Smart Artificial Firefly Colony Algorithm-Based Support Vector Regression for Enhanced Forecasting in Civil Engineering
Advanced data mining techniques are potential tools for solving civil engineering (CE) problems. This study proposes a novel smart artificial firefly colony algorithm‐based support vector regression (SAFCA‐SVR) system that integrates firefly algorithm (FA), chaotic maps, adaptive inertia weight, Lév...
Uloženo v:
| Vydáno v: | Computer-aided civil and infrastructure engineering Ročník 30; číslo 9; s. 715 - 732 |
|---|---|
| Hlavní autoři: | , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
| Vydáno: |
Blackwell Publishing Ltd
01.09.2015
|
| ISSN: | 1093-9687, 1467-8667 |
| On-line přístup: | Získat plný text |
| Tagy: |
Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
|
| Abstract | Advanced data mining techniques are potential tools for solving civil engineering (CE) problems. This study proposes a novel smart artificial firefly colony algorithm‐based support vector regression (SAFCA‐SVR) system that integrates firefly algorithm (FA), chaotic maps, adaptive inertia weight, Lévy flight, and least squares support vector regression (LS‐SVR). First, adaptive approach and randomization methods are incorporated in FA to construct a novel and highly effective metaheuristic algorithm for global optimization. The enhanced FA is then used to optimize parameters in LS‐SVR model. The proposed system is validated by comparing its performance with those of empirical methods and previous works via cross‐validation algorithm and hypothesis test through the real‐world engineering cases. Specifically, high‐performance concrete, resilient modulus of subgrade soils, and building cooling load are used as case studies. The SAFCA‐SVR achieved 8.8%–91.3% better error rates than those of previous works. Analytical results confirm that using the proposed hybrid system significantly improves the accuracy in solving CE problems. |
|---|---|
| AbstractList | Advanced data mining techniques are potential tools for solving civil engineering (CE) problems. This study proposes a novel smart artificial firefly colony algorithm‐based support vector regression (SAFCA‐SVR) system that integrates firefly algorithm (FA), chaotic maps, adaptive inertia weight, Lévy flight, and least squares support vector regression (LS‐SVR). First, adaptive approach and randomization methods are incorporated in FA to construct a novel and highly effective metaheuristic algorithm for global optimization. The enhanced FA is then used to optimize parameters in LS‐SVR model. The proposed system is validated by comparing its performance with those of empirical methods and previous works via cross‐validation algorithm and hypothesis test through the real‐world engineering cases. Specifically, high‐performance concrete, resilient modulus of subgrade soils, and building cooling load are used as case studies. The SAFCA‐SVR achieved 8.8%–91.3% better error rates than those of previous works. Analytical results confirm that using the proposed hybrid system significantly improves the accuracy in solving CE problems. |
| Author | Pham, Anh-Duc Chou, Jui-Sheng |
| Author_xml | – sequence: 1 givenname: Jui-Sheng surname: Chou fullname: Chou, Jui-Sheng email: jschou@mail.ntust.edu.tw organization: Department of Civil and Construction Engineering, National Taiwan University of Science and Technology, Taipei, Taiwan – sequence: 2 givenname: Anh-Duc surname: Pham fullname: Pham, Anh-Duc organization: Department of Civil and Construction Engineering, National Taiwan University of Science and Technology, Taipei, Taiwan |
| BookMark | eNp9kE9PGzEQxS0UJBLopZ_AZ6QNdrzxZo9hlQQk2gpC26NleyfJwMaObPMn376maXuoEDMH2-P3G-m9Aek574CQz5wNea6LLVoY8lHuI9LnpayKiZRVL99ZLYpaTqoTMojxgeUqS9EnL8utDolOQ8IVWtQdnWOAVbenje-829Npt_YB02ZbXOoILV0-7XY-Ez_AJh_oHawDxIje0VV-ztxGO5tlcx_A6pjQrSk62uAzdvl3jQ4g5OEZOV7pLsKnP-cp-T6f3TdXxc23xXUzvSmsmHBegK2Y5poJrTWUMBrXY1MzW7bMsFbymktu2kqOjJGGM2GEAWGZNFa2ANYYcUrOD3tt8DFmZ2oXMFveK87UW2TqLTL1O7IsZv-JLSadsrkUNHbvI_yAvGAH-w-Wqy_XzewvUxwYjAle_zE6PCpZiWqsfn5dqMVicifKW6Fq8QtpdpJn |
| CitedBy_id | crossref_primary_10_3390_s18051635 crossref_primary_10_1111_exsy_12205 crossref_primary_10_1002_suco_70211 crossref_primary_10_1038_s41529_022_00218_4 crossref_primary_10_1016_j_autcon_2022_104579 crossref_primary_10_1016_j_apenergy_2019_03_078 crossref_primary_10_1016_j_engstruct_2022_114148 crossref_primary_10_3390_e23040477 crossref_primary_10_1016_j_engstruct_2022_114421 crossref_primary_10_1111_mice_12191 crossref_primary_10_1007_s41939_023_00187_4 crossref_primary_10_1016_j_energy_2019_116370 crossref_primary_10_1016_j_istruc_2021_04_048 crossref_primary_10_1016_j_aej_2024_11_084 crossref_primary_10_1061__ASCE_CP_1943_5487_0000595 crossref_primary_10_1111_mice_12361 crossref_primary_10_1007_s43538_024_00347_1 crossref_primary_10_14359_51689560 crossref_primary_10_1007_s00034_019_01088_z crossref_primary_10_1016_j_autcon_2016_03_015 crossref_primary_10_1016_j_engappai_2024_109904 crossref_primary_10_1007_s41939_023_00213_5 crossref_primary_10_3390_app9224978 crossref_primary_10_1016_j_aei_2024_102642 crossref_primary_10_1016_j_aei_2020_101154 crossref_primary_10_1016_j_conbuildmat_2019_07_315 crossref_primary_10_1007_s00521_020_05048_6 crossref_primary_10_1016_j_eswa_2015_10_020 crossref_primary_10_1007_s00500_019_04103_2 crossref_primary_10_1007_s00521_016_2731_8 crossref_primary_10_3390_s18082713 crossref_primary_10_1007_s41870_024_01998_5 crossref_primary_10_1061__ASCE_MT_1943_5533_0002902 crossref_primary_10_1515_revneuro_2016_0029 crossref_primary_10_1155_2016_5089683 crossref_primary_10_1155_2022_5802217 crossref_primary_10_1007_s00521_019_04661_4 crossref_primary_10_1016_j_soildyn_2017_05_013 crossref_primary_10_1093_ijlct_ctad081 crossref_primary_10_1111_mice_12456 crossref_primary_10_1080_15732479_2020_1734632 crossref_primary_10_1111_exsy_12185 crossref_primary_10_1155_2020_8892106 crossref_primary_10_1007_s41939_023_00181_w crossref_primary_10_1061__ASCE_CP_1943_5487_0000814 crossref_primary_10_1002_spy2_221 crossref_primary_10_1016_j_conbuildmat_2021_125437 crossref_primary_10_1007_s41062_021_00506_z crossref_primary_10_3390_asi4030052 crossref_primary_10_1111_exsy_12357 crossref_primary_10_1016_j_compbiomed_2017_06_022 crossref_primary_10_1111_mice_12617 crossref_primary_10_1109_TNNLS_2017_2682102 crossref_primary_10_3390_ma16031244 crossref_primary_10_1016_j_engappai_2016_09_008 crossref_primary_10_1007_s10706_020_01536_7 crossref_primary_10_1016_j_cscm_2025_e04475 crossref_primary_10_1080_10298436_2025_2487935 crossref_primary_10_3233_JIFS_221342 crossref_primary_10_1016_j_jweia_2020_104138 crossref_primary_10_3390_su10010014 crossref_primary_10_1007_s00366_022_01781_9 crossref_primary_10_1108_ECAM_08_2023_0801 crossref_primary_10_1007_s00366_020_01249_8 crossref_primary_10_3233_ICA_160510 crossref_primary_10_1016_j_bbr_2015_10_036 crossref_primary_10_14359_51689360 crossref_primary_10_1016_j_conbuildmat_2022_129534 crossref_primary_10_14359_51689485 crossref_primary_10_1111_exsy_12255 crossref_primary_10_1111_mice_12288 crossref_primary_10_1016_j_conbuildmat_2020_120950 crossref_primary_10_1109_TII_2018_2794389 crossref_primary_10_1111_mice_13408 crossref_primary_10_1016_j_eswa_2021_115728 crossref_primary_10_1016_j_jobe_2022_105046 crossref_primary_10_1007_s00500_019_03863_1 crossref_primary_10_1016_j_engstruct_2017_10_070 crossref_primary_10_1088_1757_899X_1004_1_012010 crossref_primary_10_1111_mice_12257 crossref_primary_10_1111_mice_12654 crossref_primary_10_1002_suco_202100199 crossref_primary_10_1111_mice_12256 crossref_primary_10_1007_s42107_023_00984_9 crossref_primary_10_1111_mice_12408 crossref_primary_10_1016_j_ecoinf_2018_01_005 crossref_primary_10_3390_math9060686 crossref_primary_10_1016_j_compbiomed_2017_07_009 crossref_primary_10_1016_j_engappai_2023_106155 crossref_primary_10_1061__ASCE_CP_1943_5487_0000561 crossref_primary_10_3390_su16177591 crossref_primary_10_1016_j_conbuildmat_2024_138808 crossref_primary_10_1111_mice_12422 crossref_primary_10_3233_ICA_170546 crossref_primary_10_1016_j_conbuildmat_2020_121424 crossref_primary_10_1016_j_conbuildmat_2024_135782 crossref_primary_10_1142_S0218001416390018 crossref_primary_10_3233_JIFS_211088 crossref_primary_10_1007_s00521_019_04146_4 crossref_primary_10_1088_1755_1315_552_1_012020 |
