.00 | 3.45 | 8.00 | 0.22 | 2.95 | 3.57 | 2.95 | 14.62 | 11.67 | 7.53 | 1344.00 |
238 | 400.00 | 4.35 | 8.00 | 1.06 | 2.05 | 3.52 | 2.06 | 15.46 | 13.41 | 7.66 | 1344.00 |
239 | 300.00 | 3.40 | 5.20 | 0.00 | 3.40 | 3.43 | 3.43 | 12.00 | 8.60 | 6.73 | 600.70 |
240 | 400.00 | 4.10 | 1.92 | 0.00 | 2.70 | 3.44 | 2.72 | 8.72 | 6.02 | 4.73 | 712.50 |
241 | 400.00 | 3.40 | 7.35 | 0.00 | 3.40 | 3.55 | 3.40 | 14.15 | 10.75 | 7.29 | 1017.90 |
242 | 400.00 | 4.35 | 8.00 | 0.70 | 2.05 | 3.49 | 2.39 | 15.10 | 13.05 | 7.46 | 1392.00 |
243 | 400.00 | 3.85 | 7.24 | 0.00 | 2.95 | 3.62 | 3.58 | 14.04 | 11.09 | 7.07 | 1344.00 |
244 | 400.00 | 3.40 | 7.35 | 0.00 | 3.40 | 3.57 | 3.42 | 14.15 | 10.75 | 7.29 | 1068.80 |
245 | 300.00 | 3.40 | 5.25 | 0.00 | 3.40 | 3.47 | 3.42 | 12.05 | 8.65 | 6.75 | 610.70 |
246 | 400.00 | 4.10 | 2.10 | 0.00 | 2.70 | 3.63 | 2.73 | 8.90 | 6.20 | 4.87 | 480.00 |
247 | 400.00 | 4.25 | 8.00 | 0.20 | 2.15 | 3.56 | 2.96 | 14.60 | 12.45 | 7.20 | 1392.00 |
248 | 400.00 | 3.40 | 7.27 | 0.00 | 3.40 | 3.49 | 3.42 | 14.07 | 10.67 | 7.27 | 1248.00 |
249 | 300.00 | 3.40 | 5.22 | 0.00 | 3.40 | 3.46 | 3.44 | 12.02 | 8.62 | 6.74 | 661.60 |
250 | 300.00 | 3.40 | 5.24 | 0.00 | 3.40 | 3.48 | 3.44 | 12.04 | 8.64 | 6.75 | 610.70 |
251 | 400.00 | 3.40 | 7.40 | 0.00 | 3.40 | 3.61 | 3.41 | 14.20 | 10.80 | 7.30 | 1088.80 |
252 | 400.00 | 4.35 | 8.00 | 0.95 | 2.05 | 3.45 | 2.10 | 15.35 | 13.30 | 7.60 | 1221.50 |
253 | 400.00 | 3.45 | 8.00 | 0.25 | 2.95 | 3.62 | 2.97 | 14.65 | 11.70 | 7.55 | 1170.60 |
254 | 400.00 | 4.45 | 8.00 | 1.18 | 1.95 | 3.58 | 2.00 | 15.58 | 13.63 | 7.69 | 1032.40 |
255 | 300.00 | 3.40 | 5.20 | 0.00 | 3.40 | 3.42 | 3.42 | 12.00 | 8.60 | 6.73 | 617.00 |
256 | 400.00 | 3.40 | 7.28 | 0.00 | 3.40 | 3.53 | 3.45 | 14.08 | 10.68 | 7.27 | 1032.40 |
257 | 300.00 | 3.40 | 5.27 | 0.00 | 3.40 | 3.49 | 3.42 | 12.07 | 8.67 | 6.75 | 559.80 |
258 | 400.00 | 4.25 | 8.00 | 0.96 | 2.15 | 3.53 | 2.17 | 15.36 | 13.21 | 7.65 | 1344.00 |
259 | 300.00 | 3.40 | 5.25 | 0.00 | 3.40 | 3.49 | 3.44 | 12.05 | 8.65 | 6.75 | 610.70 |
260 | 400.00 | 4.35 | 8.00 | 1.10 | 2.05 | 3.55 | 2.05 | 15.50 | 13.45 | 7.69 | 1425.00 |
261 | 400.00 | 3.45 | 8.00 | 0.25 | 2.95 | 3.60 | 2.95 | 14.65 | 11.70 | 7.55 | 960.00 |
262 | 400.00 | 4.35 | 8.00 | 1.05 | 2.05 | 3.52 | 2.07 | 15.45 | 13.40 | 7.66 | 1248.00 |
263 | 400.00 | 4.35 | 8.00 | 1.05 | 2.05 | 3.55 | 2.10 | 15.45 | 13.40 | 7.66 | 1224.80 |
264 | 400.00 | 3.45 | 8.00 | 0.27 | 2.95 | 3.65 | 2.98 | 14.67 | 11.72 | 7.57 | 1248.00 |
265 | 400.00 | 4.35 | 8.00 | 1.04 | 2.05 | 3.52 | 2.08 | 15.44 | 13.39 | 7.65 | 1321.00 |
266 | 300.00 | 3.40 | 5.22 | 0.00 | 3.40 | 3.44 | 3.42 | 12.02 | 8.62 | 6.74 | 617.00 |
