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Artificial Intelligence, Machine Learning, and Deep Learning in Structural Engineering: A Scientometrics Review of Trends and Best Practices

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Abstract

Artificial Intelligence (AI), machine learning (ML), and deep learning (DL) are emerging techniques capable of delivering elegant and affordable solutions which can surpass those obtained through traditional methods. Despite the recent and rapid advancements in developing next-gen AI-based techniques, we continue to lack a systemic understanding of how AI, ML, and DL can fundamentally be integrated into the structural engineering domain. To advocate for a smooth and expedite the adoption of AI techniques into our field, we present a state-of-the-art review that is specifically tailored to structural engineers. This review aims to serve three purposes: (1) introduce the art and science of AI, ML, and DL in terms of its commonly used algorithms and techniques with particular attention to those of high value to this domain, (2) map the current knowledge within this domain through a scientometrics analysis of more than 4000 scholarly works with a focus on those published in the last decade to identify best practices in terms of procedures, performance metrics, and dataset size etc., and (3) review past and recent efforts that applied AI derivatives into the various subfields within structural engineering. Special attention is given to the application of AI, ML, and DL in earthquake, wind, and fire engineering, as well as structural health monitoring, damage detection, and prediction of properties of structural materials as collected from over 200 sources. Finally, a discussion on trends, recommendations, best practices, and advanced topics towards the end of this review.

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Notes

  1. This survey primarily favored search via “keywords” and confined this search to the las decades – future efforts can apply other filters such as search by “document title”, “document abstract” etc. or for a different time span.

  2. It is worth noting that the analysis displayed herein is based primarily based on our observations and constraints of this work. We do believe that a more systematic examination by means of social trends, surveys, and peer practices etc. is warranted.

  3. Some works reported a practice of eliminating data points with up to a certain degree of deviation from the global trend of data [91].

Abbreviations

AI:

Artificial intelligence

ALD:

Applied load

ANFIS:

Adoptive neuro-fuzzy interface

ANN:

Artificial Neural Network

ARI:

Arias intensity

ASI:

Acceleration spectrum intensity

BA:

Bagging technique

BD:

Bracketed duration

BFGS:

Broyden–Fletcher–Goldfarb–Shanno

BP-ANN:

Back propagation-Artificial Neural Network

CFL:

Ceiling finish layer

CGB:

Powell–Beale conjugate gradient algorithm

CGF:

Fletcher–Powell conjugate gradient back propagation

CGP:

Polak–Ribiere conjugate gradient back propagation

CSA:

Coupled simulated annealing

CVA:

Cumulative absolute velocity

DT:

Decision tree

EPA:

Effective peak acceleration

FFNN:

Feed forward neural network

FMCDM:

Fuzzy multi-criteria decision analysis

GA:

Grid search/genetic algorithm

GANs:

Generative adversarial networks

GBRT:

Gradient boosting regression tree

GDA:

Gradient descent with adaptive linear back propagation gradient

GDM:

Gradient descent BP with momentum

GDX:

Gradient descent w/momentum and adaptive linear back propagation

GEP:

Gene expression programming

GMDH:

Group method of data handling

GP:

Genetic programming (linear-based GP, Cartesian GP, grammatical GP, stack GP)

GSA:

Grid search algorithm

HI:

Housner intensity

HSSB:

High strength steel bolt

IBS:

Interfacial bond strength

JTY:

Joist type

KNN:

K-nearest neighbor

LGP:

Linear genetic programming

LM:

Levenberg–Marquart (back propagation)

LOOCV:

Leave one out cross-validation

LSTM:

The long short-term memory

LWLS-SVMR:

Locally weighted least squares support vector machines for regression

MCDM:

Multi-criteria decision analysis

MCFT:

Modified compression field theory

MGGP:

Multigene genetic programming

ML:

Machine learning

MLS-SVMR:

Multi-output least-squares support vector machine for regression

MOE:

Module of elasticity

MOR:

Module of rupture

OSS:

One step secant back propagation

PCA:

Principal component analysis

PGA:

Peak ground acceleration

PGD:

Peak ground displacement

PGV:

Peak ground velocity

PP:

Predominant period

PRSC:

Perfobon rib shear connector

PSO:

Particle swarm optimization

RC:

Reinforced concrete

RF:

Random forest

RP:

Resilient back propagation

SCG:

Scaled conjugate gradient back propagation

SD:

Significant duration

SED:

Specific energy density

SVM:

Support vector machine

TCC:

Thermal conductivity of concrete

TGP:

Tree-based Genetic Programming

UD:

Uniform Duration

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  1. Glenn Department of Civil Engineering, Clemson University, Clemson, SC, USA

    Arash Teymori Gharah Tapeh & M. Z. Naser

  2. AI Research Institute for Science and Engineering (AIRISE), Clemson University, Clemson, SC, USA

    M. Z. Naser

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Tapeh, A.T.G., Naser, M.Z. Artificial Intelligence, Machine Learning, and Deep Learning in Structural Engineering: A Scientometrics Review of Trends and Best Practices. Arch Computat Methods Eng 30, 115–159 (2023). https://doi.org/10.1007/s11831-022-09793-w

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