| Cites_doi | 10.1016/j.enbuild.2004.09.009 10.1061/(ASCE)0733-9445(2000)126:5(596) 10.1111/mice.12062 10.1111/mice.12017 10.1061/(ASCE)0733-947X(2005)131:10(771) 10.3233/ICA-130428 10.1002/9780470640425 10.1007/978-1-4757-2440-0 10.3846/13923730.2011.574343 10.12989/cac.2008.5.6.559 10.1162/089976603321891855 10.1016/j.swevo.2011.06.003 10.1109/TSMCA.2012.2224338 10.1023/A:1012427100071 10.1002/nme.2274 10.1016/j.enbuild.2012.03.010 10.1023/B:MACH.0000008082.80494.e0 10.1111/j.1467-8667.2009.00595.x 10.1111/mice.12020 10.1016/j.advengsoft.2008.05.003 10.1023/A:1018628609742 10.1111/0885-9507.00229 10.1016/j.enbuild.2012.11.030 10.1109/IWISA.2009.5072707 10.1016/S0360-1323(00)00026-3 10.5815/ijisa.2012.10.06 10.1680/macr.2010.62.8.585 10.1111/j.1467-8667.2012.00786.x 10.1111/mice.12001 10.1111/j.1467-8667.2009.00615.x 10.1016/S0893-6080(00)00077-0 10.1080/10298436.2012.671944 10.1109/EPDC.2013.6565962 10.1109/NABIC.2009.5393690 10.1016/j.engappai.2003.09.011 10.1016/j.eswa.2012.10.036 10.1016/j.eswa.2012.02.063 10.1016/j.engappai.2012.08.015 10.1007/s00521-010-0432-2 10.1061/(ASCE)0733-9364(1998)124:1(18) 10.1016/j.rser.2012.03.045 10.1016/j.conbuildmat.2012.02.038 10.3233/ICA-130421 10.1007/s00521-011-0734-z 10.1016/S0008-8846(98)00165-3 10.1007/s00366-011-0251-9 10.1016/j.apenergy.2008.11.035 10.1016/j.enbuild.2012.03.003 10.1016/S0008-8846(02)00787-1 10.1016/j.eswa.2008.07.004 10.1016/j.chaos.2006.04.057 10.1080/10298430500140891 10.2172/1018100 10.1016/j.ress.2011.05.005 10.1111/j.1467-8667.2012.00769.x 10.1103/PhysRevLett.54.616 10.1080/14680629.2009.9690218 10.1142/5089 10.1016/j.conbuildmat.2013.08.078 10.1109/81.933333 10.1016/j.cnsns.2012.06.009 10.1007/978-3-642-04586-8_10 10.1016/j.neunet.2009.05.003 10.1111/0885-9507.00219 10.1016/j.asoc.2010.06.003 10.1111/j.1467-8667.2012.00767.x 10.1016/j.ijepes.2009.03.020 10.1007/978-3-642-22185-9_6 10.1016/j.advengsoft.2011.09.014 |
| ContentType | Journal Article |
| Copyright | 2015 |
| Copyright_xml | – notice: 2015 |
| DBID | BSCLL AAYXX CITATION |
| DOI | 10.1111/mice.12121 |
| DatabaseName | Istex CrossRef |
| DatabaseTitle | CrossRef |
| DatabaseTitleList | CrossRef |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Applied Sciences Engineering Computer Science |
| EISSN | 1467-8667 |
| EndPage | 732 |
| ExternalDocumentID | 10_1111_mice_12121 MICE12121 ark_67375_WNG_GG8R34Q3_9 |
| Genre | article |
| GroupedDBID | ..I .3N .4S .DC .GA 05W 0R~ 10A 1OB 1OC 29F 31~ 33P 3SF 4.4 50Y 50Z 51W 51X 52M 52N 52O 52P 52S 52T 52U 52W 52X 5GY 5HH 5LA 5VS 66C 6P2 702 7PT 8-0 8-1 8-3 8-4 8-5 8UM 930 A03 AAESR AAEVG AAHQN AAMMB AAMNL AANHP AANLZ AAONW AASGY AAXRX AAYCA AAZKR ABCQN ABCUV ABDBF ABEML ABFSI ABJNI ACAHQ ACBWZ ACCZN ACGFS ACPOU ACRPL ACSCC ACUHS ACXBN ACXQS ACYXJ ADBBV ADEOM ADIZJ ADKYN ADMGS ADMLS ADNMO ADOZA ADXAS ADZMN AEFGJ AEIGN AEIMD AENEX AEUYR AEYWJ AFBPY AFEBI AFFPM AFGKR AGHNM AGQPQ AGXDD AGYGG AHBTC AHEFC AI. AIDQK AIDYY AIQQE AITYG AIURR AJXKR ALAGY ALMA_UNASSIGNED_HOLDINGS ALUQN ALVPJ AMBMR AMYDB ARCSS ASPBG ATUGU AUFTA AVWKF AZBYB AZFZN AZVAB BAFTC BDRZF BFHJK BHBCM BMNLL BMXJE BNHUX BROTX BRXPI BSCLL BY8 CAG COF CS3 CWDTD D-E D-F DCZOG DPXWK DR2 DRFUL DRSTM DU5 E.L EAD EAP EBS EDO EJD EMK EST ESX F00 F01 F04 FEDTE G-S G.N GODZA H.T H.X HF~ HGLYW HVGLF HZI HZ~ I-F IHE IX1 J0M K48 LATKE LC2 LC3 LEEKS LH4 LITHE LOXES LP6 LP7 LUTES LW6 LYRES MEWTI MK4 MK~ MRFUL MRSTM MSFUL MSSTM MXFUL MXSTM N04 N05 NF~ O66 O9- OIG P2P P2W P2X P4D PALCI Q.N Q11 QB0 R.K RJQFR RX1 SAMSI SUPJJ TN5 TUS UB1 VH1 W8V W99 WBKPD WIH WIK WLBEL WOHZO WQJ WXSBR WYISQ XG1 ZZTAW ~IA ~WT AAHHS ACCFJ ADZOD AEEZP AEQDE AEUQT AFPWT AIWBW AJBDE WRC AAYXX CITATION O8X |
| ID | FETCH-LOGICAL-c3811-ec70a1a03aaae4e2595b90c4d0b0d619161bd762bb6b103b3be3c06bc6deecbb3 |
| IEDL.DBID | DRFUL |
| ISICitedReferencesCount | 115 |
| ISICitedReferencesURI | http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000358692800004&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| ISSN | 1093-9687 |
| IngestDate | Sat Nov 29 05:42:04 EST 2025 Tue Nov 18 21:43:07 EST 2025 Wed Jan 22 16:55:09 EST 2025 Sun Sep 21 06:19:13 EDT 2025 |
| IsPeerReviewed | true |
| IsScholarly | true |
| Issue | 9 |
| Language | English |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-c3811-ec70a1a03aaae4e2595b90c4d0b0d619161bd762bb6b103b3be3c06bc6deecbb3 |
| Notes | istex:9EE18008EB33C39D295E35311228EB5982FAD0FD ark:/67375/WNG-GG8R34Q3-9 ArticleID:MICE12121 |
| PageCount | 18 |
| ParticipantIDs | crossref_primary_10_1111_mice_12121 crossref_citationtrail_10_1111_mice_12121 wiley_primary_10_1111_mice_12121_MICE12121 istex_primary_ark_67375_WNG_GG8R34Q3_9 |
| PublicationCentury | 2000 |
| PublicationDate | September 2015 |
| PublicationDateYYYYMMDD | 2015-09-01 |
| PublicationDate_xml | – month: 09 year: 2015 text: September 2015 |
| PublicationDecade | 2010 |
| PublicationTitle | Computer-aided civil and infrastructure engineering |
| PublicationTitleAlternate | Computer-Aided Civil and Infrastructure Engineering |
| PublicationYear | 2015 |
| Publisher | Blackwell Publishing Ltd |
| Publisher_xml | – name: Blackwell Publishing Ltd |
| References | Tsanas, A. & Xifara, A. (2012), Accurate quantitative estimation of energy performance of residential buildings using statistical machine learning tools, Energy and Buildings, 49, 560-67. Coelho, L. d. S. & Mariani, V. C. (2013), Improved firefly algorithm approach applied to chiller loading for energy conservation, Energy and Buildings, 59, 273-78. Ekici, B. B. & Aksoy, U. T. (2009), Prediction of building energy consumption by using artificial neural networks, Advances in Engineering Software, 40(5), 356-62. He, D., He, C., Jiang, L. G., Zhu, H. W. & Hu, G. R. (2001), Chaotic characteristics of a one-dimensional iterative map with infinite collapses, IEEE Transactions on Circuits and Systems I: Fundamental Theory and Applications, 48(7), 900-06. Amini, F., Hazaveh, N. K. & Rad, A. A. (2013), Wavelet PSO-based LQR algorithm for optimal structural control using active tuned mass dampers, Computer-Aided Civil and Infrastructure Engineering, 28(7), 542-57. Yeh, I. C. & Lien, L.-C. (2009), Knowledge discovery of concrete material using genetic operation trees, Expert Systems with Applications, 36(3, Part 2), 5807-12. Hsu, C.-W. & Lin, C.-J. (2002), A simple decomposition method for support vector machines, Machine Learning, 46(1-3), 291-314. Hong, W.