Pith. sign in

REVIEW 5 major objections 6 minor 65 references

Investigation of wind pressures on tall building under interference effects using machine learning techniques

T0 review · 5 major / 6 minor · reviewed 2026-08-14 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗ Review JSON

Pith's one-line read A GAN trained on only 30% of a 2,664-case wind-tunnel pressure database predicts untested building-interference configurations as accurately as the full dataset.

desk verdict Solid GAN application to wind pressure prediction, but the 30%-sufficiency/70%-savings claim needs repeated splits and an interpolation baseline before it can be trusted. read the letter →

arxiv 1908.07307 v1 pith:YFQSY5YB submitted 2019-08-20 cs.LG eess.SPstat.ML

Gang Hu , Lingbo Liu , Dacheng Tao , Jie Song , K.C.S. Kwok This is my paper · ORCID
classification cs.LGeess.SPstat.ML
keywords windpressurecoefficientsinterferenceeffectstallbuildingsgenerativeadversarialnetworksmachinelearningtunneltestingmapprediction
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

The paper attacks a practical conflict: understanding how a neighboring tall building changes wind pressures on a principal building normally requires hundreds of wind-tunnel runs, but budgets force engineers to test only a few layouts. The authors claim that a generative adversarial network can close this gap. Trained on only 30% of the measured pressure cases in a public database, their GAN reproduces both mean and fluctuating pressure coefficients on the building faces for unseen interferer positions and wind angles, with coefficients of determination of 0.988 for mean and 0.924 for fluctuating pressures. The key practical claim is that those 30% of cases are enough: beyond 30%, more data adds almost nothing, so 70% of wind-tunnel testing could be saved. If true, the result turns a data-hungry experimental program into a one-time training set plus a fast learned surrogate.

What carries the argument

The load-bearing object is a conditional GAN with a global-local generator and a patch discriminator. The condition is the triplet (Sx,Sy,θ)—the interfering building's position and the wind angle—and the output is a paired set of pressure maps, mean and fluctuating, on the building's four faces. Fully connected layers produce coarse initial maps, then five residual blocks with side outputs refine local detail, while two patch discriminators classify small 3×4 regions as real or fake; this adversarial loss enforces high-frequency spatial structure. The key property is that the network learns pressure fields as images, so spatial correlations across faces and around separation regions are part of the prediction, which is why it outperforms pointwise tree-based and boosting models.

What would settle it

Take the same 30% training budget but hold out a structured slice of the configuration space—for example, all cases with the interfering building in a particular upstream region or all wind angles in a 30-degree band—and then evaluate the trained GAN on that slice. If the coefficient of determination drops substantially below the reported 0.988 and 0.924, or the pressure maps miss the known separation regions, the claim of accurate prediction under any unseen interference condition would be refuted for that part of the space.

Watch

Extended reading notes

Core claim

The central discovery is that a purpose-built conditional GAN, rather than a pointwise regressor, can serve as an emulator of an entire wind-pressure database. Given the interferer's coordinates relative to the principal building and the wind attack angle, the generator outputs two 9×28 maps—one for mean pressure coefficients and one for fluctuating pressure coefficients—unwrapped across the four faces. The adversarial discriminator, applied to local patches, preserves the sharp spatial structure of separation bubbles and reattachment zones that average losses would blur. In head-to-head tests the GAN beats decision tree, random forest, and XGBoost, and it maintains its accuracy when trained on only 30% of the database; the paper therefore states that the 30%-trained model is equivalent to the full 2,664-case dataset. The practical conclusion drawn is that 70% of wind-tunnel test cases can be omitted.

Load-bearing premise

The load-bearing assumption is that the six reserved validation cases and the 20% test split inside one randomly chosen 30% training subset fairly represent all 2,664 possible interferer positions and wind angles, so the high agreement scores mean the model can interpolate anywhere in the configuration space, not just near the cases it was shown.

Editorial extensions

If this is right

  • Wind engineers could test roughly one in three interference configurations and generate the remaining two thirds computationally, cutting campaign time and cost.
  • The model can be re-evaluated at any continuous interferer position and wind angle, so interference factor maps for forces and moments can be produced at higher resolution than the discrete 5-degree and discrete-position grid of the original tests.
  • The same image-to-image training recipe is a template for predicting other spatially distributed wind effects, such as cladding pressures or roof pressures, from limited experimental data.
  • The data-volume curve, with accuracy saturating near 30%, gives a practical rule for sizing future wind-tunnel matrices before a campaign begins.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • Extrapolating beyond the demonstrated geometry, the 30% threshold should not be assumed to hold for unequal building heights, different cross-sections, or multiple interfering buildings; each new configuration class would need its own training set and saturation curve.
  • A structured hold-out test—reserving a whole spatial region or wind-angle band rather than a random 20%—would be a stricter check of whether the GAN is learning the physics of interference or memorizing neighborhoods in the training grid.
  • The same generator could be placed inside an optimization loop to find worst-case interferer locations for cladding or crosswind response, converting a predictive model into a design-search tool.
  • The near-flat accuracy gain beyond 30% hints that the pressure field varies smoothly with interferer position and wind angle in this two-building configuration, so the model's usefulness depends on interpolation within the tested envelope rather than extrapolation beyond it.
Share X Bluesky LinkedIn Reddit HN

