Machine learning for structural engineering: A state-of-the-art review

Thai, Huu‐Tai

doi:10.1016/j.istruc.2022.02.003

Cited by 327 publications

(70 citation statements)

References 507 publications

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“…Several ML approaches have been employed for various concrete types, but still, no coherence has been developed. In some research studies, the superior performance of decision tree-based algorithms is reported compared to neural network-based algorithms, whereas the other researchers reported the vice versa [52]. Different machine learning algorithms have been employed by various researchers for predicting the concrete material and concrete member properties [53][54][55][56].…”

Section: Plos Onementioning

confidence: 99%

Advanced machine learning algorithms to evaluate the effects of the raw ingredients on flowability and compressive strength of ultra-high-performance concrete

et al. 2022

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The estimation of concrete characteristics through artificial intelligence techniques is come out to be an effective way in the construction sector in terms of time and cost conservation. The manufacturing of Ultra-High-Performance Concrete (UHPC) is based on combining numerous ingredients, resulting in a very complex composite in fresh and hardened form. The more ingredients, along with more possible combinations, properties and relative mix proportioning, results in difficult prediction of UHPC behavior. The main aim of this research is the development of Machine Learning (ML) models to predict UHPC flowability and compressive strength. Accordingly, sophisticated and effective artificial intelligence approaches are employed in the current study. For this purpose, an individual ML model named Decision Tree (DT) and ensembled ML algorithms called Bootstrap Aggregating (BA) and Gradient Boosting (GB) are applied. Statistical analyses like; Determination Coefficient (R2), Root Mean Square Error (RMSE), and Mean Absolute Error (MAE) are also employed to evaluate algorithms’ performance. It is concluded that the GB approach appropriately forecasts the UHPC flowability and compressive strength. The higher R2 value, i.e., 0.94 and 0.95 for compressive and flowability, respectively, of the DT technique and lesser error values, have higher precision than other considered algorithms with lower R2 values. SHAP analysis reveals that limestone powder content and curing time have the highest SHAP values for UHPC flowability and compressive strength, respectively. The outcomes of this research study would benefit the scholars of the construction industry to quickly and effectively determine the flowability and compressive strength of UHPC.

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Section: Plos Onementioning

confidence: 99%

Advanced machine learning algorithms to evaluate the effects of the raw ingredients on flowability and compressive strength of ultra-high-performance concrete

et al. 2022

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“…Because the Machine Learning (ML) technique is capable of dealing with complex nonlinear structural systems under extreme action, along with the availability of large data sets, the use of ML in structural engineering has become increasingly popular in recent years. The applications of ML mainly include: (1) structural design and analysis; (2) structural health monitoring and damage detection; (3) structural fire resistance; (4) structural member resistance to various actions; (5) concrete mechanical properties and mix design [4][5][6][7][8]. Professional technicians can familiarize themselves with the field as quickly as possible by providing database and machine learning code.…”

Section: Introductionmentioning

confidence: 99%

Structural Damage Identification System Suitable for Old Arch Bridge in Rural Regions: Random Forest Approach

Xiong¹,

Liang²,

She³

et al. 2023

Computer Modeling in Engineering &Amp; Sciences

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A huge number of old arch bridges located in rural regions are at the peak of maintenance. The health monitoring technology of the long-span bridge is hardly applicable to the small-span bridge, owing to the absence of technical resources and sufficient funds in rural regions. There is an urgent need for an economical, fast, and accurate damage identification solution. The authors proposed a damage identification system of an old arch bridge implemented with a machine learning algorithm, which took the vehicle-induced response as the excitation. A damage index was defined based on wavelet packet theory, and a machine learning sample database collecting the denoised response was constructed. Through comparing three machine learning algorithms: Back-Propagation Neural Network (BPNN), Support Vector Machine (SVM), and Random Forest (R.F.), the R.F. damage identification model were found to have a better recognition ability. Finally, the Particle Swarm Optimization (PSO) algorithm was used to optimize the number of subtrees and split features of the R.F. model. The PSO optimized R.F. model was capable of the identification of different damage levels of old arch bridges with sensitive damage index. The proposed framework is practical and promising for the old bridge's structural damage identification in rural regions.

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“…Numerous studies describing successful applications of rapidly developing machine learning (ML) techniques to structural engineering problems [12][13][14][15] indicate that the ML approach might improve the accuracy of the stud resistance predictions while also satisfying the reliability requirements of building codes. ML models represent pre-trained computer algorithms that humans cannot easily comprehend.…”

Section: Introductionmentioning

confidence: 99%

Reliability-based design shear resistance of headed studs in solid slabs predicted by machine learning models

Degtyarev

Hicks

2022

Archit. Struct. Constr.

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The economical and reliable design of steel-concrete composite structures relies on accurate predictions of the resistance of headed studs transferring the longitudinal shear forces between the two materials. The existing mechanics-based or empirical design equations do not always produce accurate and safe predictions of the stud shear resistance. This study presents the evaluation of nine machine learning (ML) algorithms and the development of optimized ML models for predicting the stud resistance. The ML models were trained and tested using databases of push-out test results for studs in both normal weight and lightweight concrete. The reliability of ML model predictions was evaluated in accordance with European and US design practices. Reduction coefficients required for the ML models to satisfy the Eurocode reliability requirements for the design shear resistance were determined. Resistance factors used in US design practice were also obtained. The developed ML models were interpreted using the SHapley Additive exPlanations (SHAP) method. Predictions by the ML models were compared with those by the existing descriptive equations, which demonstrated a higher accuracy for the ML models. A web application that conveniently provides predictions of the nominal and design stud shear resistances by the developed ML models in accordance with both European and US design practices was created and deployed to the cloud.

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Machine learning for structural engineering: A state-of-the-art review

Cited by 327 publications

References 507 publications

Advanced machine learning algorithms to evaluate the effects of the raw ingredients on flowability and compressive strength of ultra-high-performance concrete

Advanced machine learning algorithms to evaluate the effects of the raw ingredients on flowability and compressive strength of ultra-high-performance concrete

Structural Damage Identification System Suitable for Old Arch Bridge in Rural Regions: Random Forest Approach

Reliability-based design shear resistance of headed studs in solid slabs predicted by machine learning models

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