-C. (2009), Hybrid evolutionary algorithms in a SVR-based electric load forecasting model, International Journal of Electrical Power & Energy Systems, 31(7-8), 409-17. Dharia, A. & Adeli, H. (2003), Neural network model for rapid forecasting of freeway link travel time, Engineering Applications of Artificial Intelligence, 16(7-8), 607-13. Gandomi, A. H., Yang, X. S., Talatahari, S. & Alavi, A. H. (2013), Firefly algorithm with chaos, Communications in Nonlinear Science and Numerical Simulation, 18(1), 89-98. Edwards, R. E., New, J. & Parker, L. E. (2012), Predicting future hourly residential electrical consumption: a machine learning case study, Energy and Buildings, 49, 591-603. Van-Gestel, T., Suykens, J. K., Baesens, B., Viaene, S., Vanthienen, J., Dedene, G., De-Moor, B. & Vandewalle, J. (2004), Benchmarking least squares support vector machine classifiers, Machine Learning, 54(1), 5-32. Kris, D. B. (2011), Least Squares Support Vector Regression with Applications to Large-Scale Data: A Statistical Approach, Faculty of Engineering, KU Leuven, Katholieke Universiteit Leuven, Leuven, Belgium. Gandomi, A. & Alavi, A. (2012), A new multi-gene genetic programming approach to nonlinear system modeling. Part I: materials and structural engineering problems, Neural Computing and Applications, 21(1), 171-87. Li, D., Xu, L., Goodman, E. D., Xu, Y. & Wu, Y. (2013), Integrating a statistical background-foreground extraction algorithm and SVM classifier for pedestrian detection and tracking, Integrated Computer-Aided Engineering, 20(3), 201-16. ASSHTO (2008), Mechanistic-Empirical Pavement Design Guide: A Manual of Practice, Washington DC. Yang, I. T. & Hsieh, Y.-H. (2013), Reliability-based design optimization with cooperation between support vector machine and particle swarm optimization, Engineering with Computers, 29(2), 151-63. Rahim, A. M. (2005), Subgrade soil index properties to estimate resilient modulus for pavement design, International Journal of Pavement Engineering, 6(3), 163-69. Adeli, H. & Wu, M. (1998), Regularization neural network for construction cost estimation, Journal of Construction Engineering and Management, 124(1), 18-24. Li, Y., Deng, S. & Xiao, D. (2011), A novel hash algorithm construction based on chaotic neural network, Neural Computing and Applications, 20(1), 133-41. Liao, S.-H., Chu, P.-H. & Hsiao, P.-Y. (2012), Data mining techniques and applications - a decade review from 2000 to 2011, Expert Systems with Applications, 39(12), 11303-11. Huang, Y. H. (1993), Pavement Analysis and Design, Prentice Hall, Englewood Cliffs, NJ. Lin, D.-Y. & Ku, Y.-H. (2014), Using genetic algorithms to optimize stopping patterns for passenger rail transportation, Computer-Aided Civil and Infrastructure Engineering, 29(4), 264-78. Yang, D., Li, G. & Cheng, G. (2007), On the efficiency of chaos optimization algorithms for global optimization, Chaos, Solitons & Fractals, 34(4), 1366-75. Panakkat, A. & Adeli, H. (2009), Recurrent neural network for approximate earthquake time and location prediction using multiple seismicity indicators, Computer-Aided Civil and Infrastructure Engineering, 24(4), 280-92. Senthilnath, J., Omkar, S. N. & Mani, V. (2011), Clustering using firefly algorithm: performance study, Swarm and Evolutionary Computation, 1(3), 164-71. Jie, S., Hui, L. & Adeli, H. (2013), Concept drift-oriented adaptive and dynamic support vector machine ensemble with time window in corporate financial risk prediction, IEEE Transactions on Systems, Man, and Cybernetics: Systems, 43(4), 801-13. Chou, J.-S. & Pham, A.-D. (2013), Enhanced artificial intelligence for ensemble approach to predicting high performance concrete compressive strength, Construction and Building Materials, 49, 554-63. Li, Q., Meng, Q., Cai, J., Yoshino, H. & Mochida, A. (2009), Applying support vector machine to predict hourly cooling load in the building, Applied Energy, 86(10), 2249-56. Dai, H., Zhang, H., Wang, W. & Xue, G. (2012), Structural reliability assessment by local approximation of limit state functions using adaptive Markov chain simulation and support vector regression, Computer-Aided Civil and Infrastructure Engineering, 27(9), 676-86. Al-Homoud, M. S. (2001), Computer-aided building energy analysis techniques, Building and Environment, 36(4), 421-33. Chou, J. S., Cheng, M. Y. & Wu, Y. W. (2013), Improving classification accuracy of project dispute resolution using hybrid artificial intelligence and support vector machine models, Expert Systems with Applications, 40(6), 2263-74. Yeh, I. C. (1998), Modeling of strength of high-performance concrete using artificial neural networks, Cement and Concrete Research, 28(12), 1797-808. Burdick, A. (2011), Strategy Guideline: Accurate Heating and Cooling Load Calculations, IBACOS, Inc., US Department of Energy, Washington DC. Yang, X.-S. (2008), Firefly Algorithm, Luniver Press, Bristol, UK. Hsiao, F.-Y., Wang, S.-H., Wang, W.-C., Wen, C.-P. & Yu, W.-D. (2012), Neuro-fuzzy cost estimation model enhanced by fast messy genetic algorithms for semiconductor hookup construction, Computer-Aided Civil and Infrastructure Engineering, 27(10), 764-81. Montalvo, I., Izquierdo, J., Pérez-García, R. & Herrera, M. (2014), Water distribution system computer-aided design by agent swarm optimization, Computer-Aided Civil and Infrastructure Engineering, 29(6), 433-48. Boukhatem, B., Kenai, S., Tagnit-Hamou, A. & Ghrici, M. (2011), Application of new information technology on concrete: an overview, Journal of Civil Engineering and Management, 17(2), 248-58. Suykens, J. A. K., Vandewalle, J. & De-Moor, B. (2001), Optimal control by least squares support vector machines, Neural Networks, 14(1), 23-35. Asuncion, A. & Newman, D. (2007), Uci Machine Learning Repository, University of California, School of Information and Computer Science, Irvine, CA. Hong, W.-C., Dong, Y., Chen, L.-Y. & Wei, S.-Y. (2011), SVR with hybrid chaotic genetic algorithms for tourism demand forecasting, Applied Soft Computing, 11(2), 1881-90. Adeli, H. & Panakkat, A. (2009), A probabilistic neural network for earthquake magnitude prediction, Neural Networks, 22(7), 1018-24. Keerthi, S. S. & Lin, C.-J. (2003), Asymptotic behaviors of support vector machines with Gaussian kernel, Neural Computation, 15(7), 1667-89. Bianchini, A. & Bandini, P. (2010), Prediction of pavement performance through neuro-fuzzy reasoning, Computer-Aided Civil and Infrastructure Engineering, 25(1), 39-54. Jiang, X. & Adeli, H. (2005), Dynamic wavelet neural network model for traffic flow forecasting, Journal of Transportation Engineering, 131(10), 771-79. Pacheco, R., Ordóñez, J. & Martínez, G. (2012), Energy efficient design of building: a review, Renewable and Sustainable Energy Reviews, 16(6), 3559-73. Verma, A., Wei, X. & Kusiak, A. (2013), Predicting the total suspended solids in wastewater: a data-mining approach, Engineering Applications of Artificial Intelligence, 26(4), 1366-72. Adeli, H. (2001), Neural networks in civil engineering: 1989-2000, Computer-Aided Civil and Infrastructure Engineering, 16(2), 126-42. Park, H. I., Kweon, G. C. & Lee, S. R. (2009), Prediction of resilient modulus of granular subgrade soils and subbase materials using artificial neural network, Road Materials and Pavement Design, 10(3), 647-65. Atici, U. (2010), Prediction of the strength of mineral-addition concrete using regression analysis, Magazine of Concrete Research, 62(8), 585-92. George, K. P. (2004), Prediction of Resilient Modulus from Soil Index Properties, Mississippi Department of Transportation, Jackson, Mississippi. Pal, S. K., Rai, C. S. & Singh, A. P. (2012), Comparative study of firefly algorithm and particle swarm optimization for noisy non-linear optimization problems, International Journal of Intelligent Systems and Applications, 4, 50-57. Shafahi, Y. & Bagherian, M. (2013), A customized particle swarm method to solve highway alignment optimization problem, Computer-Aided Civil and Infrastructure Engineering, 28(1), 52-67. Suykens, J. A. K. & Vandewalle, J. (1999), Least squares support vector machine classifiers, Neural Processing Letters, 9(3), 293-300. Nazzal, M. D. & Tatari, O. (2013), Evaluating the use of neural networks and genetic algorithms for prediction of subgrade resilient modulus, International Journal of Pavement Engineering, 14(4), 364-73. Yang, X.