267 | 400.00 | 4.35 | 8.00 | 1.10 | 2.05 | 3.54 | 2.04 | 15.50 | 13.45 | 7.69 | 1323.20 |
268 | 300.00 | 3.40 | 5.27 | 0.00 | 3.40 | 3.51 | 3.44 | 12.07 | 8.67 | 6.75 | 661.60 |
269 | 400.00 | 4.35 | 8.00 | 0.10 | 2.05 | 3.40 | 2.90 | 14.50 | 12.45 | 7.10 | 1473.00 |
270 | 300.00 | 3.40 | 5.25 | 0.00 | 3.40 | 3.48 | 3.43 | 12.05 | 8.65 | 6.75 | 661.60 |
271 | 300.00 | 3.40 | 5.25 | 0.00 | 3.40 | 3.48 | 3.43 | 12.05 | 8.65 | 6.75 | 559.80 |
272 | 400.00 | 3.85 | 7.50 | 0.00 | 2.95 | 3.66 | 3.36 | 14.30 | 11.35 | 7.13 | 1425.00 |
273 | 400.00 | 3.85 | 7.30 | 0.00 | 2.95 | 3.70 | 3.60 | 14.10 | 11.15 | 7.08 | 1440.00 |
274 | 400.00 | 3.50 | 8.00 | 0.20 | 2.90 | 3.50 | 2.90 | 14.60 | 11.70 | 7.50 | 1056.00 |
275 | 300.00 | 3.40 | 5.20 | 0.00 | 3.40 | 3.43 | 3.43 | 12.00 | 8.60 | 6.73 | 610.70 |
276 | 400.00 | 4.10 | 2.00 | 0.00 | 2.70 | 3.52 | 2.72 | 8.80 | 6.10 | 4.80 | 610.70 |
277 | 400.00 | 3.40 | 7.22 | 0.00 | 3.40 | 3.47 | 3.45 | 14.02 | 10.62 | 7.26 | 1128.60 |
278 | 300.00 | 3.40 | 5.25 | 0.00 | 3.40 | 3.47 | 3.42 | 12.05 | 8.65 | 6.75 | 610.70 |
279 | 400.00 | 3.45 | 8.00 | 0.07 | 2.95 | 3.42 | 2.95 | 14.47 | 11.52 | 7.44 | 1240.00 |
280 | 400.00 | 4.10 | 2.05 | 0.00 | 2.70 | 3.58 | 2.73 | 8.85 | 6.15 | 4.83 | 661.60 |
281 | 400.00 | 4.45 | 7.21 | 0.00 | 2.35 | 3.41 | 2.40 | 14.01 | 11.66 | 6.83 | 1318.00 |
282 | 300.00 | 3.40 | 5.25 | 0.00 | 3.40 | 3.48 | 3.43 | 12.05 | 8.65 | 6.75 | 555.40 |
283 | 400.00 | 4.35 | 8.00 | 0.96 | 2.05 | 3.42 | 2.06 | 15.36 | 13.31 | 7.61 | 1244.00 |
284 | 300.00 | 3.40 | 5.25 | 0.00 | 3.40 | 3.45 | 3.40 | 12.05 | 8.65 | 6.75 | 610.70 |
285 | 300.00 | 3.40 | 5.27 | 0.00 | 3.40 | 3.50 | 3.43 | 12.07 | 8.67 | 6.75 | 610.70 |
286 | 300.00 | 3.40 | 5.20 | 0.00 | 3.40 | 3.43 | 3.43 | 12.00 | 8.60 | 6.73 | 559.80 |
287 | 400.00 | 4.35 | 8.00 | 0.90 | 2.05 | 3.40 | 2.10 | 15.30 | 13.25 | 7.58 | 1473.00 |
288 | 300.00 | 3.40 | 5.25 | 0.00 | 3.40 | 3.47 | 3.42 | 12.05 | 8.65 | 6.75 | 610.70 |
289 | 400.00 | 4.45 | 8.00 | 0.94 | 1.95 | 3.34 | 2.00 | 15.34 | 13.39 | 7.56 | 1128.60 |
290 | 400.00 | 3.50 | 8.00 | 0.20 | 2.90 | 3.51 | 2.91 | 14.60 | 11.70 | 7.50 | 960.00 |
291 | 300.00 | 3.40 | 5.20 | 0.00 | 3.40 | 3.45 | 3.45 | 12.00 | 8.60 | 6.73 | 610.70 |
292 | 300.00 | 3.40 | 5.20 | 0.00 | 3.40 | 3.43 | 3.43 | 12.00 | 8.60 | 6.73 | 508.90 |
293 | 400.00 | 4.35 | 8.00 | 0.18 | 2.05 | 3.38 | 2.80 | 14.58 | 12.53 | 7.15 | 1473.00 |
294 | 300.00 | 3.40 | 5.25 | 0.00 | 3.40 | 3.45 | 3.40 | 12.05 | 8.65 | 6.75 | 610.70 |
295 | 400.00 | 3.40 | 7.28 | 0.00 | 3.40 | 3.53 | 3.45 | 14.08 | 10.68 | 7.27 | 1032.40 |
296 | 300.00 | 3.40 | 5.21 | 0.00 | 3.40 | 3.44 | 3.43 | 12.01 | 8.61 | 6.74 | 508.50 |