Signed reviews

No signed human review yet.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

5 major / 6 minor

Summary. The manuscript proposes using machine learning to predict mean and fluctuating wind pressure coefficients on a tall principal building under interference from an identical neighboring building, using the Tokyo Polytechnic University aerodynamic database (2664 cases; 37 interferer locations × 72 wind angles). Four models—decision tree regressor, random forest, XGBoost, and a custom two-stream GAN with a global-local generator and patch discriminator—are compared on a 30% subset of the data; the GAN achieves the highest R² on the inner test set. The authors then train GANs on 10–90% of the data and, based on MSE/R² curves and two visual comparisons on six held-out cases, conclude that 30% suffices for accurate prediction and that 70% of wind tunnel tests could be saved.

Significance. The practical goal is attractive: if a generative model can learn pressure distributions from a limited set of wind-tunnel tests, the cost of interference studies could be reduced substantially. The paper is among the early applications of GANs to wind-pressure prediction, uses a public benchmark database, and includes careful hyperparameter optimization for classical baselines; the four-model comparison and visual pressure-map outputs are useful. However, the headline quantitative claim—that 30% of the data is equivalent to the full set and that 70% of tests can be saved—rests on a single random split, a small hold-out set, and no comparison to classical interpolation. The significance is therefore conditional on additional validation experiments.

major comments (5)
  1. [§4.3, Figs. 11-12] The 30% threshold and the accompanying "70% savings" claim are based on a single random split with no error bars or repeated-seed trials. The MSE/R² trajectories show one run per data portion; because the test set for each portion is the 20% held out from that same portion, the curves conflate training-set size with changes in test-set composition and size. To support the central claim, the authors should fix an independent test set, repeat the random selection over multiple seeds, and report mean ± standard deviation (or confidence intervals) for each portion.
  2. [§4.1 and §4.3] The final validation uses only six held-out cases, and only two are displayed "due to page limit"; the manuscript asserts rather than demonstrates that these cases span the relevant interference conditions. No per-case quantitative errors are reported for the six cases, and no coverage analysis (e.g., spanning critical locations Sx, Sy and wind angles) is provided. This is too thin to support the claim of accurate prediction under "any unseen interference conditions."
  3. [§5 and Introduction] The claim that GANs specifically enable the 70% savings is not tested against a non-ML baseline. The introduction dismisses linear interpolation as "questionable," but no quantitative comparison with linear/kriging interpolation or other regressors is given. Without such a baseline, the observed accuracy at 30% data may reflect the smoothness of the pressure field rather than a property of the GAN architecture.
  4. [§4.3 and Conclusions] The equivalence claim—that the GANs model based on 30% of the dataset can be considered equivalent to the entire 2664 dataset—is not directly tested. The authors never train a GAN on the full dataset and compare it on the same fixed hold-out set; the 10–90% curves use changing inner test sets and only two visual comparisons between 30% and 50% models. A direct comparison of GAN-30% and GAN-100% on a fixed test set, with a statistical equivalence or non-inferiority test, is needed.
  5. [§4.3 and Introduction] All evaluated inputs are discrete grid points from the 37 locations × 72 angles; no off-grid condition (e.g., Sx = 5.5B or θ = 57.5°) is tested, although the stated goal includes "untested interference conditions" and "high-resolution interference factor maps." The interpolation capability of the GAN in continuous input space therefore remains unvalidated.
minor comments (6)
  1. [Section 2] "Principle building" appears in Section 2 and the Conclusions; it should be "principal building."
  2. [Introduction] "Building breaths" should be "building breadths."
  3. [§3.4.3, Eq. (2)] Mt and Ft are not defined before use; the authors should define the ground-truth mean and fluctuating pressure maps and correct the garbled summation notation "5É" in the displayed equation.
  4. [§4.2] The opening sentence refers to "2 unseen interference conditions" for the validation; this should read "6 held-out cases (two shown)."
  5. [Conclusions] The statement that the 30%-based GANs model is "superior to the full dataset" is conceptually misstated; the intended claim is presumably that it achieves accuracy comparable to a model trained on the full dataset, which is not yet demonstrated.
  6. [Abstract/Section 5] The abstract and discussion say "30% of the entire 2664 dataset," whereas Section 2 says 30% of the remaining 2558 cases after removing the six hold-out cases; this inconsistency should be corrected.

