Damage identification in reinforced concrete beams using wavelet transform of modal excitation responses

This study focuses on identifying damage in reinforced concrete (RC) beams using timedomain modal testing and wavelet analysis. A numerical model of an RC beam was used to generate various damage scenarios with different severities and locations. Acceleration time histories were recorded for both damaged and undamaged structures. Two damage indices, DI_MW and DI_SW, derived from the wavelet analysis, were employed to determine the location and severity of the damage. The results showed that different wavelet families and specific mother wavelets had varying effectiveness in detecting damage. The Daubechies wavelet family (db2, db6, and db9) detected damage at the center and sides of the RC beams due to good time and frequency localization. The Biorthogonal wavelet family (bior2.8 and bior3.1) provided improved time–frequency resolution. The Symlets wavelet family (sym2 and sym7) offered a balanced trade-off between time and frequency localization. The Shannon wavelet family (shan1-0.5 and shan1-0.1) exhibited good time localization, while the Frequency B-Spline wavelet family (fbsp2-1-0.1) excelled in frequency localization. Certain combinations of mother wavelets, such as shan1-0.5 with the DI_SW index, were highly effective in detecting damage. The DI_SW index outperformed DI_MW across different numerical models. Selecting appropriate wavelet analysis techniques, particularly utilizing shan1-0.5 in the DI_SW, proved effective for detecting damage in RC beams

Approximating Helical Pile Pullout Resistance Using Metaheuristic-Enabled Fuzzy Hybrids

Piles have paramount importance for various structural systems in a wide scope of civil and geotechnical engineering works. Accurately predicting the pullout resistance of piles is critical for the long-term structural resilience of civil infrastructures. In this research, three sophisticated models are employed for precisely predicting the pullout resistance (Pul) of helical piles. Metaheuristic schemes of gray wolf optimization (GWO), differential evolution (DE), and ant colony optimization (ACO) were deployed for tuning an adaptive neuro-fuzzy inference system (ANFIS) in mapping the Pul behavior from three independent factors, namely the embedment ratio, the density class, and the ratio of the shaft base diameter to the shaft diameter. Based on the results, i.e., the Pearson’s correlation coefficient (R = 0.99986 vs. 0.99962 and 0.99981) and root mean square error (RMSE = 7.2802 vs. 12.1223 and 8.5777), the GWO-ANFIS surpassed the DE- and ACO-based ensembles in the training phase. However, smaller errors were obtained for the DE-ANFIS and ACO-ANFIS in predicting the Pul pattern. Overall, the results show that all three models are capable of predicting the Pul for helical piles in both loose and dense soils with superior accuracy. Hence, the combination of ANFIS and the mentioned metaheuristic algorithms is recommended for real-world purposes

The road to optimal acceleration of Dixon imaging and quantitative T2-mapping in the ankle using compressed sensing and parallel imaging

Objective: This study aimed to find the optimal acceleration factor achievable with CS-SENSE for a clinical ankle protocol while maintaining comparable image quality. Methods: We explored the optimal acceleration achievable with factor CS-SENSE, for an ankle protocol with T2-weighted, PD-weighted TSE-Dixon (coronal, axial and sagittal) and T2-mapping (sagittal) sequences, on a 3 T MRI-scanner. This study contained three steps: (1) phantom test, (2) pilot test on healthy volunteers, (3) anatomical assessment on a cohort of healthy volunteers and a quantitative analysis. CS-SENSE images (acceleration factors between 2.0× and 12.0×) were compared to reference SENSE images (acceleration factor 2.0×). Three blinded radiologists evaluated the image quality and provided an anatomical assessment using a five-point Likert scale of 25 anatomical regions. Results: The total acquisition time of the TSE-Dixon sequence was reduced by 45 % from 13′38″ to 7′37″ (acceleration factor between 3.6× and 4.0×), the T2-mapping scan time was reduced by 31 % from 5′28″ to 3′47″ (acceleration factor of 3.0×), while maintaining comparable image quality. The results from the anatomical assessment of SENSE 2.0× versus CS-SENSE 3.6× were comparable in 88.7 % as shown by the 5-point Likert scale measurements. The T2-relaxation measurements had a good correlation of ρ = 0.7 between SENSE and CS-SENSE. Conclusion: We found an optimum acceleration factor with CS-SENSE between 3.6× and 4.0× for TSE-Dixon and 3.0× for T2-mapping sequences in a clinical MR imaging protocol of the ankle. The total scan time was reduced by 41 % while maintaining adequate image quality

Selected AI optimization techniques and applications in geotechnical engineering

AbstractIn an age of depleting earth due to global warming impacting badly on the ozone layer of the earth system, the need to employ technologies to substitute those engineering practices which result in emissions contributing to the death of our earth has arisen. One of those technologies is one that can sufficiently replace overdependence on laboratory activities where oxides of carbon and other toxins are released. Also, it is one technology that brings precision to other engineering activities especially earthwork design and construction thereby reducing to lower ebb the release of carbon oxides due to inexact utilization of materials during geotechnical practices. In this review, the use of artificial intelligence techniques in geotechnics has been explored as a precise technique through which geotechnical engineering works don’t impact on our planet due to precision. The intelligent learning algorithms of ANN, Fuzzy Logic, GEP, ANFIS, ANOVA and other nature-inspired algorithms have been reviewed as they are applied in the prediction of geotechnical and geoenvironmental problems and system. It is a complex exercise to conduct experimental protocols during the design and construction of earthwork infrastructures. Most times, such experimental exercises don’t meet the required condition for sustainable design and construction. At other times, certain errors as a result of experimental set up or human misjudgment may mar the accuracy of measurements and release unexpected emissions. The employment of the evolutionary learning methods has solved most of the lapses encountered in repeated laboratory measurements. So, in this review work, the relevant computational intelligent techniques employed at different times, under different laboratory protocols and utilizing different materials, have been presented as a comprehensive guide to future researchers in this innovative and evolving field of artificial intelligence. With this extensive review, a researcher would not have to look far to get a technical and state of the art guide in the utilization of various intelligent techniques that would enable engineering models in a more efficient, precise and more sustainable approach to forestall multiple practices that release carbon emissions into the environment