297 | 400.00 | 4.35 | 8.00 | 0.96 | 2.05 | 3.42 | 2.06 | 15.36 | 13.31 | 7.61 | 1119.70 |
298 | 300.00 | 3.40 | 5.20 | 0.00 | 3.40 | 3.43 | 3.43 | 12.00 | 8.60 | 6.73 | 559.80 |
299 | 300.00 | 3.40 | 5.25 | 0.00 | 3.40 | 3.46 | 3.41 | 12.05 | 8.65 | 6.75 | 559.80 |
300 | 400.00 | 4.35 | 8.00 | 0.98 | 2.05 | 3.44 | 2.06 | 15.38 | 13.33 | 7.62 | 1344.00 |
301 | 300.00 | 3.40 | 5.30 | 0.00 | 3.40 | 3.52 | 3.42 | 12.10 | 8.70 | 6.76 | 559.80 |
302 | 400.00 | 4.35 | 8.00 | 1.03 | 2.05 | 3.48 | 2.05 | 15.43 | 13.38 | 7.65 | 1344.00 |
303 | 400.00 | 4.10 | 2.01 | 0.00 | 2.70 | 3.53 | 2.72 | 8.81 | 6.11 | 4.80 | 528.00 |
304 | 400.00 | 4.65 | 7.40 | 0.00 | 2.15 | 3.59 | 3.39 | 14.20 | 12.05 | 6.80 | 1551.00 |
305 | 400.00 | 4.35 | 8.00 | 1.05 | 2.05 | 3.55 | 2.10 | 15.45 | 13.40 | 7.66 | 1221.50 |
306 | 300.00 | 3.40 | 5.20 | 0.00 | 3.40 | 3.45 | 3.45 | 12.00 | 8.60 | 6.73 | 610.70 |
307 | 400.00 | 3.40 | 7.30 | 0.00 | 3.40 | 3.50 | 3.40 | 14.10 | 10.70 | 7.28 | 1152.00 |
308 | 400.00 | 4.35 | 8.00 | 0.60 | 2.05 | 3.46 | 2.46 | 15.00 | 12.95 | 7.40 | 1395.00 |
309 | 400.00 | 3.50 | 8.00 | 0.16 | 2.90 | 3.47 | 2.91 | 14.56 | 11.66 | 7.47 | 1056.00 |
310 | 400.00 | 4.35 | 8.00 | 1.03 | 2.05 | 3.53 | 2.10 | 15.43 | 13.38 | 7.65 | 1395.00 |
311 | 400.00 | 3.40 | 7.30 | 0.00 | 3.40 | 3.48 | 3.38 | 14.10 | 10.70 | 7.28 | 967.00 |
312 | 400.00 | 3.45 | 5.24 | 0.00 | 3.35 | 3.44 | 3.40 | 12.04 | 8.69 | 6.72 | 1240.00 |
313 | 400.00 | 4.35 | 8.00 | 1.03 | 2.05 | 3.48 | 2.05 | 15.43 | 13.38 | 7.65 | 1395.00 |
314 | 300.00 | 3.40 | 5.27 | 0.00 | 3.40 | 3.49 | 3.42 | 12.07 | 8.67 | 6.75 | 610.70 |
315 | 400.00 | 3.45 | 8.00 | 0.20 | 2.95 | 3.56 | 2.96 | 14.60 | 11.65 | 7.52 | 1152.00 |
316 | 400.00 | 4.35 | 8.00 | 1.07 | 2.05 | 3.52 | 2.05 | 15.47 | 13.42 | 7.67 | 1082.30 |
317 | 400.00 | 3.40 | 7.30 | 0.00 | 3.40 | 3.50 | 3.40 | 14.10 | 10.70 | 7.28 | 900.00 |
318 | 300.00 | 3.40 | 5.20 | 0.00 | 3.40 | 3.44 | 3.44 | 12.00 | 8.60 | 6.73 | 559.80 |
319 | 400.00 | 4.20 | 8.00 | 0.86 | 2.20 | 3.48 | 2.22 | 15.26 | 13.06 | 7.61 | 1344.00 |
320 | 400.00 | 4.10 | 1.84 | 0.00 | 2.70 | 3.39 | 2.75 | 8.64 | 5.94 | 4.67 | 423.90 |
321 | 400.00 | 4.35 | 8.00 | 0.98 | 2.05 | 3.45 | 2.07 | 15.38 | 13.33 | 7.62 | 1395.00 |
322 | 400.00 | 4.35 | 8.00 | 0.95 | 2.05 | 3.41 | 2.06 | 15.35 | 13.30 | 7.60 | 1110.60 |
323 | 300.00 | 3.40 | 5.29 | 0.00 | 3.40 | 3.54 | 3.45 | 12.09 | 8.69 | 6.76 | 661.60 |
324 | 400.00 | 4.25 | 8.00 | 0.10 | 2.15 | 3.54 | 3.04 | 14.50 | 12.35 | 7.14 | 1551.00 |
325 | 400.00 | 4.65 | 7.50 | 0.00 | 2.15 | 3.55 | 3.25 | 14.30 | 12.15 | 6.82 | 1551.00 |
326 | 300.00 | 3.40 | 5.23 | 0.00 | 3.40 | 3.45 | 3.42 | 12.03 | 8.63 | 6.74 | 559.80 |