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity: standard supervised learning with held-out validation; the 30% sufficiency claim is an empirical saturation judgment, not forced by construction.

full rationale

The paper's pipeline is ordinary supervised regression: the TPU wind-pressure database is split into training/testing subsets, four models map the conditioning inputs (Sx, Sy, wind angle) to mean and fluctuating pressure maps, and performance is evaluated on testing and validation cases not used in training. The GANs generator and discriminator losses (Eqs. 1-7) contain no term encoding the validation outcomes or the '30% is sufficient' conclusion, so the predictions are not defined in terms of the targets. The 30% threshold is chosen empirically from R2/MSE saturation curves (Figs. 11-12) and then checked on six held-out cases; this involves model-selection on the same data, which is a statistical validity concern, but it is not circular because no fitted parameter is renamed as a prediction and the held-out cases remain unseen during training. The only self-citation with a methodological role is Hu & Kwok (2019) for 10-fold cross-validation, which is a standard external technique and is not load-bearing; no uniqueness theorem, ansatz, or defining identity is imported from prior work. Therefore the derivation chain is self-contained as a machine-learning evaluation, and there is no circular step to report. Concerns about single-split variability and the strength of the phrase 'any unseen interference conditions' are generalization-risk issues, not circularity.

Assumptions & free parameters 3 free parameters · 4 assumptions · 0 invented entities

The central claim rests on the public TPU database as ground truth, a smoothness assumption for interpolation, and a data-dependent choice of the 30% threshold. The model introduces no new physical entities, only a neural network architecture.

free parameters (3)
  • Training data portion threshold = 30%
    The paper selects 30% after inspecting the portion-performance curves (Figures 11 and 12); the conclusion that 70% of wind tunnel tests can be saved depends on this data-dependent threshold.
  • Adversarial loss weight lambda = 100
    Set by the authors for the GAN training loss (Eq. 4); affects the balance between pixel accuracy and adversarial realism, and hence the predicted pressure maps.
  • Number of residual blocks per branch = 5
    Architecture choice in the global-local generator (Section 3.4.1); not shown to be optimized, selected by hand.
assumptions (4)
  • domain assumption The TPU aerodynamic database provides accurate wind tunnel measurements of pressure coefficients for the principal building.
    The entire training and validation rests on this public database (Section 2); the paper does not independently verify the data.
  • domain assumption Pressure coefficients vary smoothly with interfering building location (Sx, Sy) and wind angle, so interpolation and generative prediction are valid.
    The method implicitly assumes continuity and smoothness; the paper criticizes linear interpolation but relies on a learned nonlinear interpolation (Section 1, 4).
  • domain assumption The 9x28 pressure map resolution (252 taps) sufficiently represents the continuous pressure field.
    The GAN output is a 9x28 map; fine-scale pressure variations between taps are not modeled (Section 2, 3.4.1).
  • domain assumption The 6 randomly held-out cases and the 20% test split are representative of the full interference configuration space.
    Generalization to any unseen interference conditions assumes these samples cover the input distribution (Sections 2 and 4.2).

how reviews work

0 comments
Cite this review

Pith. "Pith review of Investigation of wind pressures on tall building under interference effects using machine learning techniques." pith.science (2026). https://pith.science/paper/YFQSY5YB

@misc{pith2026190807307,
  author       = {Pith},
  title        = {Pith review of: Investigation of wind pressures on tall building under interference effects using machine learning techniques},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/YFQSY5YB}},
  note         = {Machine review of arXiv:1908.07307}
}
read the original abstract

Interference effects of tall buildings have attracted numerous studies due to the boom of clusters of tall buildings in megacities. To fully understand the interference effects of buildings, it often requires a substantial amount of wind tunnel tests. Limited wind tunnel tests that only cover part of interference scenarios are unable to fully reveal the interference effects. This study used machine learning techniques to resolve the conflicting requirement between limited wind tunnel tests that produce unreliable results and a completed investigation of the interference effects that is costly and time-consuming. Four machine learning models including decision tree, random forest, XGBoost, generative adversarial networks (GANs), were trained based on 30% of a dataset to predict both mean and fluctuating pressure coefficients on the principal building. The GANs model exhibited the best performance in predicting these pressure coefficients. A number of GANs models were then trained based on different portions of the dataset ranging from 10% to 90%. It was found that the GANs model based on 30% of the dataset is capable of predicting both mean and fluctuating pressure coefficients under unseen interference conditions accurately. By using this GANs model, 70% of the wind tunnel test cases can be saved, largely alleviating the cost of this kind of wind tunnel testing study.