-S. (2010), Engineering Optimization: An Introduction with Metaheuristic Applications, John Wiley & Sons, Hoboken, NJ. Rigatos, G. G. (2013), Adaptive fuzzy control for differentially flat MIMO nonlinear dynamical systems, Integrated Computer-Aided Engineering, 20(2), 111-26. Mousavi, S. M., Aminian, P., Gandomi, A. H., Alavi, A. H. & Bolandi, H. (2012), A new predictive model for compressive strength o 2013; 29 2013; 26 2009; 40 2013; 28 2005; 131 2009; 86 2013; 20 2011; 96 2011; 11 2003; 15 2003; 16 2001; 48 2008; 5 2014; 29 2012; 16 2008; 75 2011; 17 2007; 34 2010; 62 2013; 18 2013; 59 2013; 14 2010; 25 2009; 10 2000; 126 2002; 46 2011; 20 1985 2001; 16 2012; 27 2005; 37 1998; 124 1985; 54 2001; 14 2012; 21 2009; 22 2009; 24 1998; 28 2013; 49 2011; 1 2011 2013; 43 2010 2013; 40 2002; 32 2009 2008 2007 1995 2005 2004 1993 2012; 39 2002 2012; 34 1999; 9 2004; 54 2009; 36 2009; 31 2005; 6 2012; 49 2013 2012; 45 2012; 4 2001; 36 e_1_2_7_5_1 Yang X.‐S. (e_1_2_7_75_1) 2008 e_1_2_7_3_1 e_1_2_7_9_1 e_1_2_7_19_1 Pham D. (e_1_2_7_55_1) 2005 e_1_2_7_60_1 e_1_2_7_17_1 e_1_2_7_62_1 e_1_2_7_81_1 e_1_2_7_15_1 e_1_2_7_64_1 e_1_2_7_13_1 Kris D. B. (e_1_2_7_40_1) 2011 e_1_2_7_43_1 e_1_2_7_66_1 ASSHTO (e_1_2_7_7_1) 2008 e_1_2_7_11_1 e_1_2_7_45_1 e_1_2_7_68_1 e_1_2_7_47_1 e_1_2_7_26_1 e_1_2_7_49_1 e_1_2_7_28_1 e_1_2_7_73_1 e_1_2_7_50_1 e_1_2_7_71_1 e_1_2_7_25_1 e_1_2_7_31_1 e_1_2_7_52_1 e_1_2_7_77_1 e_1_2_7_23_1 e_1_2_7_33_1 e_1_2_7_54_1 e_1_2_7_21_1 e_1_2_7_35_1 e_1_2_7_56_1 Huang Y. H. (e_1_2_7_34_1) 1993 e_1_2_7_37_1 e_1_2_7_58_1 e_1_2_7_79_1 e_1_2_7_39_1 George K. P. (e_1_2_7_27_1) 2004 e_1_2_7_6_1 e_1_2_7_4_1 e_1_2_7_80_1 e_1_2_7_18_1 Asuncion A. (e_1_2_7_8_1) 2007 e_1_2_7_16_1 e_1_2_7_61_1 e_1_2_7_2_1 e_1_2_7_42_1 e_1_2_7_63_1 e_1_2_7_12_1 e_1_2_7_44_1 e_1_2_7_65_1 e_1_2_7_10_1 e_1_2_7_46_1 e_1_2_7_67_1 Carmichael R. F. (e_1_2_7_14_1) 1985 Rigatos G. G. (e_1_2_7_57_1) 2013; 20 e_1_2_7_48_1 e_1_2_7_69_1 e_1_2_7_29_1 Li D. (e_1_2_7_41_1) 2013; 20 e_1_2_7_72_1 e_1_2_7_51_1 e_1_2_7_70_1 e_1_2_7_30_1 e_1_2_7_53_1 e_1_2_7_76_1 e_1_2_7_24_1 e_1_2_7_32_1 e_1_2_7_74_1 e_1_2_7_22_1 e_1_2_7_20_1 e_1_2_7_36_1 e_1_2_7_59_1 e_1_2_7_78_1 e_1_2_7_38_1 |
| References_xml | – reference: Hsu, C.-W. & Lin, C.-J. (2002), A simple decomposition method for support vector machines, Machine Learning, 46(1-3), 291-314. – reference: Hsiao, F.-Y., Wang, S.-H., Wang, W.-C., Wen, C.-P. & Yu, W.-D. (2012), Neuro-fuzzy cost estimation model enhanced by fast messy genetic algorithms for semiconductor hookup construction, Computer-Aided Civil and Infrastructure Engineering, 27(10), 764-81. – reference: Senthilnath, J., Omkar, S. N. & Mani, V. (2011), Clustering using firefly algorithm: performance study, Swarm and Evolutionary Computation, 1(3), 164-71. – reference: Asuncion, A. & Newman, D. (2007), Uci Machine Learning Repository, University of California, School of Information and Computer Science, Irvine, CA. – reference: Coelho, L. d. S. & Mariani, V. C. (2013), Improved firefly algorithm approach applied to chiller loading for energy conservation, Energy and Buildings, 59, 273-78. – reference: Jiang, X. & Adeli, H. (2008), Neuro-genetic algorithm for non-linear active control of structures, International Journal for Numerical Methods in Engineering, 75(7), 770-86. – reference: Li, Q., Meng, Q., Cai, J., Yoshino, H. & Mochida, A. (2009), Applying support vector machine to predict hourly cooling load in the building, Applied Energy, 86(10), 2249-56. – reference: Rigatos, G. G. (2013), Adaptive fuzzy control for differentially flat MIMO nonlinear dynamical systems, Integrated Computer-Aided Engineering, 20(2), 111-26. – reference: Suykens, J. A. K., Vandewalle, J. & De-Moor, B. (2001), Optimal control by least squares support vector machines, Neural Networks, 14(1), 23-35. – reference: Huang, Y. H. (1993), Pavement Analysis and Design, Prentice Hall, Englewood Cliffs, NJ. – reference: Adeli, H. (2001), Neural networks in civil engineering: 1989-2000, Computer-Aided Civil and Infrastructure Engineering, 16(2), 126-42. – reference: ASSHTO (2008), Mechanistic-Empirical Pavement Design Guide: A Manual of Practice, Washington DC. – reference: Edwards, R. E., New, J. & Parker, L. E. (2012), Predicting future hourly residential electrical consumption: a machine learning case study, Energy and Buildings, 49, 591-603. – reference: Rahim, A. M. (2005), Subgrade soil index properties to estimate resilient modulus for pavement design, International Journal of Pavement Engineering, 6(3), 163-69. – reference: Chou, J.-S. & Pham, A.-D. (2013), Enhanced artificial intelligence for ensemble approach to predicting high performance concrete compressive strength, Construction and Building Materials, 49, 554-63. – reference: Boukhatem, B., Kenai, S., Tagnit-Hamou, A. & Ghrici, M. (2011), Application of new information technology on concrete: an overview, Journal of Civil Engineering and Management, 17(2), 248-58. – reference: Pacheco, R., Ordóñez, J. & Martínez, G. (2012), Energy efficient design of building: a review, Renewable and Sustainable Energy Reviews, 16(6), 3559-73. – reference: Gandomi, A. H., Yang, X. S., Talatahari, S. & Alavi, A. H. (2013), Firefly algorithm with chaos, Communications in Nonlinear Science and Numerical Simulation, 18(1), 89-98. – reference: Li, D., Xu, L., Goodman, E. D., Xu, Y. & Wu, Y. (2013), Integrating a statistical background-foreground extraction algorithm and SVM classifier for pedestrian detection and tracking, Integrated Computer-Aided Engineering, 20(3), 201-16. – reference: Mosa, A. M., Rahmat, R. A. O. K., Ismail, A. & Taha, M. R. (2013), Expert system to control construction problems in flexible pavements, Computer-Aided Civil and Infrastructure Engineering, 28(4), 307-23. – reference: Lin, D.-Y. & Ku, Y.-H. (2014), Using genetic algorithms to optimize stopping patterns for passenger rail transportation, Computer-Aided Civil and Infrastructure Engineering, 29(4), 264-78. – reference: Nazzal, M. D. & Tatari, O. (2013), Evaluating the use of neural networks and genetic algorithms for prediction of subgrade resilient modulus, International Journal of Pavement Engineering, 14(4), 364-73. – reference: George, K. P. (2004), Prediction of Resilient Modulus from Soil Index Properties, Mississippi Department of Transportation, Jackson, Mississippi. – reference: Siamak, S. G., Hamed, B. J. & Ramezanianpour, A. A. (2012), Hybrid support vector regression - particle swarm optimization for prediction of compressive strength and RCPT of concretes containing metakaolin, Construction and Building Materials, 34, 321-29. – reference: Montalvo, I., Izquierdo, J., Pérez-García, R. & Herrera, M. (2014), Water distribution system computer-aided design by agent swarm optimization, Computer-Aided Civil and Infrastructure Engineering, 29(6), 433-48. – reference: Chou, J.-S. & Le, T.-S. (2011), Reliability-based performance simulation for optimized pavement maintenance, Reliability Engineering & System Safety, 96(10), 1402-10. – reference: Hong, W.-C. (2009), Hybrid evolutionary algorithms in a SVR-based electric load forecasting model, International Journal of Electrical Power & Energy Systems, 31(7-8), 409-17. – reference: Suykens, J. A. K. & Vandewalle, J. (1999), Least squares support vector machine classifiers, Neural Processing Letters, 9(3), 293-300. – reference: Yang, X.