327 | 400.00 | 3.40 | 7.26 | 0.00 | 3.40 | 3.48 | 3.42 | 14.06 | 10.66 | 7.27 | 1032.40 |
328 | 400.00 | 4.35 | 8.00 | 0.96 | 2.05 | 3.46 | 2.10 | 15.36 | 13.31 | 7.61 | 1395.00 |
329 | 400.00 | 3.40 | 7.33 | 0.00 | 3.40 | 3.55 | 3.42 | 14.13 | 10.73 | 7.28 | 1094.25 |
330 | 300.00 | 3.40 | 5.24 | 0.00 | 3.40 | 3.45 | 3.41 | 12.04 | 8.64 | 6.75 | 610.70 |
331 | 400.00 | 4.35 | 8.00 | 1.04 | 2.05 | 3.49 | 2.05 | 15.44 | 13.39 | 7.65 | 1344.00 |
332 | 400.00 | 4.35 | 8.00 | 1.04 | 2.05 | 3.49 | 2.05 | 15.44 | 13.39 | 7.65 | 1395.00 |
333 | 400.00 | 4.35 | 8.00 | 1.05 | 2.05 | 3.50 | 2.05 | 15.45 | 13.40 | 7.66 | 1152.00 |
334 | 400.00 | 3.45 | 8.00 | 0.20 | 2.95 | 3.50 | 2.90 | 14.60 | 11.65 | 7.52 | 1017.90 |
335 | 300.00 | 3.40 | 5.25 | 0.00 | 3.40 | 3.49 | 3.44 | 12.05 | 8.65 | 6.75 | 559.80 |
336 | 400.00 | 3.40 | 7.35 | 0.00 | 3.40 | 3.57 | 3.42 | 14.15 | 10.75 | 7.29 | 1119.70 |
337 | 400.00 | 4.75 | 7.40 | 0.00 | 2.05 | 3.52 | 3.32 | 14.20 | 12.15 | 6.76 | 1425.00 |
338 | 400.00 | 4.35 | 8.00 | 1.02 | 2.05 | 3.47 | 2.05 | 15.42 | 13.37 | 7.64 | 1395.00 |
339 | 400.00 | 4.10 | 1.96 | 0.00 | 2.70 | 3.49 | 2.73 | 8.76 | 6.06 | 4.76 | 508.90 |
340 | 300.00 | 3.40 | 5.30 | 0.00 | 3.40 | 3.50 | 3.40 | 12.10 | 8.70 | 6.76 | 611.70 |
341 | 400.00 | 3.45 | 8.00 | 0.14 | 2.95 | 3.52 | 2.98 | 14.54 | 11.59 | 7.48 | 885.00 |
342 | 300.00 | 3.40 | 5.25 | 0.00 | 3.40 | 3.48 | 3.43 | 12.05 | 8.65 | 6.75 | 559.80 |
343 | 400.00 | 4.35 | 8.00 | 1.05 | 2.05 | 3.50 | 2.05 | 15.45 | 13.40 | 7.66 | 1152.00 |
344 | 300.00 | 3.40 | 5.22 | 0.00 | 3.40 | 3.44 | 3.42 | 12.02 | 8.62 | 6.74 | 661.60 |
345 | 300.00 | 3.40 | 5.24 | 0.00 | 3.40 | 3.46 | 3.42 | 12.04 | 8.64 | 6.75 | 559.80 |
346 | 400.00 | 3.85 | 7.50 | 0.00 | 2.95 | 3.68 | 3.38 | 14.30 | 11.35 | 7.13 | 1425.00 |
347 | 400.00 | 4.45 | 8.00 | 1.01 | 1.95 | 3.40 | 1.99 | 15.41 | 13.46 | 7.60 | 1128.60 |
348 | 400.00 | 3.85 | 7.36 | 0.00 | 2.95 | 3.66 | 3.50 | 14.16 | 11.21 | 7.10 | 1344.00 |
349 | 400.00 | 3.45 | 8.00 | 0.15 | 2.95 | 3.64 | 3.09 | 14.55 | 11.60 | 7.49 | 1344.00 |
350 | 300.00 | 3.40 | 5.25 | 0.00 | 3.40 | 3.48 | 3.43 | 12.05 | 8.65 | 6.75 | 610.70 |
351 | 300.00 | 3.40 | 5.30 | 0.00 | 3.40 | 3.52 | 3.42 | 12.10 | 8.70 | 6.76 | 661.60 |
352 | 400.00 | 4.25 | 8.00 | 0.20 | 2.15 | 3.55 | 2.95 | 14.60 | 12.45 | 7.20 | 1392.00 |
353 | 300.00 | 3.40 | 5.30 | 0.00 | 3.40 | 3.50 | 3.40 | 12.10 | 8.70 | 6.76 | 559.80 |
354 | 400.00 | 3.40 | 7.31 | 0.00 | 3.40 | 3.56 | 3.45 | 14.11 | 10.71 | 7.28 | 1032.40 |
355 | 400.00 | 4.10 | 1.72 | 0.00 | 2.70 | 3.27 | 2.75 | 8.52 | 5.82 | 4.57 | 423.90 |
356 | 300.00 | 3.40 | 5.22 | 0.00 | 3.40 | 3.46 | 3.44 | 12.02 | 8.62 | 6.74 | 661.60 |
