An engineer's guide to eXplainable Artificial Intelligence and Interpretable Machine Learning: Navigating causality, forced goodness, and the false perception of inference
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Prager, 1964, Problems of optimal structural design, J. Appl. Mech. Trans., 35, 102, 10.1115/1.3601120
Rutherford, 2002
Antoy, 2014
The Economist
Bogue, 2018, What are the prospects for robots in the construction industry?, Ind. Robot., 45, 1, 10.1108/IR-11-2017-0194
Jordan, 2015, Machine learning: trends, perspectives, and prospects, Science, 349, 255, 10.1126/science.aaa8415
Taffese, 2017, Machine learning for durability and service-life assessment of reinforced concrete structures: recent advances and future directions, Autom. Constr., 77, 1, 10.1016/j.autcon.2017.01.016
Dung, 2019, Autonomous concrete crack detection using deep fully convolutional neural network, Autom. Constr., 99, 52, 10.1016/j.autcon.2018.11.028
Valero, 2019, Automated defect detection and classification in ashlar masonry walls using machine learning, Autom. Constr., 106, 10.1016/j.autcon.2019.102846
Witten, 2011
Naser, 2020, AI modelling & mapping functions: a cognitive, physics- guided, simulation-free and instantaneous approach to fire evaluation, 590
Alavi, 2010, Multi expression programming: a new approach to formulation of soil classification, Eng. Comput., 26, 111, 10.1007/s00366-009-0140-7
Seitlllari, 2019, Leveraging artificial intelligence to assess explosive spalling in fire-exposed RC columns, Comput. Concr., 24, 271
Solhmirzaei, 2020, Machine learning framework for predicting failure mode and shear capacity of ultra high performance concrete beams, Eng. Struct., 224, 10.1016/j.engstruct.2020.111221
Huang, 2019, Classification of in-plane failure modes for reinforced concrete frames with infills using machine learning, J. Build. Eng., 25
Dexters, 2019, Testing for knowledge: maximising information obtained from fire tests by using machine learning techniques
Kaveh, 2019, Optimum design of three-dimensional steel frames with prismatic and non-prismatic elements, Eng. Comput., 36, 1011, 10.1007/s00366-019-00746-9
Tjoa
Dosilovic, 2018, Explainable artificial intelligence: a survey
Gilpin, 2019, Explaining explanations: an overview of interpretability of machine learning
Rudin, 2019, Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead, Nat. Mach. Intell., 1, 206, 10.1038/s42256-019-0048-x
Boehmke, 2020, Interpretable machine learning
The Royal Society, 2019
D’Amico, 2019, Machine learning for sustainable structures: a call for data, Structures, 19, 1, 10.1016/j.istruc.2018.11.013
Zhu, 2015, Machine teaching: an inverse problem to machine learning and an approach toward optimal education, 4083
Behnood, 2020, Machine learning study of the mechanical properties of concretes containing waste foundry sand, Constr. Build. Mater., 243, 10.1016/j.conbuildmat.2020.118152
Behnood, 2021, Predicting the dynamic modulus of asphalt mixture using machine learning techniques: an application of multi biogeography-based programming, Constr. Build. Mater., 266, 10.1016/j.conbuildmat.2020.120983
Mangalathu, 2020, Data-driven machine-learning-based seismic failure mode identification of reinforced concrete shear walls, Eng. Struct., 208, 10.1016/j.engstruct.2020.110331
Mangalathu, 2019, Rapid seismic damage evaluation of bridge portfolios using machine learning techniques, Eng. Struct., 201, 10.1016/j.engstruct.2019.109785
Naser, 2019, Heuristic machine cognition to predict fire-induced spalling and fire resistance of concrete structures, Autom. Constr., 106, 10.1016/j.autcon.2019.102916
Naser, 2020, Properties and material models for construction materials post exposure to elevated temperatures, Mech. Mater., 142, 10.1016/j.mechmat.2019.103293
Siam, 2019, Machine learning algorithms for structural performance classifications and predictions: application to reinforced masonry shear walls, Structures, 22, 10.1016/j.istruc.2019.06.017
Mangalathu, 2018, Artificial neural network based multi-dimensional fragility development of skewed concrete bridge classes, Eng. Struct., 162, 166, 10.1016/j.engstruct.2018.01.053
Alwanas, 2019, Load-carrying capacity and mode failure simulation of beam-column joint connection: application of self-tuning machine learning model, Eng. Struct., 194, 220, 10.1016/j.engstruct.2019.05.048
Inkoom, 2019, Prediction of the crack condition of highway pavements using machine learning models, Struct. Infrastruct. Eng., 15, 940, 10.1080/15732479.2019.1581230