-S. (2010), Engineering Optimization: An Introduction with Metaheuristic Applications, John Wiley & Sons, Hoboken, NJ. – reference: Bianchini, A. & Bandini, P. (2010), Prediction of pavement performance through neuro-fuzzy reasoning, Computer-Aided Civil and Infrastructure Engineering, 25(1), 39-54. – reference: Amini, F., Hazaveh, N. K. & Rad, A. A. (2013), Wavelet PSO-based LQR algorithm for optimal structural control using active tuned mass dampers, Computer-Aided Civil and Infrastructure Engineering, 28(7), 542-57. – reference: Yeh, I. C. (1998), Modeling of strength of high-performance concrete using artificial neural networks, Cement and Concrete Research, 28(12), 1797-808. – reference: Bhanja, S. & Sengupta, B. (2002), Investigations on the compressive strength of silica fume concrete using statistical methods, Cement and Concrete Research, 32(9), 1391-94. – reference: Mousavi, S. M., Aminian, P., Gandomi, A. H., Alavi, A. H. & Bolandi, H. (2012), A new predictive model for compressive strength of HPC using gene expression programming, Advances in Engineering Software, 45(1), 105-14. – reference: Yeh, I. C. & Lien, L.-C. (2009), Knowledge discovery of concrete material using genetic operation trees, Expert Systems with Applications, 36(3, Part 2), 5807-12. – reference: Keerthi, S. S. & Lin, C.-J. (2003), Asymptotic behaviors of support vector machines with Gaussian kernel, Neural Computation, 15(7), 1667-89. – reference: Li, Y., Deng, S. & Xiao, D. (2011), A novel hash algorithm construction based on chaotic neural network, Neural Computing and Applications, 20(1), 133-41. – reference: Burdick, A. (2011), Strategy Guideline: Accurate Heating and Cooling Load Calculations, IBACOS, Inc., US Department of Energy, Washington DC. – reference: Shafahi, Y. & Bagherian, M. (2013), A customized particle swarm method to solve highway alignment optimization problem, Computer-Aided Civil and Infrastructure Engineering, 28(1), 52-67. – reference: Gandomi, A. & Alavi, A. (2012), A new multi-gene genetic programming approach to nonlinear system modeling. Part I: materials and structural engineering problems, Neural Computing and Applications, 21(1), 171-87. – reference: Geisel, T., Nierwetberg, J. & Zacherl, A. (1985), Accelerated diffusion in Josephson junctions and related chaotic systems, Physical Review Letters, 54(7), 616-19. – reference: Hong, W.-C., Dong, Y., Chen, L.-Y. & Wei, S.-Y. (2011), SVR with hybrid chaotic genetic algorithms for tourism demand forecasting, Applied Soft Computing, 11(2), 1881-90. – reference: Vapnik, V. N. (1995), The Nature of Statistical Learning Theory, Springer-Verlag, New York. – reference: Tsanas, A. & Xifara, A. (2012), Accurate quantitative estimation of energy performance of residential buildings using statistical machine learning tools, Energy and Buildings, 49, 560-67. – reference: Van-Gestel, T., Suykens, J. K., Baesens, B., Viaene, S., Vanthienen, J., Dedene, G., De-Moor, B. & Vandewalle, J. (2004), Benchmarking least squares support vector machine classifiers, Machine Learning, 54(1), 5-32. – reference: Panakkat, A. & Adeli, H. (2009), Recurrent neural network for approximate earthquake time and location prediction using multiple seismicity indicators, Computer-Aided Civil and Infrastructure Engineering, 24(4), 280-92. – reference: Yang, D., Li, G. & Cheng, G. (2007), On the efficiency of chaos optimization algorithms for global optimization, Chaos, Solitons & Fractals, 34(4), 1366-75. – reference: He, D., He, C., Jiang, L. G., Zhu, H. W. & Hu, G. R. (2001), Chaotic characteristics of a one-dimensional iterative map with infinite collapses, IEEE Transactions on Circuits and Systems I: Fundamental Theory and Applications, 48(7), 900-06. – reference: Samant, A. & Adeli, H. (2001), Enhancing neural network traffic incident-detection algorithms using wavelets, Computer-Aided Civil and Infrastructure Engineering, 16(4), 239-45. – reference: Yang, X.-S. (2008), Firefly Algorithm, Luniver Press, Bristol, UK. – reference: Park, H. I., Kweon, G. C. & Lee, S. R. (2009), Prediction of resilient modulus of granular subgrade soils and subbase materials using artificial neural network, Road Materials and Pavement Design, 10(3), 647-65. – reference: Yeh, I. C. (2008), Modeling slump of concrete with fly ash and superplasticizer, Computers and Concrete, 5(6), 559-72. – reference: Jiang, X. & Adeli, H. (2005), Dynamic wavelet neural network model for traffic flow forecasting, Journal of Transportation Engineering, 131(10), 771-79. – reference: Yang, I. T. & Hsieh, Y.-H. (2013), Reliability-based design optimization with cooperation between support vector machine and particle swarm optimization, Engineering with Computers, 29(2), 151-63. – reference: Chou, J. S., Cheng, M. Y. & Wu, Y. W. (2013), Improving classification accuracy of project dispute resolution using hybrid artificial intelligence and support vector machine models, Expert Systems with Applications, 40(6), 2263-74. – reference: Dong, B., Cao, C. & Lee, S. E. (2005), Applying support vector machines to predict building energy consumption in tropical region, Energy and Buildings, 37(5), 545-53. – reference: Dharia, A. & Adeli, H. (2003), Neural network model for rapid forecasting of freeway link travel time, Engineering Applications of Artificial Intelligence, 16(7-8), 607-13. – reference: Adeli, H. & Panakkat, A. (2009), A probabilistic neural network for earthquake magnitude prediction, Neural Networks, 22(7), 1018-24. – reference: Jie, S., Hui, L. & Adeli, H. (2013), Concept drift-oriented adaptive and dynamic support vector machine ensemble with time window in corporate financial risk prediction, IEEE Transactions on Systems, Man, and Cybernetics: Systems, 43(4), 801-13. – reference: Ekici, B. B. & Aksoy, U. T. (2009), Prediction of building energy consumption by using artificial neural networks, Advances in Engineering Software, 40(5), 356-62. – reference: Liao, S.-H., Chu, P.-H. & Hsiao, P.-Y. (2012), Data mining techniques and applications - a decade review from 2000 to 2011, Expert Systems with Applications, 39(12), 11303-11. – reference: Pal, S. K., Rai, C. S. & Singh, A. P. (2012), Comparative study of firefly algorithm and particle swarm optimization for noisy non-linear optimization problems, International Journal of Intelligent Systems and Applications, 4, 50-57. – reference: Dai, H., Zhang, H., Wang, W. & Xue, G. (2012), Structural reliability assessment by local approximation of limit state functions using adaptive Markov chain simulation and support vector regression, Computer-Aided Civil and Infrastructure Engineering, 27(9), 676-86. – reference: Suykens, J. A. K., Van-Gestel, T., De-Brabanter, J., De-Moor, B. & Vandewalle, J. (2002), Least Squares Support Vector Machines, World Scientific, Singapore City, Singapore. – reference: Al-Homoud, M. S. (2001), Computer-aided building energy analysis techniques, Building and Environment, 36(4), 421-33. – reference: Kris, D. B. (2011), Least Squares Support Vector Regression with Applications to Large-Scale Data: A Statistical Approach, Faculty of Engineering, KU Leuven, Katholieke Universiteit Leuven, Leuven, Belgium. – reference: Verma, A., Wei, X. & Kusiak, A. (2013), Predicting the total suspended solids in wastewater: a data-mining approach, Engineering Applications of Artificial Intelligence, 26(4), 1366-72. – reference: Adeli, H. & Wu, M. (1998), Regularization neural network for construction cost estimation, Journal of Construction Engineering and Management, 