Figures

Figures reproduced from arXiv: 1908.07307 by the authors.

Figure 9
Figure 9. It can be seen that for mean pressure coefficients, all [PITH_FULL_IMAGE:figures/full_fig_p009_9.png] view at source ↗
Figure 14
Figure 14. In general, the predictions are not as accurate as [PITH_FULL_IMAGE:figures/full_fig_p011_14.png] view at source ↗

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

65 extracted references · 64 canonical work pages

  1. [1]

    APACrefauthors \ 2001

    Adeli2001 APACrefauthors Adeli, H. APACrefauthors \ 2001 . Neural Networks in Civil Engineering: 1989-2000 Neural Networks in Civil Engineering: 1989-2000 . Computer-Aided Civil and Infrastructure Engineering 16 2 126--142

    work page 2001

  2. [2]

    APACrefauthors \ 2014

    Alpaydin2014 APACrefauthors Alpaydin, E. APACrefauthors \ 2014 . Introduction to machine learning Introduction to machine learning \ ( Second \ ). Cambridge, Massachusetts MIT press

    work page 2014

  3. [3]

    \ Kwok, K C

    Bailey1985 APACrefauthors Bailey, P A. \ Kwok, K C. APACrefauthors \ 1985 . Interference excitation of twin tall buildings Interference excitation of twin tall buildings . Journal of Wind Engineering and Industrial Aerodynamics 21 3 323--338

    work page 1985

  4. [4]

    , Zhongzhen, Y

    Bin2006 APACrefauthors Bin, Y. , Zhongzhen, Y. \ Baozhen, Y. APACrefauthors \ 2006 . Bus arrival time prediction using support vector machines Bus arrival time prediction using support vector machines . Journal of Intelligent Transportation Systems: Technology, Planning, and Operations 10 4 151--158

    work page 2006

  5. [5]

    , Gimenez, J M

    Bre2018 APACrefauthors Bre, F. , Gimenez, J M. \ Fachinotti, V D. APACrefauthors \ 2018 . Prediction of wind pressure coefficients on building surfaces using artificial neural networks Prediction of wind pressure coefficients on building surfaces using artificial neural networks . Energy and Buildings 158 1429--1441

    work page 2018

  6. [6]

    APACrefauthors \ 1984

    Breiman1984 APACrefauthors Breiman, L. APACrefauthors \ 1984 . Classification and Regression Trees Classification and Regression Trees . New York Routledge

    work page 1984

  7. [7]

    APACrefauthors \ 2001

    Breiman2001 APACrefauthors Breiman, L. APACrefauthors \ 2001 . Random forests Random forests . Machine Learning 45 1 5--32

    work page 2001

  8. [8]

    u y \" u k \

    Cha2017 APACrefauthors Cha, Y J. , Choi, W. \ B \" u y \" u k \" o zt \" u rk, O. APACrefauthors \ 2017 . Deep Learning-Based Crack Damage Detection Using Convolutional Neural Networks Deep Learning-Based Crack Damage Detection Using Convolutional Neural Networks . Computer-Aided Civil and Infrastructure Engineering 32 5 361--378

    work page 2017

Show all 65 references
  1. [9]

    \ Guestrin, C

    Chen2016 APACrefauthors Chen, T. \ Guestrin, C. APACrefauthors \ 2016 . XGBoost: A scalable tree boosting system XGBoost: A scalable tree boosting system . Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining - KDD '16 785--794

    2016

  2. [10]

    , Chou, J S

    Cheng2012 APACrefauthors Cheng, M Y. , Chou, J S. , Roy, A F. \ Wu, Y W. APACrefauthors \ 2012 . High-performance Concrete Compressive Strength Prediction using Time-Weighted Evolutionary Fuzzy Support Vector Machines Inference Model High-performance Concrete Compressive Stren...

    2012

  3. [11]

    \ Pham, A D

    Chou2015 APACrefauthors Chou, J S. \ Pham, A D. APACrefauthors \ 2015 . Smart Artificial Firefly Colony Algorithm-Based Support Vector Regression for Enhanced Forecasting in Civil Engineering Smart Artificial Firefly Colony Algorithm-Based Support Vector Regression for Enhance...