357 | 400.00 | 3.45 | 8.00 | 0.10 | 2.95 | 3.60 | 3.10 | 14.50 | 11.55 | 7.46 | 1344.00 |
358 | 400.00 | 4.35 | 8.00 | 0.87 | 2.05 | 3.37 | 2.10 | 15.27 | 13.22 | 7.56 | 1473.00 |
359 | 400.00 | 4.35 | 8.00 | 1.11 | 2.05 | 3.56 | 2.05 | 15.51 | 13.46 | 7.69 | 1128.60 |
360 | 400.00 | 4.35 | 8.00 | 0.80 | 2.05 | 3.45 | 2.25 | 15.20 | 13.15 | 7.52 | 1392.00 |
361 | 400.00 | 4.35 | 8.00 | 1.08 | 2.05 | 3.56 | 2.08 | 15.48 | 13.43 | 7.67 | 1224.80 |
362 | 400.00 | 3.45 | 8.00 | 0.20 | 2.95 | 3.53 | 2.93 | 14.60 | 11.65 | 7.52 | 1068.80 |
363 | 400.00 | 4.35 | 8.00 | 1.02 | 2.05 | 3.48 | 2.06 | 15.42 | 13.37 | 7.64 | 1248.00 |
364 | 400.00 | 4.45 | 8.00 | 1.04 | 1.95 | 3.43 | 1.99 | 15.44 | 13.49 | 7.61 | 1224.80 |
365 | 400.00 | 3.45 | 8.00 | 0.10 | 2.95 | 3.54 | 3.04 | 14.50 | 11.55 | 7.46 | 1017.90 |
366 | 400.00 | 3.85 | 7.30 | 0.00 | 2.95 | 3.68 | 3.58 | 14.10 | 11.15 | 7.08 | 1440.00 |
367 | 400.00 | 4.35 | 8.00 | 1.08 | 2.05 | 3.53 | 2.05 | 15.48 | 13.43 | 7.67 | 1248.00 |
368 | 400.00 | 4.35 | 8.00 | 1.01 | 2.05 | 3.46 | 2.05 | 15.41 | 13.36 | 7.64 | 1550.00 |
369 | 400.00 | 4.45 | 8.00 | 1.03 | 1.95 | 3.43 | 2.00 | 15.43 | 13.48 | 7.61 | 1128.60 |
370 | 400.00 | 3.40 | 7.23 | 0.00 | 3.40 | 3.43 | 3.40 | 14.03 | 10.63 | 7.26 | 960.00 |
371 | 400.00 | 4.75 | 7.50 | 0.00 | 2.05 | 3.60 | 3.30 | 14.30 | 12.25 | 6.79 | 1425.00 |
372 | 400.00 | 3.40 | 7.24 | 0.00 | 3.40 | 3.44 | 3.40 | 14.04 | 10.64 | 7.26 | 1240.00 |
373 | 400.00 | 3.40 | 7.40 | 0.00 | 3.40 | 3.62 | 3.42 | 14.20 | 10.80 | 7.30 | 1017.90 |
374 | 400.00 | 4.25 | 8.00 | 1.02 | 2.15 | 3.58 | 2.16 | 15.42 | 13.27 | 7.68 | 1248.00 |
375 | 300.00 | 3.40 | 5.22 | 0.00 | 3.40 | 3.45 | 3.43 | 12.02 | 8.62 | 6.74 | 559.80 |
376 | 400.00 | 3.45 | 8.00 | 0.18 | 2.95 | 3.55 | 2.97 | 14.58 | 11.63 | 7.51 | 1344.00 |
377 | 400.00 | 4.75 | 7.60 | 0.00 | 2.05 | 3.44 | 3.04 | 14.40 | 12.35 | 6.81 | 1473.00 |
378 | 400.00 | 4.35 | 8.00 | 1.03 | 2.05 | 3.53 | 2.10 | 15.43 | 13.38 | 7.65 | 1224.80 |
379 | 400.00 | 3.45 | 8.00 | 0.22 | 2.95 | 3.59 | 2.97 | 14.62 | 11.67 | 7.53 | 1152.00 |
380 | 400.00 | 3.45 | 8.00 | 0.30 | 2.95 | 3.66 | 2.96 | 14.70 | 11.75 | 7.59 | 960.00 |
381 | 300.00 | 3.40 | 5.25 | 0.00 | 3.40 | 3.47 | 3.42 | 12.05 | 8.65 | 6.75 | 559.80 |
382 | 300.00 | 3.40 | 5.30 | 0.00 | 3.40 | 3.50 | 3.40 | 12.10 | 8.70 | 6.76 | 559.80 |
383 | 300.00 | 3.40 | 5.25 | 0.00 | 3.40 | 3.46 | 3.41 | 12.05 | 8.65 | 6.75 | 532.40 |
384 | 300.00 | 3.40 | 5.23 | 0.00 | 3.40 | 3.46 | 3.43 | 12.03 | 8.63 | 6.74 | 559.80 |
385 | 400.00 | 4.35 | 8.00 | 1.10 | 2.05 | 3.55 | 2.05 | 15.50 | 13.45 | 7.69 | 1119.70 |
386 | 400.00 | 3.40 | 7.31 | 0.00 | 3.40 | 3.54 | 3.43 | 14.11 | 10.71 | 7.28 | 1032.80 |