Rafiei, 2017, A novel machine learning-based algorithm to detect damage in high-rise building structures, Struct. Des. Tall Spec. Build., 26, 1, 10.1002/tal.1400
Ai, 2012, Feature extraction based on contourlet transform and its application to surface inspection of metals, Opt. Eng., 51, 1, 10.1117/1.OE.51.11.113605
Park, 2016, Machine learning-based imaging system for surface defect inspection, Int. J. Precis. Eng. Manuf. Green Technol., 3, 303, 10.1007/s40684-016-0039-x
Bobadilha, 2020, Artificial neural networks modelling based on visual analysis of coated cross laminated timber (CLT) to predict color change during outdoor exposure, Holzforschung, 73
Kim, 2019, Crack and noncrack classification from concrete surface images using machine learning, Struct. Health Monit., 18, 725, 10.1177/1475921718768747
Zhang, 2019, High cycle fatigue life prediction of laser additive manufactured stainless steel: a machine learning approach, Int. J. Fatigue, 128, 10.1016/j.ijfatigue.2019.105194
Lu, 2017, Fatigue reliability assessment of welded steel bridge decks under stochastic truck loads via machine learning, J. Bridg. Eng., 22, 1, 10.1061/(ASCE)BE.1943-5592.0000982
Ben Chaabene, 2020, Machine learning prediction of mechanical properties of concrete: critical review, Constr. Build. Mater., 260, 10.1016/j.conbuildmat.2020.119889
Adadi, 2018, Peeking inside the black-box: a survey on explainable artificial intelligence (XAI), IEEE Access, 6, 52138, 10.1109/ACCESS.2018.2870052
Morgan, 2020, Opportunities and challenges for machine learning in materials science, Annu. Rev. Mater. Res., 50, 71, 10.1146/annurev-matsci-070218-010015
Molnar, 2019, 320
Liem, 2018
Ribeiro, 2016, “Why should i trust you?” Explaining the predictions of any classifier, 1135
Lundberg, 2017, A unified approach to interpreting model predictions
Hastie, 2017, Generalized linear models
Chen, 2016, XGBoost: A scalable tree boosting system, 785
Lyons, 2016, Engineering trust in complex automated systems, Ergon. Des., 24, 13
Heyman, 1999
Cohen, 2018
Ross, 2016, Enabling adaptable buildings: results of a preliminary expert survey, Vol. 145, 420
Adams, 2017, Circular economy in construction: current awareness, challenges and enablers, Vol. 170, 15
Miller, 2019, Explanation in artificial intelligence: insights from the social sciences, Artif. Intell., 267, 1, 10.1016/j.artint.2018.07.007
Altmann, 2010, Permutation importance: a corrected feature importance measure, Bioinformatics, 26, 1340, 10.1093/bioinformatics/btq134
Friedman, 2001, Greedy function approximation: a gradient boosting machine, Ann. Stat., 29, 1189, 10.1214/aos/1013203451
Goldstein, 2015, Peeking inside the black box: visualizing statistical learning with plots of individual conditional expectation, J. Comput. Graph. Stat., 24, 44, 10.1080/10618600.2014.907095
Goyal
Chandrashekar, 2014, A survey on feature selection methods, Comput. Electr. Eng., 40, 16, 10.1016/j.compeleceng.2013.11.024
Lundberg
Nyshadham, 2019, Machine-learned multi-system surrogate models for materials prediction, npj Comput. Mater., 5, 10.1038/s41524-019-0189-9
Gorissen, 2010, A surrogate modeling and adaptive sampling toolbox for computer based design, J. Mach. Learn. Res., 11, 2051
Naser, 2020, Concrete under fire: an assessment through intelligent pattern recognition, Eng. Comput., 36, 1915, 10.1007/s00366-019-00805-1
Naser, 2021, Observational analysis of fire-induced spalling of concrete through ensemble machine learning and surrogate modeling, J. Mater. Civ. Eng., 33, 1, 10.1061/(ASCE)MT.1943-5533.0003525
Naser, 2020, Machine learning assessment of fiber-reinforced polymer-strengthened and reinforced concrete members, ACI Struct. J., 117, 237
Alagusundaramoorthy, 2003, Flexural behavior of R/C beams strengthened with carbon fiber reinforced polymer sheets or fabric, J. Compos. Constr., 7, 292, 10.1061/(ASCE)1090-0268(2003)7:4(292)
Almusallam, 2001, Ultimate strength prediction for RC beams externally strengthened by composite materials, Compos. B Eng., 32, 609, 10.1016/S1359-8368(01)00008-7
Rahimi, 2001, Concrete beams strengthened with externally bonded FRP plates, J. Compos. Constr., 5, 44, 10.1061/(ASCE)1090-0268(2001)5:1(44)
Hawileh, 2013, Finite element simulation of reinforced concrete beams externally strengthened with short-length CFRP plates, Compos. B Eng., 45, 1722, 10.1016/j.compositesb.2012.09.032
Al-Tamimi, 2011, Effects of ratio of CFRP plate length to shear span and end anchorage on flexural behavior of SCC RC beams, J. Compos. Constr., 15, 908, 10.1061/(ASCE)CC.1943-5614.0000221
Sabau, 2018, Strengthening of RC beams using bottom and side NSM reinforcement, Compos. Part B, 149, 82, 10.1016/j.compositesb.2018.05.011
