Structural health monitoring of exterior beam–column subassemblies through detailed numerical modelling and using various machine learning techniques

Santarsiero, Giuseppe; Mishra, Mayank; Singh, Manav Kumar; Masi, Angelo

doi:10.1016/j.mlwa.2021.100190

Cited by 10 publications

(5 citation statements)

References 40 publications

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“…Using optimization algorithms to make up for the defects of a single ML algorithm and to achieve efficient and accurate structural health monitoring has become a research hotspot. Guoqing Gui et al showed that the three optimized SVM methods can realize the accurate health monitoring of civil engineering structures, and their sensitivity, accuracy, and effectiveness are significantly better than traditional SVM methods [15], Giuseppe Santarsiero et al proposed an artificial neural network (ANN) optimized by particle swarm optimization (PSO) which can monitor the performance of reinforced concrete structures effectively [16]. Although many effective methods have been proposed in the field of bridge structural damage identification, most of these methods need to rely on professional equipment or laboratories, the cost of which is unaffordable for less-developed regions.…”

Section: Introductionmentioning

confidence: 99%

Structural Health Evaluation of Arch Bridge by Field Test and Optimized BPNN Algorithm

Xiong,

She,

Liang

et al. 2023

Frattura Ed Integrità Strutturale

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Arch bridges play an important role in rural roads in China. Due to insufficient funds and a lack of management techniques, many rural arch bridges are in a state of disrepair, unable to meet the increasing transportation needs. Thus, it is of great significance to develop a set of rapid and economic damage identification procedures for the management and maintenance of old arch bridges. Sanliushui Bridge, located in Chenggu County, Hanzhong, is selected as a model case. Field tests and numerical simulations were carried out to identify the damage states of Sanliushui Bridge. The sum square of wavelet packet energy change rate, a damage identification index based on wavelet packet analysis method was implemented to process the measured data of the load test and the simulated data of the numerical calculation model with assumed damage. BPNN, GA-BPNN, PSO-BPNN and test data analysis are adopted to compare the measured data with the simulated data to quantitatively identify the damage degree of the selected bridge. By comparing the results of the two methods mentioned above, it is found that the proposed damage identification approach realized a precise damage identification of the selected arch bridges.

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Section: Introductionmentioning

confidence: 99%

Structural Health Evaluation of Arch Bridge by Field Test and Optimized BPNN Algorithm

Xiong,

She,

Liang

et al. 2023

Frattura Ed Integrità Strutturale

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“…In recent years, with the continuous development of artificial intelligence technology, many scholars have applied artificial neural networks in engineering structure prediction or structural material research [16][17][18][19][20][21][22] with rich results. Liu et al [23] established a BP neural network prediction model for blast safe vibration velocity of newly cast concrete structures based on BP neural network theory and selected key influencing factors such as Poisson's ratio, and predicted the blast safe vibration velocity of concrete at different ages under two different conditions.…”

Section: Introductionmentioning

confidence: 99%

Fatigue Performance Prediction of RC Beams Based on Optimized Machine Learning Technology

et al. 2022

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The development of fatigue damage in reinforced concrete (RC) beams is affected by various factors such as repetitive loads and material properties, and there exists a complex nonlinear mapping relationship between their fatigue performance and each factor. To this end, a fatigue performance prediction model for RC beams was proposed based on the deep belief network (DBN) optimized by particle swarm optimization (PSO). The original database of fatigue loading tests was established by conducting fatigue loading tests on RC beams. The mid-span deflection, reinforcement strain, and concrete strain during fatigue loading of RC beams were predicted and evaluated. The fatigue performance prediction results of the RC beam based on the PSO-DBN model were compared with those of the single DBN model and the BP model. The models were evaluated using the R2 coefficient, mean absolute percentage error, mean absolute error, and root mean square error. The results showed that the fatigue performance prediction model of RC beams based on PSO-DBN is more accurate and efficient.

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“…Besides the experimental studies for developing knowhow about the influence of various input parameters on the structural performance, soft computing (SC)/machine learning (ML)/(AI)/techniques are gaining popularity nowadays, because of their ability to learn from training data so as to formulate a trained algorithm which can be used for accurate prediction of the output(s) [ 22 , 23 , 24 , 25 ]. The accuracy of a typical AI model depends on the number of data points used during the training process and the selection of influential input variables (i.e., high Pearson correlation value).…”

Section: Introductionmentioning

confidence: 99%

“…A literature survey reveals successful application of different AI techniques, such as ANN, GEP and ANFIS alongside the combination of meta-heuristic optimization algorithms and ML algorithms for predicting the mechanical performance of concrete as well as soils [ 24 , 33 , 34 , 35 , 36 , 37 , 38 , 39 ]. However, the ANN and ANFIS provide lesser insight about the models pertaining to their practical implications, e.g., to derive an empirical relation between the input parameters and output(s), which can further be used for predicting the output(s) and performing parametric and sensitivity analysis [ 40 , 41 ].…”

Section: Introductionmentioning

confidence: 99%

Prediction of Axial Capacity of Concrete Filled Steel Tubes Using Gene Expression Programming

Khan

Iqbal

Raheel³

et al. 2022

Materials

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The safety and economy of an infrastructure project depends on the material and design equations used to simulate the performance of a particular member. A variety of materials can be used in conjunction to achieve a composite action, such as a hollow steel section filled with concrete, which can be successfully utilized in the form of an axially loaded member. This study aims to model the ultimate compressive strength (Pu) of concrete-filled hollow steel sections (CFSS) by formulating a mathematical expression using gene expression programming (GEP). A total of 149 datapoints were obtained from the literature, considering ten input parameters, including the outer diameter of steel tube (D), wall thickness of steel tube, compressive strength of concrete (fc’), elastic modulus of concrete (Ec), yield strength of steel (fv), elastic modulus of steel (Es), length of the column (L), confinement factor (ζ), ratio of D to thickness of column, and the ratio of length to D of column. The performance of the developed models was assessed using coefficient of regression R2, root mean squared error RMSE, mean absolute error MAE and comparison of regression slopes. It was found that the optimal GEP Model T3, having number of chromosomes Nc = 100, head size Hs = 8 and number of genes Ng = 3, outperformed all the other models. For this particular model, R2overall equaled 0.99, RMSE values were 133.4 and 162.2, and MAE = 92.4 and 108.7, for training (TR) and testing (TS) phases, respectively. Similarly, the comparison of regression slopes analysis revealed that the Model T3 exhibited the highest R2 of 0.99 with m = 1, in both the TR and TS stages, respectively. Finally, parametric analysis showed that the Pu of composite steel columns increased linearly with the value of D, t and fy.

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Structural health monitoring of exterior beam–column subassemblies through detailed numerical modelling and using various machine learning techniques

Cited by 10 publications

References 40 publications

Structural Health Evaluation of Arch Bridge by Field Test and Optimized BPNN Algorithm

Structural Health Evaluation of Arch Bridge by Field Test and Optimized BPNN Algorithm

Fatigue Performance Prediction of RC Beams Based on Optimized Machine Learning Technology

Prediction of Axial Capacity of Concrete Filled Steel Tubes Using Gene Expression Programming

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