    2015

  4. [12]

    , Tsai, C F

    Chou2014 APACrefauthors Chou, J S. , Tsai, C F. , Pham, A D. \ Lu, Y H. APACrefauthors \ 2014 . Machine learning in concrete strength simulations: Multi-nation data analytics Machine learning in concrete strength simulations: Multi-nation data analytics . Construction and Buil...

    2014

  5. [13]

    , Park, G

    Figueiredo2011 APACrefauthors Figueiredo, E. , Park, G. , Farrar, C R. , Worden, K. \ Figueiras, J. APACrefauthors \ 2011 . Machine learning algorithms for damage detection under operational and environmental variability Machine learning algorithms for damage detection under o...

    2011

  6. [14]

    , Koco \' n , A

    Flaga2018 APACrefauthors Flaga, A. , Koco \' n , A. , K aput, R. \ Bosak, G. APACrefauthors \ 2018 . The environmental effects of aerodynamic interference between two closely positioned irregular high buildings The environmental effects of aerodynamic interference between two ...

    2018

  7. [15]

    , Li, Q S

    Fu2006 APACrefauthors Fu, J Y. , Li, Q S. \ Xie, Z N. APACrefauthors \ 2006 . Prediction of wind loads on a large flat roof using fuzzy neural networks Prediction of wind loads on a large flat roof using fuzzy neural networks . Engineering Structures 28 1 153--161

    2006

  8. [16]

    , Liang, S G

    Fu2007 APACrefauthors Fu, J Y. , Liang, S G. \ Li, Q S. APACrefauthors \ 2007 . Prediction of wind-induced pressures on a large gymnasium roof using artificial neural networks Prediction of wind-induced pressures on a large gymnasium roof using artificial neural networks . Com...

    2007

  9. [17]

    , Pouget-Abadie, J

    Goodfellow2014 APACrefauthors Goodfellow, I. , Pouget-Abadie, J. , Mirza, M. , Xu, B. , Warde-Farley, D. , Ozair, S. Bengio, Y. APACrefauthors \ 2014 . Generative Adversarial Nets Generative Adversarial Nets . Advances in Neural Information Processing Systems 27 2672--2680

    2014

  10. [18]

    , Zhang, X

    He2016 APACrefauthors He, K. , Zhang, X. , Ren, S. \ Sun, J. APACrefauthors \ 2016 . Deep Residual Learning for Image Recognition Deep Residual Learning for Image Recognition . the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) the ieee conference on compute...

    2016

  11. [19]

    \ Kwok, K C S

    Hu2019 APACrefauthors Hu, G. \ Kwok, K C S. APACrefauthors \ 2019 . Predicting wind pressures around circular cylinders using machine learning techniques Predicting wind pressures around circular cylinders using machine learning techniques . arXiv preprint arXiv:1901.06752

    2019 arXiv

  12. [20]

    Huang2017 APACrefauthors Huang, D. , He, S. , He, X. \ Zhu, X. APACrefauthors \ 2017 . Prediction of wind loads on high-rise building using a BP neural network combined with POD Prediction of wind loads on high-rise building using a BP neural network combined with POD . Journa...

    2017

  13. [21]

    Huang2018 APACrefauthors Huang, D. , Hu, D. , He, J. \ Xiong, Y. APACrefauthors \ 2018 . Structure Damage Detection Based on Ensemble Learning Structure Damage Detection Based on Ensemble Learning . Proceedings of 2018 9th International Conference on Mechanical and Aerospace E...

    2018

  14. [22]

    , Tamura, Y

    Hui2017 APACrefauthors Hui, Y. , Tamura, Y. \ Yang, Q. APACrefauthors \ 2017 . Analysis of interference effects on torsional moment between two high-rise buildings based on pressure and flow field measurement Analysis of interference effects on torsional moment between two hig...

    2017

  15. [23]

    , Tamura, Y

    Hui2012 APACrefauthors Hui, Y. , Tamura, Y. \ Yoshida, A. APACrefauthors \ 2012 . Mutual interference effects between two high-rise building models with different shapes on local peak pressure coefficients Mutual interference effects between two high-rise building models with ...

    2012

  16. [24]

    , Cheng, P

    Jin2018 APACrefauthors Jin, X. , Cheng, P. , Chen, W L. \ Li, H. APACrefauthors \ 2018 . Prediction model of velocity field around circular cylinder over various Reynolds numbers by fusion convolutional neural networks based on pressure on the cylinder Prediction model of velo...

    2018

  17. [25]

    APACrefauthors \ 1986

    Kareem1986 APACrefauthors Kareem, A. APACrefauthors \ 1986 . Performance of Cladding in Hurricane Alicia Performance of Cladding in Hurricane Alicia . Journal of Structural Engineering 112 12 2679--2693

    1986

  18. [26]

    , Stathopoulos, T

    Khanduri1998 APACrefauthors Khanduri, A. , Stathopoulos, T. \ B \' e dard, C. APACrefauthors \ 1998 . Wind-induced interference effects on buildings \- a review of the state-of-the-art Wind-induced interference effects on buildings \- a review of the state-of-the-art . Enginee...