Sharaky, 2014, Flexural response of reinforced concrete (RC) beams strengthened with near surface mounted (NSM) fibre reinforced polymer (FRP) bars, Compos. Struct., 109, 8, 10.1016/j.compstruct.2013.10.051
Teng, 2006, Debonding failures of RC beams strengthened with near surface mounted CFRP strips, J. Compos. Constr., 10, 92, 10.1061/(ASCE)1090-0268(2006)10:2(92)
Triantafillou, 1992, Strengthening of RC beams with epoxy-bonded fibre-composite materials, Mater. Struct., 25, 201, 10.1007/BF02473064
Wenwei, 2006, Experimental study and analysis of RC beams strengthened with CFRP laminates under sustaining load, Int. J. Solids Struct., 43, 1372, 10.1016/j.ijsolstr.2005.03.076
Arduini, 1997, Brittle failure in FRP plate and sheet bonded beams, ACI Struct. J., 43, 363
Ceroni, 2010, Experimental performances of RC beams strengthened with FRP materials, Constr. Build. Mater., 24, 1547, 10.1016/j.conbuildmat.2010.03.008
Kuntal, 2017, Efficient near surface mounted CFRP shear strengthening of high strength prestressed concrete beams – an experimental study, Compos. Struct., 180, 16, 10.1016/j.compstruct.2017.07.095
Daghash, 2017, Flexural performance evaluation of NSM basalt FRP-strengthened concrete beams using digital image correlation system, Compos. Struct., 176, 748, 10.1016/j.compstruct.2017.06.021
Dias, 2018, Behavior of RC beams flexurally strengthened with NSM CFRP laminates, Compos. Struct., 201, 363, 10.1016/j.compstruct.2018.05.126
Fanning, 2001, Ultimate response of RC beams strengthened with CFRP plates, J. Compos. Constr., 5, 122, 10.1061/(ASCE)1090-0268(2001)5:2(122)
Al-Mahmoud, 2010, RC beams strengthened with NSM CFRP rods and modeling of peeling-off failure, Compos. Struct., 92, 1920, 10.1016/j.compstruct.2010.01.002
Kotynia, 2007, Analysis of the flexural response of NSM FRP-strengthened concrete beams, 16
Canadian Standards Association (CSA)
BRI, 1997
ACI committee 440, 2015
CNRDT-203
Ketkar, 2017, Introduction to Keras
Freund, 1997, A decision-theoretic generalization of on-line learning and an application to boosting, J. Comput. Syst. Sci., 55, 119, 10.1006/jcss.1997.1504
Gradient boosted tree (GBT)
Scikit, sklearn.ensemble
XGBoost Python Package
Freund, 1996, Experiments with a new boosting algorithm
Naser, 2021, Mechanistically informed machine learning and artificial intelligence in fire engineering and sciences, Fire. Technol, 1
Ke, 2017, LightGBM: a highly efficient gradient boosting decision tree, 3149
LightGBM
Li, 2018, Visualizing the loss landscape of neural nets
Keras
Schmidt, 2010, Age-fitness pareto optimization, 543
Cremonesi, 2010, Performance of recommender algorithms on Top-N recommendation tasks categories and subject descriptors, 9
Laszczyk, 2019, Survey of quality measures for multi-objective optimization: construction of complementary set of multi-objective quality measures, Swarm Evol. Comput., 48, 109, 10.1016/j.swevo.2019.04.001
Naser, 2019, Concrete under fire: an assessment through intelligent pattern recognition, Eng. Comput., 36, 1
Degtyarev, 2021, Neural networks for predicting shear strength of CFS channels with slotted webs, J. Constr. Steel Res., 177, 10.1016/j.jcsr.2020.106443
De Lorenzis, 2007, Near-surface mounted FRP reinforcement: an emerging technique for strengthening structures, Compos. Part B, 38, 119, 10.1016/j.compositesb.2006.08.003
Scikit
Zhang, 2018, Performance comparison of fiber sheet strengthened RC beams bonded with geopolymer and epoxy resin under ambient and fire conditions, J. Struct. Fire Eng., 9, 174, 10.1108/JSFE-01-2017-0023
Dreiseitl, 2002, Logistic regression and artificial neural network classification models: a methodology review, J. Biomed. Inform., 35, 352, 10.1016/S1532-0464(03)00034-0
Blanco-Justicia, 2019, Machine learning explainability through comprehensible decision trees, 15, 10.1007/978-3-030-29726-8_2
Naser, 2021, Evaluating structural response of concrete-filled steel tubular columns through machine learning, J. Build. Eng., 34
Hall, 1998, Practical feature subset selection for machine learning, Computer Science, 181
Kaibel, 2021, Rethinking the gold standard with multi-armed bandits: machine learning allocation algorithms for experiments, Organ. Res. Methods, 24, 78, 10.1177/1094428119854153
Raschka, 2018, Model evaluation, model selection, and algorithm selection in machine learning, ArXiv
Emmert-Streib, 2020
Hoffman, 2018, Explaining explanation for “explainable AI”, Vol. 62, 197
Hase, 2020, Evaluating explainable AI: which algorithmic explanations help users predict model behavior?, 5540