387 | 300.00 | 3.40 | 5.18 | 0.00 | 3.40 | 3.38 | 3.40 | 11.98 | 8.58 | 6.73 | 559.80 |
388 | 400.00 | 3.85 | 7.35 | 0.00 | 2.95 | 3.64 | 3.49 | 14.15 | 11.20 | 7.09 | 1425.00 |
389 | 300.00 | 3.40 | 5.30 | 0.00 | 3.40 | 3.54 | 3.44 | 12.10 | 8.70 | 6.76 | 661.60 |
390 | 300.00 | 3.40 | 5.20 | 0.00 | 3.40 | 3.45 | 3.45 | 12.00 | 8.60 | 6.73 | 559.80 |
391 | 400.00 | 4.10 | 1.75 | 0.00 | 2.70 | 3.26 | 2.71 | 8.55 | 5.85 | 4.59 | 508.90 |
392 | 400.00 | 4.35 | 8.00 | 0.95 | 2.05 | 3.42 | 2.07 | 15.35 | 13.30 | 7.60 | 1119.70 |
393 | 400.00 | 4.10 | 2.08 | 0.00 | 2.70 | 3.63 | 2.75 | 8.88 | 6.18 | 4.86 | 432.00 |
394 | 300.00 | 3.40 | 5.25 | 0.00 | 3.40 | 3.46 | 3.41 | 12.05 | 8.65 | 6.75 | 532.40 |
395 | 300.00 | 3.40 | 5.30 | 0.00 | 3.40 | 3.53 | 3.43 | 12.10 | 8.70 | 6.76 | 559.80 |
396 | 300.00 | 3.40 | 5.25 | 0.00 | 3.40 | 3.47 | 3.42 | 12.05 | 8.65 | 6.75 | 559.80 |
397 | 400.00 | 4.35 | 8.00 | 1.01 | 2.05 | 3.51 | 2.10 | 15.41 | 13.36 | 7.64 | 1473.00 |
398 | 400.00 | 3.40 | 7.31 | 0.00 | 3.40 | 3.51 | 3.40 | 14.11 | 10.71 | 7.28 | 1395.00 |
399 | 400.00 | 3.40 | 7.35 | 0.00 | 3.40 | 3.57 | 3.42 | 14.15 | 10.75 | 7.29 | 1119.70 |
400 | 300.00 | 3.40 | 5.25 | 0.00 | 3.40 | 3.49 | 3.44 | 12.05 | 8.65 | 6.75 | 661.60 |
401 | 400.00 | 4.35 | 8.00 | 0.99 | 2.05 | 3.44 | 2.05 | 15.39 | 13.34 | 7.63 | 1395.00 |
402 | 300.00 | 3.40 | 5.25 | 0.00 | 3.40 | 3.46 | 3.41 | 12.05 | 8.65 | 6.75 | 610.70 |
403 | 400.00 | 4.10 | 2.08 | 0.00 | 2.70 | 3.58 | 2.70 | 8.88 | 6.18 | 4.86 | 480.00 |
404 | 400.00 | 4.35 | 8.00 | 0.90 | 2.05 | 3.43 | 2.13 | 15.30 | 13.25 | 7.58 | 1395.00 |
405 | 300.00 | 3.40 | 5.30 | 0.00 | 3.40 | 3.50 | 3.40 | 12.10 | 8.70 | 6.76 | 661.60 |
406 | 300.00 | 3.40 | 5.25 | 0.00 | 3.40 | 3.46 | 3.41 | 12.05 | 8.65 | 6.75 | 661.60 |
407 | 300.00 | 3.40 | 5.20 | 0.00 | 3.40 | 3.42 | 3.42 | 12.00 | 8.60 | 6.73 | 661.60 |
408 | 400.00 | 3.50 | 8.00 | 0.05 | 2.90 | 3.36 | 2.91 | 14.45 | 11.55 | 7.40 | 1056.00 |
409 | 300.00 | 3.40 | 5.25 | 0.00 | 3.40 | 3.47 | 3.42 | 12.05 | 8.65 | 6.75 | 559.80 |
410 | 300.00 | 3.40 | 5.30 | 0.00 | 3.40 | 3.50 | 3.40 | 12.10 | 8.70 | 6.76 | 559.80 |
411 | 300.00 | 3.40 | 5.15 | 0.00 | 3.40 | 3.36 | 3.41 | 11.95 | 8.55 | 6.72 | 610.70 |
412 | 300.00 | 3.40 | 5.25 | 0.00 | 3.40 | 3.49 | 3.44 | 12.05 | 8.65 | 6.75 | 610.70 |
413 | 400.00 | 4.35 | 8.00 | 1.04 | 2.05 | 3.54 | 2.10 | 15.44 | 13.39 | 7.65 | 1224.80 |
414 | 400.00 | 4.35 | 8.00 | 1.07 | 2.05 | 3.55 | 2.08 | 15.47 | 13.42 | 7.67 | 1224.80 |
415 | 300.00 | 3.40 | 5.23 | 0.00 | 3.40 | 3.46 | 3.43 | 12.03 | 8.63 | 6.74 | 661.60 |
416 | 400.00 | 4.35 | 8.00 | 1.08 | 2.05 | 3.54 | 2.06 | 15.48 | 13.43 | 7.67 | 1395.00 |