124(1), 18-24. – reference: Sarma, K. & Adeli, H. (2000), Fuzzy genetic algorithm for optimization of steel structures, Journal of Structural Engineering, 126(5), 596-604. – reference: Atici, U. (2010), Prediction of the strength of mineral-addition concrete using regression analysis, Magazine of Concrete Research, 62(8), 585-92. – year: 2011 – year: 1985 – volume: 28 start-page: 307 issue: 4 year: 2013 end-page: 23 article-title: Expert system to control construction problems in flexible pavements publication-title: Computer‐Aided Civil and Infrastructure Engineering – volume: 34 start-page: 321 year: 2012 end-page: 29 article-title: Hybrid support vector regression – particle swarm optimization for prediction of compressive strength and RCPT of concretes containing metakaolin publication-title: Construction and Building Materials – volume: 4 start-page: 50 year: 2012 end-page: 57 article-title: Comparative study of firefly algorithm and particle swarm optimization for noisy non‐linear optimization problems publication-title: International Journal of Intelligent Systems and Applications – volume: 1 start-page: 164 issue: 3 year: 2011 end-page: 71 article-title: Clustering using firefly algorithm: performance study publication-title: Swarm and Evolutionary Computation – volume: 20 start-page: 133 issue: 1 year: 2011 end-page: 41 article-title: A novel hash algorithm construction based on chaotic neural network publication-title: Neural Computing and Applications – start-page: 279 year: 2005 end-page: 83 – year: 2005 – volume: 17 start-page: 248 issue: 2 year: 2011 end-page: 58 article-title: Application of new information technology on concrete: an overview publication-title: Journal of Civil Engineering and Management – volume: 96 start-page: 1402 issue: 10 year: 2011 end-page: 10 article-title: Reliability‐based performance simulation for optimized pavement maintenance publication-title: Reliability Engineering & System Safety – volume: 28 start-page: 52 issue: 1 year: 2013 end-page: 67 article-title: A customized particle swarm method to solve highway alignment optimization problem publication-title: Computer‐Aided Civil and Infrastructure Engineering – volume: 39 start-page: 11303 issue: 12 year: 2012 end-page: 11 article-title: Data mining techniques and applications – a decade review from 2000 to 2011 publication-title: Expert Systems with Applications – volume: 49 start-page: 560 year: 2012 end-page: 67 article-title: Accurate quantitative estimation of energy performance of residential buildings using statistical machine learning tools publication-title: Energy and Buildings – start-page: 1 year: 2013 end-page: 6 – volume: 28 start-page: 542 issue: 7 year: 2013 end-page: 57 article-title: Wavelet PSO‐based LQR algorithm for optimal structural control using active tuned mass dampers publication-title: Computer‐Aided Civil and Infrastructure Engineering – volume: 18 start-page: 89 issue: 1 year: 2013 end-page: 98 article-title: Firefly algorithm with chaos publication-title: Communications in Nonlinear Science and Numerical Simulation – volume: 54 start-page: 5 issue: 1 year: 2004 end-page: 32 article-title: Benchmarking least squares support vector machine classifiers publication-title: Machine Learning – volume: 54 start-page: 616 issue: 7 year: 1985 end-page: 19 article-title: Accelerated diffusion in Josephson junctions and related chaotic systems publication-title: Physical Review Letters – volume: 14 start-page: 364 issue: 4 year: 2013 end-page: 73 article-title: Evaluating the use of neural networks and genetic algorithms for prediction of subgrade resilient modulus publication-title: International Journal of Pavement Engineering – volume: 14 start-page: 23 issue: 1 year: 2001 end-page: 35 article-title: Optimal control by least squares support vector machines publication-title: Neural Networks – volume: 20 start-page: 111 issue: 2 year: 2013 end-page: 26 article-title: Adaptive fuzzy control for differentially flat MIMO nonlinear dynamical systems publication-title: Integrated Computer‐Aided Engineering – start-page: 53 year: 2011 end-page: 66 – volume: 29 start-page: 151 issue: 2 year: 2013 end-page: 63 article-title: Reliability‐based design optimization with cooperation between support vector machine and particle swarm optimization publication-title: Engineering with Computers – volume: 21 start-page: 171 issue: 1 year: 2012 end-page: 87 article-title: A new multi‐gene genetic programming approach to nonlinear system modeling. Part I: materials and structural engineering problems publication-title: Neural Computing and Applications – volume: 46 start-page: 291 issue: 1–3 year: 2002 end-page: 314 article-title: A simple decomposition method for support vector machines publication-title: Machine Learning – year: 2008 – volume: 40 start-page: 356 issue: 5 year: 2009 end-page: 62 article-title: Prediction of building energy consumption by using artificial neural networks publication-title: Advances in Engineering Software – start-page: 1137 year: 1995 end-page: 43 – year: 2004 – volume: 20 start-page: 201 issue: 3 year: 2013 end-page: 16 article-title: Integrating a statistical background‐foreground extraction algorithm and SVM classifier for pedestrian detection and tracking publication-title: Integrated Computer‐Aided Engineering – volume: 49 start-page: 591 year: 2012 end-page: 603 article-title: Predicting future hourly residential electrical consumption: a machine learning case study publication-title: Energy and Buildings – volume: 16 start-page: 3559 issue: 6 year: 2012 end-page: 73 article-title: Energy efficient design of building: a review publication-title: Renewable and Sustainable Energy Reviews – volume: 131 start-page: 771 issue: 10 year: 2005 end-page: 79 article-title: Dynamic wavelet neural network model for traffic flow forecasting publication-title: Journal of Transportation Engineering – volume: 15 start-page: 1667 issue: 7 year: 2003 end-page: 89 article-title: Asymptotic behaviors of support vector machines with Gaussian kernel publication-title: Neural Computation – volume: 29 start-page: 433 issue: 6 year: 2014 end-page: 48 article-title: Water distribution system computer‐aided design by agent swarm optimization publication-title: Computer‐Aided Civil and Infrastructure Engineering – volume: 48 start-page: 900 issue: 7 year: 2001 end-page: 06 article-title: Chaotic characteristics of a one‐dimensional iterative map with infinite collapses publication-title: IEEE Transactions on Circuits and Systems I: Fundamental Theory and Applications – year: 1993 – volume: 75 start-page: 770 issue: 7 year: 2008 end-page: 86 article-title: Neuro‐genetic algorithm for non‐linear active control of structures publication-title: International Journal for Numerical Methods in Engineering – volume: 29 start-page: 264 issue: 4 year: 2014 end-page: 78 article-title: Using genetic algorithms to optimize stopping patterns for passenger rail transportation publication-title: Computer‐Aided Civil and Infrastructure Engineering – volume: 32 start-page: 1391 issue: 9 year: 2002 end-page: 94 article-title: Investigations on the compressive strength of silica fume concrete using statistical methods publication-title: Cement and Concrete Research – volume: 40 start-page: 2263 issue: 6 year: 2013 end-page: 74 article-title: Improving classification accuracy of project dispute resolution using hybrid artificial intelligence and support vector machine models publication-title: Expert Systems with Applications – volume: 86 start-page: 2249 issue: 10 year: 2009 end-page: 