    1998

  19. [27]

    , Tamura, Y

    Kim2011 APACrefauthors Kim, W. , Tamura, Y. \ Yoshida, A. APACrefauthors \ 2011 . Interference effects on local peak pressures between two buildings Interference effects on local peak pressures between two buildings . Journal of Wind Engineering and Industrial Aerodynamics 99 ...

    2011

  20. [28]

    , Tamura, Y

    Kim2015 APACrefauthors Kim, W. , Tamura, Y. \ Yoshida, A. APACrefauthors \ 2015 . Interference effects on aerodynamic wind forces between two buildings Interference effects on aerodynamic wind forces between two buildings . Journal of Wind Engineering and Industrial Aerodynami...

    2015

  21. [29]

    Kingma2014 APACrefauthors Kingma, D P. \ Ba, J. APACrefauthors \ 2014 . Adam: A method for stochastic optimization Adam: A method for stochastic optimization . arXiv preprint arXiv:1412.6980

    2014 arXiv

  22. [30]

    , Hitchcock, P A

    Kwok2009 APACrefauthors Kwok, K C S. , Hitchcock, P A. \ Burton, M D. APACrefauthors \ 2009 . Perception of vibration and occupant comfort in wind-excited tall buildings Perception of vibration and occupant comfort in wind-excited tall buildings . Journal of Wind Engineering a...

    2009

  23. [31]

    , Zhao, J G

    lam2011 APACrefauthors Lam, K M. , Zhao, J G. \ Leung, M Y H. APACrefauthors \ 2011 . Wind-induced loading and dynamic responses of a row of tall buildings under strong interference Wind-induced loading and dynamic responses of a row of tall buildings under strong interference...

    2011

  24. [32]

    , S nderby, S K

    Larsen2016 APACrefauthors Larsen, A B L. , S nderby, S K. , Larochelle, H. \ Winther, O. APACrefauthors \ 2016 . Autoencoding Beyond Pixels Using a Learned Similarity Metric Autoencoding Beyond Pixels Using a Learned Similarity Metric . Proceedings of the 33rd International Co...

    2016

  25. [33]

    , Theis, L

    Ledig_2017_CVPR APACrefauthors Ledig, C. , Theis, L. , Huszar, F. , Caballero, J. , Cunningham, A. , Acosta, A. Shi, W. APACrefauthors \ 2017 July . Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network Photo-realistic single image super-resoluti...

    2017

  26. [34]

    , Laima, S

    Li2018 APACrefauthors Li, S. , Laima, S. \ Li, H. APACrefauthors \ 2018 . Data-driven modeling of vortex-induced vibration of a long-span suspension bridge using decision tree learning and support vector regression Data-driven modeling of vortex-induced vibration of a long-spa...

    2018

  27. [35]

    , Zhao, X

    Li2019 APACrefauthors Li, S. , Zhao, X. \ Zhou, G. APACrefauthors \ 2019 . Automatic pixel-level multiple damage detection of concrete structure using fully convolutional network Automatic pixel-level multiple damage detection of concrete structure using fully convolutional ne...

    2019

  28. [36]

    , Qiu, Z

    Liu2019 APACrefauthors Liu, L. , Qiu, Z. , Li, G. , Wang, Q. , Ouyang, W. \ Lin, L. APACrefauthors \ 2019 . Contextualized Spatial-Temporal Network for Taxi Origin-Destination Demand Prediction Contextualized Spatial-Temporal Network for Taxi Origin-Destination Demand Predicti...

    2019

  29. [37]

    , Wang, H

    Liu2018b APACrefauthors Liu, L. , Wang, H. , Li, G. , Ouyang, W. \ Lin, L. APACrefauthors \ 2018 . Crowd Counting using Deep Recurrent Spatial-Aware Network Crowd Counting using Deep Recurrent Spatial-Aware Network . International Joint Conference on Artificial Intelligence (I...

    2018

  30. [38]

    , Zhang, R

    Liu2018a APACrefauthors Liu, L. , Zhang, R. , Peng, J. , Li, G. , Du, B. \ Lin, L. APACrefauthors \ 2018 . Attentive Crowd Flow Machines Attentive Crowd Flow Machines . ACM International Conference on Multimedia (ACM MM). Acm international conference on multimedia (acm mm)

    2018

  31. [39]

    , Luo, H

    Liu2019a APACrefauthors Liu, Y. , Luo, H. , Zhao, B. , Zhao, X. \ Han, Z. APACrefauthors \ 2019 . Short-Term Power Load Forecasting Based on Clustering and XGBoost Method Short-Term Power Load Forecasting Based on Clustering and XGBoost Method . Proceedings of the IEEE Interna...