417 | 400.00 | 4.35 | 8.00 | 0.90 | 2.05 | 3.37 | 2.07 | 15.30 | 13.25 | 7.58 | 1395.00 |
418 | 400.00 | 4.25 | 8.00 | 1.00 | 2.15 | 3.56 | 2.16 | 15.40 | 13.25 | 7.67 | 1248.00 |
419 | 400.00 | 4.35 | 8.00 | 0.80 | 2.05 | 3.46 | 2.26 | 15.20 | 13.15 | 7.52 | 1395.00 |
420 | 400.00 | 4.35 | 8.00 | 1.08 | 2.05 | 3.53 | 2.05 | 15.48 | 13.43 | 7.67 | 1152.00 |
421 | 400.00 | 4.35 | 8.00 | 0.80 | 2.05 | 3.45 | 2.25 | 15.20 | 13.15 | 7.52 | 1392.00 |
422 | 400.00 | 4.35 | 8.00 | 1.00 | 2.05 | 3.45 | 2.05 | 15.40 | 13.35 | 7.63 | 1119.70 |
423 | 300.00 | 3.40 | 5.22 | 0.00 | 3.40 | 3.42 | 3.40 | 12.02 | 8.62 | 6.74 | 508.90 |
424 | 400.00 | 3.45 | 6.29 | 0.00 | 3.35 | 3.44 | 3.35 | 13.09 | 9.74 | 7.02 | 1240.00 |
425 | 400.00 | 4.35 | 8.00 | 1.10 | 2.05 | 3.60 | 2.10 | 15.50 | 13.45 | 7.69 | 1395.00 |
426 | 400.00 | 4.35 | 8.00 | 1.05 | 2.05 | 3.51 | 2.06 | 15.45 | 13.40 | 7.66 | 1344.00 |
427 | 300.00 | 3.40 | 5.20 | 0.00 | 3.40 | 3.44 | 3.44 | 12.00 | 8.60 | 6.73 | 611.60 |
428 | 400.00 | 3.40 | 7.40 | 0.00 | 3.40 | 3.61 | 3.41 | 14.20 | 10.80 | 7.30 | 1152.00 |
429 | 300.00 | 3.40 | 5.20 | 0.00 | 3.40 | 3.44 | 3.44 | 12.00 | 8.60 | 6.73 | 661.60 |
430 | 400.00 | 4.35 | 8.00 | 1.00 | 2.05 | 3.50 | 2.10 | 15.40 | 13.35 | 7.63 | 1395.00 |
431 | 400.00 | 3.40 | 7.28 | 0.00 | 3.40 | 3.48 | 3.40 | 14.08 | 10.68 | 7.27 | 1318.00 |
432 | 400.00 | 3.55 | 5.39 | 0.00 | 3.25 | 3.44 | 3.25 | 12.19 | 8.94 | 6.72 | 1083.00 |
433 | 300.00 | 3.40 | 5.30 | 0.00 | 3.40 | 3.50 | 3.40 | 12.10 | 8.70 | 6.76 | 610.70 |
434 | 400.00 | 4.10 | 1.80 | 0.00 | 2.70 | 3.39 | 2.79 | 8.60 | 5.90 | 4.64 | 620.00 |
435 | 300.00 | 3.40 | 5.23 | 0.00 | 3.40 | 3.45 | 3.42 | 12.03 | 8.63 | 6.74 | 559.80 |
436 | 300.00 | 3.40 | 5.30 | 0.00 | 3.40 | 3.52 | 3.42 | 12.10 | 8.70 | 6.76 | 661.60 |
437 | 400.00 | 4.35 | 8.00 | 1.00 | 2.05 | 3.52 | 2.12 | 15.40 | 13.35 | 7.63 | 1395.00 |
438 | 400.00 | 4.35 | 8.00 | 0.95 | 2.05 | 3.45 | 2.10 | 15.35 | 13.30 | 7.60 | 1224.80 |
439 | 400.00 | 3.40 | 7.40 | 0.00 | 3.40 | 3.58 | 3.38 | 14.20 | 10.80 | 7.30 | 967.00 |
440 | 400.00 | 4.35 | 8.00 | 0.97 | 2.05 | 3.42 | 2.05 | 15.37 | 13.32 | 7.61 | 1317.00 |
441 | 400.00 | 3.45 | 8.00 | 0.12 | 2.95 | 3.47 | 2.95 | 14.52 | 11.57 | 7.47 | 1318.00 |
442 | 300.00 | 3.40 | 5.24 | 0.00 | 3.40 | 3.48 | 3.44 | 12.04 | 8.64 | 6.75 | 559.80 |
443 | 300.00 | 3.40 | 5.20 | 0.00 | 3.40 | 3.43 | 3.43 | 12.00 | 8.60 | 6.73 | 661.60 |
444 | 400.00 | 4.45 | 8.00 | 1.10 | 1.95 | 3.49 | 1.99 | 15.50 | 13.55 | 7.65 | 1128.60 |
445 | 300.00 | 3.40 | 5.25 | 0.00 | 3.40 | 3.46 | 3.41 | 12.05 | 8.65 | 6.75 | 559.80 |
446 | 300.00 | 3.40 | 5.18 | 0.00 | 3.40 | 3.38 | 3.40 | 11.98 | 8.58 | 6.73 | 559.80 |
