The high variability in turbulence is a significant feature of the realistic atmospheric boundary layer winds which might have strong effects on wind loads on structures submerged in atmospheric boundary layer. This article has been devoted to this matter of science which is of practical importance to wind-engineering design and research. First, the variation of the turbulence intensity of the atmospheric boundary layer flow has been studied using theoretical calculations and meteorological wind measurements. Second, the effects of free-stream turbulence on wind loads on circular cylindrical structures have been revealed at high Reynolds number and equivalent conditions based on field measurements and wind tunnel model tests for wind effects on a large cooling tower. Through these works, it is found that the turbulence intensity for the measured atmospheric boundary layer winds is highly variable due to the significant effect of the mean wind speed, which is not well represented by the traditional empirical formulae. Besides, the free-stream turbulence significantly influences the dynamic characteristics of wind effects on the cooling tower in most cases, and the wind effects for a flow field of high turbulence intensity are generally more unfavorable than those for a flow field of low turbulence intensity.
Wind effects on structures obtained by field measurements are often found to be nonstationary, but related researches shared by the wind-engineering community are still limited. In this paper, empirical mode decomposition (EMD) is applied to the nonstationary wind pressure time-history samples measured on an actual 167-meter high large cooling tower. It is found that the residue and some intrinsic mode functions (IMFs) of low frequencies produced by EMD are responsible for the samples’ nonstationarity. Replacing the residue by the constant mean and subtracting the IMFs of low frequencies can help the nonstationary samples become stationary ones. A further step is taken to compare the loading characteristics extracted from the original nonstationary samples with those extracted from the processed stationary samples. Results indicate that nonstationarity effects on wind loads are notable in most cases. The passive wind tunnel simulation technique based on the assumption of stationarity is also examined, and it is found that the technique is basically conservative for use.
Abstract:Full-scale/model test comparison studies to validate the traditional atmospheric boundary layer (ABL) wind tunnel simulation technique are reviewed. According to the literature review, notable discrepancies between full-scale measurement results and model test results were observed by most performed comparison studies, but the causes of the observed discrepancies were not revealed in a scientific way by those studies. In this regard, a new research scheme for future full-scale/model test comparison studies is proposed in this article, which utilizes the multiple-fan actively controlled wind tunnel simulation technique. With the new research scheme, future full-scale/model test comparison studies are expected to reasonably disclose the main problems with the traditional ABL wind tunnel simulation technique, and the technique can be improved correspondingly.Keywords: comparison study; full-scale measurement; wind tunnel model test; multiple-fan actively controlled wind tunnel; research scheme IntroductionAs a mature technique applied in aviation industry, the wind tunnel model test was introduced into the field of wind-engineering research and design in 1960s. After Jensen [1] proposed that the flow field simulated in the wind tunnel should be similar to the actual atmospheric boundary layer (ABL) flow field for wind-engineering model tests, simulating ABL has become an indispensable test procedure. To fulfill the task of simulating realistic ABL turbulent flow fields in the wind tunnel, passive simulation devices, including spires, roughness elements, grids and barriers, can jointly be used. It has been found that the target flow fields can be obtained by adjusting the position and the number of some devices placed in the beginning of the wind tunnel [2,3]. During the past 50 years, most scientific researches and engineering practices made by the wind-engineering community utilized the traditional ABL wind tunnel simulation technique. With this technique, theoretical achievements were made, and numerous engineering structures' safety against extreme wind events was ensured.
In the field of structural health monitoring (SHM), with the mature development of artificial intelligence, deep learning-based structural damage identification techniques have attracted wide attention. In this paper, the convolutional neural network (CNN) is used to extract the damage feature of simple supported steel beams. Firstly, the transient dynamic analysis of the steel beam is carried out by finite element software, and the acceleration response signals under different damage scenarios are obtained. Then, the acceleration response signal is decomposed by wavelet packet decomposition (WPD) to extract the wavelet packet band energy ratio variation (ERV) index as the training sample of CNN. Subsequently, the vibration experiment of a simple supported steel beam was carried out, and the results were compared with the numerical simulation results. The characteristic indexes were obtained by making corresponding changes to the vibration signal, and then, the experimental data were input into the CNN to predict the effect of damage detection. The results show that the method can successfully detect the intact structure, single damage, and multiple damages with an accuracy of 95.14% under impact load, and the performance is better than that of support vector machine (SVM), with good robustness.
Defects in different positions and degrees in pile foundations will affect the building structure’s safety and the foundation’s bearing capacity. The efficiency and accuracy of using traditional methods to identify multi-defect types of pile foundations are very low, so finding suitable methods to improve their related indicators for pile foundation safety and engineering applications is necessary. In this paper, under the condition of secondary development of finite element software ABAQUS to obtain the time-domain signal database of six kinds of multi-defect pile foundations, a multi-defect type identification method of pile foundations based on two-channel convolutional neural network (TC-CNN) and low-strain pile integrity test (LSPIT) is proposed. Firstly, simulated time-domain signals of the dynamic measurements that match the experimental results performed wavelet packet denoising. Secondly, the 1D time-domain signals before and after denoising and the corresponding 2D wavelet time–frequency maps are inputs to retain more data information and prevent overfitting. Finally, TC-CNN achieved the multi-defect type identification of concrete piles. Compared with the single-channel convolutional neural network, this method can effectively fuse 1D and 2D features, extract more potential features, and make the classification accuracy reach 99.17%.
If the layer of soil surrounding a pile is not taken into account during the engineering detection process, the velocity-time curve might show asymptotic diameter shrinkage or diameter expanding features, which would alter the interpretation of the test findings. In this study, we suggest combining multi-feature extraction and a convolutional neural network (CNN) to increase accuracy in pile defect recognition for layered soil conditions and traditional deep learning flaws. First, numerical simulations are run to create velocity–time curves for foundation piles under layered soil conditions. Then, the data are extracted from three dimensions: time domain, frequency domain, and time-frequency domain, respectively, and fused into a set of feature vectors. Finally, a foundation pile defect identification model combining multi-scale features and CNN is established. The findings demonstrate that the CNN model has 97.8% accuracy while the PNN has 28.6% accuracy, demonstrating that the approach is very reliable.
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