    2019

  32. [40]

    , Kim, Y C

    Lo2016 APACrefauthors Lo, Y L. , Kim, Y C. \ Li, Y C. APACrefauthors \ 2016 . Downstream interference effect of high-rise buildings under turbulent boundary layer flow Downstream interference effect of high-rise buildings under turbulent boundary layer flow . Journal of Wind E...

    2016

  33. [41]

    , Kim, Y C

    Lo2017 APACrefauthors Lo, Y L. , Kim, Y C. \ Yoshida, A. APACrefauthors \ 2017 . Effects of aerodynamic modification mechanisms on interference from neighboring buildings Effects of aerodynamic modification mechanisms on interference from neighboring buildings . Journal of Win...

    2017

  34. [42]

    APACrefauthors \ 2011

    Loh2011 APACrefauthors Loh, W Y. APACrefauthors \ 2011 . Classification and regression trees Classification and regression trees . Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery 1 1 14--23

    2011

  35. [43]

    , Duan, Y

    Lv2015 APACrefauthors Lv, Y. , Duan, Y. , Kang, W. , Li, Z. \ Wang, F Y. APACrefauthors \ 2015 . Traffic Flow Prediction with Big Data: A Deep Learning Approach Traffic Flow Prediction with Big Data: A Deep Learning Approach . IEEE Transactions on Intelligent Transportation Sy...

    2015

  36. [44]

    , Sekimoto, Y

    Maeda2018 APACrefauthors Maeda, H. , Sekimoto, Y. , Seto, T. , Kashiyama, T. \ Omata, H. APACrefauthors \ 2018 . Road Damage Detection and Classification Using Deep Neural Networks with Smartphone Images Road Damage Detection and Classification Using Deep Neural Networks with ...

    2018

  37. [45]

    , Terry, B

    Mara2014 APACrefauthors Mara, T. , Terry, B. , Ho, T. \ Isyumov, N. APACrefauthors \ 2014 . Aerodynamic and peak response interference factors for an upstream square building of identical height Aerodynamic and peak response interference factors for an upstream square building...

    2014

  38. [46]

    , Hua, X G

    Ni2005 APACrefauthors Ni, Y Q. , Hua, X G. , Fan, K Q. \ Ko, J M. APACrefauthors \ 2005 . Correlating modal properties with temperature using long-term monitoring data and support vector machine technique Correlating modal properties with temperature using long-term monitoring...

    2005

  39. [47]

    , Glisic, B

    Oh2019 APACrefauthors Oh, B K. , Glisic, B. , Kim, Y. \ Park, H S. APACrefauthors \ 2019 . Convolutional neural network-based wind-induced response estimation model for tall buildings Convolutional neural network-based wind-induced response estimation model for tall buildings ...

    2019

  40. [48]

    , Gross, S

    Paszke2017 APACrefauthors Paszke, A. , Gross, S. , Chintala, S. , Chanan, G. , Yang, E. , DeVito, Z. Lerer, A. APACrefauthors \ 2017 . Automatic differentiation in PyTorch Automatic differentiation in PyTorch . NIPS Workshop Autodiff Submission

    2017

  41. [49]

    APACrefauthors \ 1996

    Reich1996 APACrefauthors Reich, Y. APACrefauthors \ 1996 . Artificial Intelligence in Bridge Engineering Artificial Intelligence in Bridge Engineering . Computer-Aided Civil and Infrastructure Engineering 11 6 433--445

    1996

  42. [50]

    APACrefauthors \ 1997

    Reich1997 APACrefauthors Reich, Y. APACrefauthors \ 1997 . Machine Learning Techniques for Civil Engineering Problems Machine Learning Techniques for Civil Engineering Problems . Computer-Aided Civil and Infrastructure Engineering 12 4 295--310

    1997

  43. [51]

    \ Barai, S

    Reich1999 APACrefauthors Reich, Y. \ Barai, S. APACrefauthors \ 1999 . Evaluating machine learning models for engineering problems Evaluating machine learning models for engineering problems . Artificial Intelligence in Engineering 13 3 257--272

    1999

  44. [52]

    , Fischer, P

    Ronneberger2015 APACrefauthors Ronneberger, O. , Fischer, P. \ Brox, T. APACrefauthors \ 2015 . U-Net: Convolutional Networks for Biomedical Image Segmentation U-Net: Convolutional Networks for Biomedical Image Segmentation . N. Navab, J. Hornegger, W M. Wells \ A F. Frangi\ (...