447 | 400.00 | 3.45 | 8.00 | 0.20 | 2.95 | 3.61 | 3.01 | 14.60 | 11.65 | 7.52 | 1344.00 |
448 | 300.00 | 3.40 | 5.25 | 0.00 | 3.40 | 3.47 | 3.42 | 12.05 | 8.65 | 6.75 | 559.80 |
449 | 400.00 | 4.05 | 8.00 | 0.60 | 2.35 | 3.41 | 2.41 | 15.00 | 12.65 | 7.52 | 1240.00 |
450 | 400.00 | 4.45 | 8.00 | 1.00 | 1.95 | 3.40 | 2.00 | 15.40 | 13.45 | 7.59 | 1128.60 |
451 | 300.00 | 3.40 | 5.24 | 0.00 | 3.40 | 3.48 | 3.44 | 12.04 | 8.64 | 6.75 | 610.70 |
452 | 300.00 | 3.40 | 5.22 | 0.00 | 3.40 | 3.44 | 3.42 | 12.02 | 8.62 | 6.74 | 617.00 |
453 | 400.00 | 3.55 | 5.36 | 0.00 | 3.25 | 3.41 | 3.25 | 12.16 | 8.91 | 6.71 | 930.00 |
454 | 400.00 | 3.50 | 8.00 | 0.25 | 2.90 | 3.57 | 2.92 | 14.65 | 11.75 | 7.53 | 1056.00 |
455 | 400.00 | 4.35 | 8.00 | 1.02 | 2.05 | 3.47 | 2.05 | 15.42 | 13.37 | 7.64 | 1152.00 |
456 | 300.00 | 3.40 | 5.25 | 0.00 | 3.40 | 3.46 | 3.41 | 12.05 | 8.65 | 6.75 | 610.70 |
457 | 400.00 | 4.75 | 7.60 | 0.00 | 2.05 | 3.49 | 3.09 | 14.40 | 12.35 | 6.81 | 1473.00 |
458 | 400.00 | 3.40 | 7.24 | 0.00 | 3.40 | 3.44 | 3.40 | 14.04 | 10.64 | 7.26 | 967.00 |
459 | 400.00 | 4.35 | 8.00 | 1.10 | 2.05 | 3.60 | 2.10 | 15.50 | 13.45 | 7.69 | 1297.80 |
460 | 400.00 | 3.45 | 8.00 | 0.12 | 2.95 | 3.47 | 2.95 | 14.52 | 11.57 | 7.47 | 1318.00 |
461 | 400.00 | 3.45 | 8.00 | 0.06 | 2.95 | 3.41 | 2.95 | 14.46 | 11.51 | 7.43 | 1240.00 |
462 | 400.00 | 4.45 | 8.00 | 0.94 | 1.95 | 3.49 | 2.15 | 15.34 | 13.39 | 7.56 | 1318.00 |
463 | 300.00 | 3.40 | 5.25 | 0.00 | 3.40 | 3.48 | 3.43 | 12.05 | 8.65 | 6.75 | 508.90 |
464 | 300.00 | 3.40 | 5.30 | 0.00 | 3.40 | 3.51 | 3.41 | 12.10 | 8.70 | 6.76 | 559.80 |
465 | 400.00 | 4.10 | 1.50 | 0.00 | 2.70 | 3.04 | 2.74 | 8.30 | 5.60 | 4.38 | 508.90 |
466 | 400.00 | 4.25 | 8.00 | 0.40 | 2.15 | 3.55 | 2.75 | 14.80 | 12.65 | 7.32 | 1392.00 |
467 | 400.00 | 3.45 | 5.25 | 0.00 | 3.35 | 3.45 | 3.40 | 12.05 | 8.70 | 6.72 | 1240.00 |
468 | 300.00 | 3.40 | 5.25 | 0.00 | 3.40 | 3.49 | 3.44 | 12.05 | 8.65 | 6.75 | 559.80 |
469 | 400.00 | 4.35 | 8.00 | 1.07 | 2.05 | 3.52 | 2.05 | 15.47 | 13.42 | 7.67 | 1425.00 |
470 | 400.00 | 4.35 | 8.00 | 1.07 | 2.05 | 3.52 | 2.05 | 15.47 | 13.42 | 7.67 | 1425.00 |
471 | 300.00 | 3.40 | 5.26 | 0.00 | 3.40 | 3.49 | 3.43 | 12.06 | 8.66 | 6.75 | 508.90 |
472 | 400.00 | 3.85 | 7.60 | 0.00 | 2.95 | 3.67 | 3.27 | 14.40 | 11.45 | 7.15 | 1425.00 |
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Nguyen, H., Cao, MT., Tran, XL. et al. A novel whale optimization algorithm optimized XGBoost regression for estimating bearing capacity of concrete piles. Neural Comput & Applic 35, 3825–3852 (2023). https://doi.org/10.1007/s00521-022-07896-w
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DOI: https://doi.org/10.1007/s00521-022-07896-w