56 article-title: Applying support vector machine to predict hourly cooling load in the building publication-title: Applied Energy – volume: 5 start-page: 559 issue: 6 year: 2008 end-page: 72 article-title: Modeling slump of concrete with fly ash and superplasticizer publication-title: Computers and Concrete – volume: 45 start-page: 105 issue: 1 year: 2012 end-page: 14 article-title: A new predictive model for compressive strength of HPC using gene expression programming publication-title: Advances in Engineering Software – volume: 36 start-page: 5807 issue: 3 year: 2009 end-page: 12 article-title: Knowledge discovery of concrete material using genetic operation trees publication-title: Expert Systems with Applications – volume: 126 start-page: 596 issue: 5 year: 2000 end-page: 604 article-title: Fuzzy genetic algorithm for optimization of steel structures publication-title: Journal of Structural Engineering – volume: 9 start-page: 293 issue: 3 year: 1999 end-page: 300 article-title: Least squares support vector machine classifiers publication-title: Neural Processing Letters – volume: 28 start-page: 1797 issue: 12 year: 1998 end-page: 808 article-title: Modeling of strength of high‐performance concrete using artificial neural networks publication-title: Cement and Concrete Research – year: 2007 – volume: 37 start-page: 545 issue: 5 year: 2005 end-page: 53 article-title: Applying support vector machines to predict building energy consumption in tropical region publication-title: Energy and Buildings – start-page: 269 year: 2009 end-page: 304 – volume: 31 start-page: 409 issue: 7–8 year: 2009 end-page: 17 article-title: Hybrid evolutionary algorithms in a SVR‐based electric load forecasting model publication-title: International Journal of Electrical Power & Energy Systems – volume: 36 start-page: 421 issue: 4 year: 2001 end-page: 33 article-title: Computer‐aided building energy analysis techniques publication-title: Building and Environment – volume: 6 start-page: 163 issue: 3 year: 2005 end-page: 69 article-title: Subgrade soil index properties to estimate resilient modulus for pavement design publication-title: International Journal of Pavement Engineering – volume: 11 start-page: 1881 issue: 2 year: 2011 end-page: 90 article-title: SVR with hybrid chaotic genetic algorithms for tourism demand forecasting publication-title: Applied Soft Computing – volume: 124 start-page: 18 issue: 1 year: 1998 end-page: 24 article-title: Regularization neural network for construction cost estimation publication-title: Journal of Construction Engineering and Management – volume: 25 start-page: 39 issue: 1 year: 2010 end-page: 54 article-title: Prediction of pavement performance through neuro‐fuzzy reasoning publication-title: Computer‐Aided Civil and Infrastructure Engineering – volume: 49 start-page: 554 year: 2013 end-page: 63 article-title: Enhanced artificial intelligence for ensemble approach to predicting high performance concrete compressive strength publication-title: Construction and Building Materials – volume: 62 start-page: 585 issue: 8 year: 2010 end-page: 92 article-title: Prediction of the strength of mineral‐addition concrete using regression analysis publication-title: Magazine of Concrete Research – volume: 59 start-page: 273 year: 2013 end-page: 78 article-title: Improved firefly algorithm approach applied to chiller loading for energy conservation publication-title: Energy and Buildings – volume: 22 start-page: 1018 issue: 7 year: 2009 end-page: 24 article-title: A probabilistic neural network for earthquake magnitude prediction publication-title: Neural Networks – year: 2010 – start-page: 1 year: 2009 end-page: 4 – start-page: 210 year: 2009 end-page: 14 – year: 2002 – volume: 16 start-page: 607 issue: 7–8 year: 2003 end-page: 13 article-title: Neural network model for rapid forecasting of freeway link travel time publication-title: Engineering Applications of Artificial Intelligence – volume: 43 start-page: 801 issue: 4 year: 2013 end-page: 13 article-title: Concept drift‐oriented adaptive and dynamic support vector machine ensemble with time window in corporate financial risk prediction publication-title: IEEE Transactions on Systems, Man, and Cybernetics: Systems – volume: 34 start-page: 1366 issue: 4 year: 2007 end-page: 75 article-title: On the efficiency of chaos optimization algorithms for global optimization publication-title: Chaos, Solitons & Fractals – volume: 10 start-page: 647 issue: 3 year: 2009 end-page: 65 article-title: Prediction of resilient modulus of granular subgrade soils and subbase materials using artificial neural network publication-title: Road Materials and Pavement Design – volume: 27 start-page: 676 issue: 9 year: 2012 end-page: 86 article-title: Structural reliability assessment by local approximation of limit state functions using adaptive Markov chain simulation and support vector regression publication-title: Computer‐Aided Civil and Infrastructure Engineering – year: 1995 – volume: 16 start-page: 126 issue: 2 year: 2001 end-page: 42 article-title: Neural networks in civil engineering: 1989–2000 publication-title: Computer‐Aided Civil and Infrastructure Engineering – volume: 16 start-page: 239 issue: 4 year: 2001 end-page: 45 article-title: Enhancing neural network traffic incident‐detection algorithms using wavelets publication-title: Computer‐Aided Civil and Infrastructure Engineering – volume: 26 start-page: 1366 issue: 4 year: 2013 end-page: 72 article-title: Predicting the total suspended solids in wastewater: a data‐mining approach publication-title: Engineering Applications of Artificial Intelligence – volume: 27 start-page: 764 issue: 10 year: 2012 end-page: 81 article-title: Neuro‐fuzzy cost estimation model enhanced by fast messy genetic algorithms for semiconductor hookup construction publication-title: Computer‐Aided Civil and Infrastructure Engineering – volume: 24 start-page: 280 issue: 4 year: 2009 end-page: 92 article-title: Recurrent neural network for approximate earthquake time and location prediction using multiple seismicity indicators publication-title: Computer‐Aided Civil and Infrastructure Engineering – year: 2013 – ident: e_1_2_7_21_1 doi: 10.1016/j.enbuild.2004.09.009 – ident: e_1_2_7_59_1 doi: 10.1061/(ASCE)0733-9445(2000)126:5(596) – ident: e_1_2_7_46_1 doi: 10.1111/mice.12062 – ident: e_1_2_7_6_1 doi: 10.1111/mice.12017 – ident: e_1_2_7_35_1 doi: 10.1061/(ASCE)0733-947X(2005)131:10(771) – volume: 20 start-page: 201 issue: 3 year: 2013 ident: e_1_2_7_41_1 article-title: Integrating a statistical background‐foreground extraction algorithm and SVM classifier for pedestrian detection and tracking publication-title: Integrated Computer‐Aided Engineering doi: 10.3233/ICA-130428 – ident: e_1_2_7_76_1 doi: 10.1002/9780470640425 – ident: e_1_2_7_70_1 doi: 10.1007/978-1-4757-2440-0 – ident: e_1_2_7_12_1 doi: 10.3846/13923730.2011.574343 – volume-title: Pavement Analysis and Design year: 1993 ident: e_1_2_7_34_1 – ident: e_1_2_7_80_1 doi: 10.12989/cac.2008.5.6.559 – ident: e_1_2_7_38_1 doi: 10.1162/089976603321891855 – ident: e_1_2_7_39_1 – ident: e_1_2_7_60_1 doi: 10.1016/j.swevo.2011.06.003 – ident: e_1_2_7_37_1 doi: 10.1109/TSMCA.2012.2224338 – ident: e_1_2_7_33_1 doi: 10.1023/A:1012427100071 – ident: e_1_2_7_36_1 doi: 10.1002/nme.2274 – volume-title: Technical Note, Manufacturing Engineering Centre year: 2005 ident: e_1_2_7_55_1 – ident: e_1_2_7_22_1 doi: 10.1016/j.enbuild.2012.03.010 – ident: e_1_2_7_69_1 doi: 10.1023/B:MACH.0000008082.80494.e0 – ident: e_1_2_7_53_1 doi: 10.1111/j.1467-8667.2009.00595.x – ident: e_1_2_7_45_1 doi: 10.1111/mice.12020 – ident: e_1_2_7_23_1 doi: 10.1016/j.advengsoft.2008.05.003 – ident: e_1_2_7_65_1 doi: 10.1023/A:1018628609742 – ident: e_1_2_7_58_1 doi: 10.1111/0885-9507.00229 – ident: e_1_2_7_18_1 doi: 10.1016/j.enbuild.2012.11.030 – ident: e_1_2_7_31_1 doi: 10.1109/IWISA.2009.5072707 – ident: e_1_2_7_5_1 doi: 10.1016/S0360-1323(00)00026-3 – volume-title: Prediction of Resilient Modulus from Soil Index Properties year: 2004 ident: e_1_2_7_27_1 – ident: e_1_2_7_52_1 doi: 10.5815/ijisa.2012.10.06 – ident: e_1_2_7_9_1 doi: 10.1680/macr.2010.62.8.585 – ident: e_1_2_7_32_1 doi: 10.1111/j.1467-8667.2012.00786.x – ident: e_1_2_7_47_1 doi: 10.1111/mice.12001 – ident: e_1_2_7_11_1 doi: 10.1111/j.1467-8667.2009.00615.x – ident: e_1_2_7_66_1 doi: 10.1016/S0893-6080(00)00077-0 – ident: e_1_2_7_49_1 doi: 10.1080/10298436.2012.671944 – ident: e_1_2_7_50_1 doi: 10.1109/EPDC.2013.6565962 – ident: e_1_2_7_77_1 doi: 10.1109/NABIC.2009.5393690 – ident: e_1_2_7_20_1 doi: 10.1016/j.engappai.2003.09.011 – ident: e_1_2_7_15_1 doi: 10.1016/j.eswa.2012.10.036 – ident: e_1_2_7_44_1 doi: 10.1016/j.eswa.2012.02.063 – ident: e_1_2_7_71_1 doi: 10.1016/j.engappai.2012.08.015 – ident: e_1_2_7_43_1 doi: 10.1007/s00521-010-0432-2 – ident: e_1_2_7_4_1 doi: 10.1061/(ASCE)0733-9364(1998)124:1(18) – volume-title: Uci Machine Learning Repository year: 2007 ident: e_1_2_7_8_1 – ident: e_1_2_7_51_1 doi: 10.1016/j.rser.2012.03.045 – ident: e_1_2_7_62_1 doi: 10.1016/j.conbuildmat.2012.02.038 – volume-title: Transportation Research Record year: 1985 ident: e_1_2_7_14_1 – volume: 20 start-page: 111 issue: 2 year: 2013 ident: e_1_2_7_57_1 article-title: Adaptive fuzzy control for differentially flat MIMO nonlinear dynamical systems publication-title: Integrated Computer‐Aided Engineering doi: 10.3233/ICA-130421 – ident: e_1_2_7_24_1 doi: 10.1007/s00521-011-0734-z – volume-title: Mechanistic‐Empirical Pavement Design Guide: A Manual of Practice year: 2008 ident: e_1_2_7_7_1 – ident: e_1_2_7_79_1 doi: 10.1016/S0008-8846(98)00165-3 – ident: e_1_2_7_74_1 doi: 10.1007/s00366-011-0251-9 – ident: e_1_2_7_42_1 doi: 10.1016/j.apenergy.2008.11.035 – ident: e_1_2_7_68_1 doi: 10.1016/j.enbuild.2012.03.003 – ident: e_1_2_7_10_1 doi: 10.1016/S0008-8846(02)00787-1 – ident: e_1_2_7_81_1 doi: 10.1016/j.eswa.2008.07.004 – ident: e_1_2_7_73_1 doi: 10.1016/j.chaos.2006.04.057 – volume-title: Firefly Algorithm year: 2008 ident: e_1_2_7_75_1 – ident: e_1_2_7_56_1 doi: 10.1080/10298430500140891 – ident: e_1_2_7_13_1 doi: 10.2172/1018100 – ident: e_1_2_7_16_1 doi: 10.1016/j.ress.2011.05.005 – ident: e_1_2_7_61_1 doi: 10.1111/j.1467-8667.2012.00769.x – ident: e_1_2_7_26_1 doi: 10.1103/PhysRevLett.54.616 – ident: e_1_2_7_54_1 doi: 10.1080/14680629.2009.9690218 – ident: e_1_2_7_67_1 doi: 10.1142/5089 – ident: e_1_2_7_17_1 doi: 10.1016/j.conbuildmat.2013.08.078 – ident: e_1_2_7_28_1 doi: 10.1109/81.933333 – volume-title: Least Squares Support Vector Regression with Applications to Large‐Scale Data: A Statistical Approach year: 2011 ident: e_1_2_7_40_1 – ident: e_1_2_7_64_1 – ident: e_1_2_7_72_1 – ident: e_1_2_7_25_1 doi: 10.1016/j.cnsns.2012.06.009 – ident: e_1_2_7_63_1 doi: 10.1007/978-3-642-04586-8_10 – ident: e_1_2_7_3_1 doi: 10.1016/j.neunet.2009.05.003 – ident: e_1_2_7_2_1 doi: 10.1111/0885-9507.00219 – ident: e_1_2_7_30_1 doi: 10.1016/j.asoc.2010.06.003 – ident: e_1_2_7_19_1 doi: 10.1111/j.1467-8667.2012.00767.x – ident: e_1_2_7_29_1 doi: 10.1016/j.ijepes.2009.03.020 – ident: e_1_2_7_78_1 doi: 10.1007/978-3-642-22185-9_6 – ident: e_1_2_7_48_1 doi: 10.1016/j.advengsoft.2011.09.014 |
| SSID | ssj0000443 |
| Score | 2.4353633 |
| Snippet | Advanced data mining techniques are potential tools for solving civil engineering (CE) problems. This study proposes a novel smart artificial firefly colony... |
| SourceID | crossref wiley istex |
| SourceType | Enrichment Source Index Database Publisher |
| StartPage | 715 |
| Title | Smart Artificial Firefly Colony Algorithm-Based Support Vector Regression for Enhanced Forecasting in Civil Engineering |
| URI | https://api.istex.fr/ark:/67375/WNG-GG8R34Q3-9/fulltext.pdf https://onlinelibrary.wiley.com/doi/abs/10.1111%2Fmice.12121 |
| Volume | 30 |
| WOSCitedRecordID | wos000358692800004&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| journalDatabaseRights | – providerCode: PRVWIB databaseName: Wiley Online Library Full Collection 2020 customDbUrl: eissn: 1467-8667 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0000443 issn: 1093-9687 databaseCode: DRFUL dateStart: 19970101 isFulltext: true titleUrlDefault: https://onlinelibrary.wiley.com providerName: Wiley-Blackwell |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpV1JS8NAFH5I60EP7mLdGFAEhUDSydKAF61tPUjRut7CbNVgjaWL2Js_wd_oL_HNJKkVRBBvYXiZhJm3fDPz5nsAuxhFGMU4YvF22bNcxMwWC8vCklxvECoM-IZK6eYsaDYrd3fh-RQc5ndhUn6I8Yabtgzjr7WBM96fMHJdrV1zI-hb5MUyKq5XgOJJq3599uWJ3SzBPqRW6FeCjJ5UZ_J8vf0tIBX12L5-B6om0tTn__ePCzCXIUxylKrEIkypZAnmM7RJMlvuY1Ne0CFvW4LZCXbCZRhdPqFemY5SmglSR__Y7oxIFT1mMiJHnfvnXjx4ePp4ez_GaIi9D7saz5MbcxZAWuo-TbNNCGJjUkseTL4B0fVABevrjGsSJ6Qav8QdMvHtFbiu166qp1ZWqcESGPEdS4nAZg6zKWNMuQqXVB4PbeFKm9sSl2gIK7lEt8u5zx2bcsoVFbbPhS-VEpzTVSgkz4laA-I4whGerHhStV3mBxrASikUQpcy84VXgv18uiKR0ZjrahqdKF_O6EGPzKCXYGcs203JO36U2jOzPhZhvUed7hZ40W2zETUalRZ1L2gUluDATPYvfUVoOzXztP4X4Q2YQSDmpblrm1AY9IZqC6bFyyDu97Yztf4EG579bg |
| linkProvider | Wiley-Blackwell |
| linkToHtml | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpV1bSxtBFD4UU6g-aL2UxtZ2oEVoYWE3s5fso41JLKahjZf6tswtuhhXSaI0b_0J_Y3-Es-ZncQIRRDflmF2dpk5l29mv_0OwGfMIoJjHvFkvxZ5IWJmT6Q15WlJB4QGE76VUjruJN1u_eQk_em4OfQvTKkPMTtwI8-w8ZocnA6k57ycyrWTOAL9Rl4J0Y7QwCu7vdZR5z4Uh45hn3IvjeuJ0yclKs_93Q8yUoUm989DpGpTTWvlmS_5GpYdxmQ7pVGswgtTrMGKw5vMefMIm6YlHaZta7A0p0-4DpODC7QsO1ApNMFaGCH7gwlrYMwsJmxncHo5zMdnF7d__33DfIijX18RomfH9msA65nTkmhbMETHrFmcWcYBo4qgSoyIc83ygjXym3zA5p69AUet5mFjz3O1GjyFOT_wjEp8EQifCyFMaHBTFcnUV6H2pa9xk4bAUmoMvFLGMvC55NJw5cdSxdoYJSV_AwvFZWHeAgsCFahI1yNt-qGIE4KwWiuD4KUmYhVV4ct0vTLlhMypnsYgm25oaNIzO-lV-DTre1XKd_y317Zd9lkXMTwnwlsSZb-77azdrvd4-ItnaRW-2tV-ZKwMvadprzaf0vkjvNo7_NHJOt-7--9gEWFZVDLZ3sPCeHhttuCluhnno-EHZ-N3Pu4BbQ |
| linkToPdf | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpV1daxNBFL1IIqIPra0W01odUASFhd3MfmQfa5qN0hBqtLVvy3ylXZpuQ5KW5q0_wd_oL-m9s5M0BRHEt2W4O7vMzL33zOzZcwHeYxYRHPOIJ4fNyAsRM3sibSpPSzogNJjwrZTScS_p91snJ-mh4-bQvzCVPsTywI08w8ZrcnAz1sMVL6dy7SSOQL-R10OqIlOD-v4gO-rdh-LQMexT7qVxK3H6pETlub_7QUaq0-DePESqNtVk6__5ks9hzWFMtlctig14ZMpNWHd4kzlvnmLToqTDom0Tnq3oE76A-fcLXFm2o0pogmUYIYejOWtjzCznbG90ejkpZmcXv29_fcZ8iL1fjQnRs2P7NYANzGlFtC0ZomPWKc8s44BRRVAlpsS5ZkXJ2sV1MWIrz34JR1nnR_uL52o1eApzfuAZlfgiED4XQpjQ4KYqkqmvQu1LX-MmDYGl1Bh4pYxl4HPJpeHKj6WKtTFKSr4FtfKyNK-ABYEKVKRbkTbDUMQJQVitlUHw0hSxihrwcTFfuXJC5lRPY5QvNjQ06Lkd9Aa8W9qOK_mOP1p9sNO-NBGTcyK8JVH-s9_Nu93WgIffeJ424JOd7b_0laP3dOzV9r8Yv4Unh_tZ3vvaP9iBp4jKoorI9hpqs8mV2YXH6npWTCdv3BK_A8iQAOg |
| openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Smart+Artificial+Firefly+Colony+Algorithm%E2%80%90Based+Support+Vector+Regression+for+Enhanced+Forecasting+in+Civil+Engineering&rft.jtitle=Computer-aided+civil+and+infrastructure+engineering&rft.au=Chou%2C+Jui%E2%80%90Sheng&rft.au=Pham%2C+Anh%E2%80%90Duc&rft.date=2015-09-01&rft.issn=1093-9687&rft.eissn=1467-8667&rft.volume=30&rft.issue=9&rft.spage=715&rft.epage=732&rft_id=info:doi/10.1111%2Fmice.12121&rft.externalDBID=10.1111%252Fmice.12121&rft.externalDocID=MICE12121 |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=1093-9687&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=1093-9687&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=1093-9687&client=summon |