    2015

  45. [53]

    , Figueiredo, E

    Santos2016 APACrefauthors Santos, A. , Figueiredo, E. , Silva, M F. , Sales, C S. \ Costa, J C. APACrefauthors \ 2016 . Machine learning algorithms for damage detection: Kernel-based approaches Machine learning algorithms for damage detection: Kernel-based approaches . Journal...

    2016

  46. [54]

    , Cevik, A

    Sonebi2016 APACrefauthors Sonebi, M. , Cevik, A. , Gr \" u newald, S. \ Walraven, J. APACrefauthors \ 2016 . Modelling the fresh properties of self-compacting concrete using support vector machine approach Modelling the fresh properties of self-compacting concrete using suppor...

    2016

  47. [55]

    , Tse, K T

    Song2016 APACrefauthors Song, J. , Tse, K T. , Tamura, Y. \ Kareem, A. APACrefauthors \ 2016 . Aerodynamics of closely spaced buildings: With application to linked buildings Aerodynamics of closely spaced buildings: With application to linked buildings . Journal of Wind Engine...

    2016

  48. [56]

    \ Kwok, K C S

    Tang2004 APACrefauthors Tang, U F. \ Kwok, K C S. APACrefauthors \ 2004 . Interference excitation mechanisms on a 3DOF aeroelastic CAARC building model Interference excitation mechanisms on a 3DOF aeroelastic CAARC building model . Journal of Wind Engineering and Industrial Ae...

    2004

  49. [57]

    , Wood, G S

    Thepmongkorn2002 APACrefauthors Thepmongkorn, S. , Wood, G S. \ Kwok, K C S. APACrefauthors \ 2002 . Interference effects on wind-induced coupled motion of a tall building Interference effects on wind-induced coupled motion of a tall building . Journal of Wind Engineering and ...

    2002

  50. [58]

    \ Cheng, J C P

    Wang2019 APACrefauthors Wang, M. \ Cheng, J C P. APACrefauthors \ 2019 . A unified convolutional neural network integrated with conditional random field for pipe defect segmentation A unified convolutional neural network integrated with conditional random field for pipe defect...

    2019

  51. [59]

    \ Manson, G

    Worden2007 APACrefauthors Worden, K. \ Manson, G. APACrefauthors \ 2007 . The application of machine learning to structural health monitoring The application of machine learning to structural health monitoring . Philosophical Transactions of the Royal Society A: Mathematical, ...

    2007

  52. [60]

    Xie2015 APACrefauthors Xie, S. \ Tu, Z. APACrefauthors \ 2015 . Holistically-nested edge detection Holistically-nested edge detection . the IEEE international conference on computer vision the ieee international conference on computer vision \ ( \ 1395--1403)

    2015

  53. [61]

    \ Li, Q S

    Yan2016 APACrefauthors Yan, B. \ Li, Q S. APACrefauthors \ 2016 . Wind tunnel study of interference effects between twin super-tall buildings with aerodynamic modifications Wind tunnel study of interference effects between twin super-tall buildings with aerodynamic modificatio...

    2016

  54. [62]

    , Xie, Z

    Yu2018 APACrefauthors Yu, X. , Xie, Z. \ Gu, M. APACrefauthors \ 2018 . Interference effects between two tall buildings with different section sizes on wind-induced acceleration Interference effects between two tall buildings with different section sizes on wind-induced accele...

    2018

  55. [63]

    , Qian, L

    Zhang2018 APACrefauthors Zhang, D. , Qian, L. , Mao, B. , Huang, C. , Huang, B. \ Si, Y. APACrefauthors \ 2018 . A Data-Driven Design for Fault Detection of Wind Turbines Using Random Forests and XGboost A Data-Driven Design for Fault Detection of Wind Turbines Using Random Fo...

    2018

  56. [64]

    APACrefauthors \ 2012

    Zhou2012 APACrefauthors Zhou, Z H. APACrefauthors \ 2012 . Ensemble Methods: Foundations and Algorithms Ensemble Methods: Foundations and Algorithms . Taylor & Francis

    2012

  57. [65]

    \ Lam, K M

    Zu2018 APACrefauthors Zu, G B. \ Lam, K M. APACrefauthors \ 2018 . Across-wind excitation mechanism for interference of twin tall buildings in staggered arrangement Across-wind excitation mechanism for interference of twin tall buildings in staggered arrangement . Journal of W...

    2018

Pith tools

Reviewed August 14, 2026 · model on record in the stance chip above.