The presence of water films on a runway surface presents a risk to the landing of aircraft. The tire of the aircraft is separated from the runway due to a hydrodynamic force exerted through the water film, a phenomenon called hydroplaning. Although a lot of numerical investigations into hydroplaning have been conducted, only a few have considered the impact of the runway permeability. Hence, computational problems, such as excessive distortion and computing efficiency decay, may arise with such numerical models when dealing with the thin water film. This paper presents a numerical model comprising of the tire, water film, and the interaction with the runway, applying a mathematical model using the smoothed particle hydrodynamics and finite element (SPH-FE) algorithm. The material properties and geometric features of the tire model were included in the model framework and water film thicknesses from 0.75 mm to 7.5 mm were used in the numerical simulation. Furthermore, this work investigated the impacts of both surface texture and the runway permeability. The interaction between tire rubber and the rough runway was analyzed in terms of frictional force between the two bodies. The SPH-FE model was validated with an empirical equation proposed by the National Aeronautics and Space Administration (NASA). Then the computational efficiency of the model was compared with the traditional coupled Eulerian-Lagrangian (CEL) algorithm. Based on the SPH-FE model, four types of the runway (Flat, SMA-13, AC-13, and OGFC-13) were discussed. The simulation of the asphalt runway shows that the SMA-13, AC-13, and OGFC-13 do not present a hydroplaning risk when the runway permeability coefficient exceeds 6%.
Training samples for deep learning networks are typically obtained through various field experiments, which require significant manpower, resource and time consumption. However, it is possible to utilize simulated data to augment the training samples. In this paper, by comparing the actual experimental model with the simulated model generated by the gprMax [1] forward simulation method, the feasibility of obtaining simulated samples through gprMax simulation is validated. Subsequently, the samples generated by gprMax forward simulation are used for training the network to detect objects in existing real samples. At the same time, aiming at the detection and intelligent recognition of road sub-surface defects, the Swin-YOLOX algorithm is introduced, and the excellence of the detection network, which is improved by augmenting the simulated samples with real samples, is further verified. By comparing the prediction performance of the object detection models, it is observed that the model trained with mixed samples achieved a recall of 94.74% and a mean average precision (
Automatic detection and assessment of surface cracks are beneficial for understanding the mechanical performance of ultra-high performance concrete (UHPC). This study detects crack evolution using a novel dynamic mode decomposition (DMD) method. In this method, the sparse matrix ‘determined’ from images is used to reconstruct the foreground that contains cracks, and the global threshold method is adopted to extract the crack patterns. The application of the DMD method to the three-point bending test demonstrates the efficiency in inspecting cracks with high accuracy. Accordingly, the geometric features, including the area and its projection in two major directions, are evaluated over time. The relationship between the geometric properties of cracks and load-displacement curves of UHPC is discussed. Due to the irregular shape of cracks in the spatial domain, the cracks are then transformed into the Fourier domain to assess their development. Results indicate that crack patterns in the Fourier domain exhibit a distinct concentration around a central position. Moreover, the power spectral density of cracks exhibits an increasing trend over time. The investigation into crack evolution in both the spatial and Fourier domains contributes significantly to elucidating the mechanical behavior of UHPC.
This study investigated the application of electrical resistance tomography (ERT) in characterizing the slurry spatial distribution in cemented granular materials (CGMs). For CGM formed by self-flow grouting, the voids in the accumulation are only partially filled and the bond strength is often limited, which results in difficulty in obtaining in situ samples for quality evaluation. Therefore, it is usually infeasible to evaluate the grouting effect or monitor the slurry spatial distribution by a mechanical method. In this research, the process of grouting cement paste into high alumina ceramic beads (HACB) accumulation is reliably monitored with ERT. It shows that ERT results can be used to calculate the cement paste volume in the HACB accumulation, based on calibrating the saturation exponent n in Archie’s law. The results support the feasibility of ERT as an imaging tool in CGM characterization and may provide guidance for engineering applications in the future.
This study evaluated the feasibility of using polypropylene fiber (PF) as reinforcement in improving tensile strength behavior of cement-stabilized dredged sediment (CDS). The effects of cement content, water content, PF content and length on the tensile strength and stress–strain behavioral evolutions were evaluated by conducting splitting tensile strength tests. Furthermore, the micro-mechanisms characterizing the tensile strength behavior inside PF-reinforced CDS (CPFDS) were clarified via analyzing macro failure and microstructure images. The results indicate that the highest tensile strengths of 7, 28, 60, and 90 d CPFDS were reached at PF contents of 0.6%, 1.0%, 1.0%, and 1.0%, exhibiting values 5.96%, 65.16%, 34.10%, and 35.83% higher than those of CDS, respectively. Short, 3 mm, PF of showed the best reinforcement efficiency. The CPFDS exhibited obvious tensile strain-hardening characteristic, and also had better ductility than CDS. The mix factor (CCa/Cwb) and time parameter (qt0(t)) of CDS, and the reinforcement index (kt-PF) of CPFDS were used to establish the tensile strength prediction models of CDS and CPFDS, considering multiple factors. The PF “bridge effect” and associated cementation-reinforcement coupling actions inside CPFDS were mainly responsible for tensile strength behavior improvement. The key findings contribute to the use of CPFDS as recycled engineering soils.
The paper introduces a novel approach for detecting structural damage in full-scale structures using surrogate models generated from incomplete modal data and deep neural networks (DNNs). A significant challenge in this field is the limited availability of measurement data for full-scale structures, which is addressed in this paper by generating data sets using a reduced finite element (FE) model constructed by SAP2000 software and the MATLAB programming loop. The surrogate models are trained using response data obtained from the monitored structure through a limited number of measurement devices. The proposed approach involves training a single surrogate model that can quickly predict the location and severity of damage for all potential scenarios. To achieve the most generalized surrogate model, the study explores different types of layers and hyperparameters of the training algorithm and employs state-of-the-art techniques to avoid overfitting and to accelerate the training process. The approach’s effectiveness, efficiency, and applicability are demonstrated by two numerical examples. The study also verifies the robustness of the proposed approach on data sets with sparse and noisy measured data. Overall, the proposed approach is a promising alternative to traditional approaches that rely on FE model updating and optimization algorithms, which can be computationally intensive. This approach also shows potential for broader applications in structural damage detection.
The use of prefabricated vertical drains (PVD) in liquefiable deposits is gaining attention due to enhanced drainage. However, investigations on PVD in mitigating re-liquefaction during repeated shaking events are not available. This study performed a series of shaking table experiments on untreated and PVD-treated specimens prepared with 40% and 60% relative density. Repeated sinusoidal loading was applied with an incremental peak acceleration of 0.1g, 0.2g, 0.3g, and 0.4g, at 5 Hz shaking frequency with 40 s duration. The performance of treated ground was evaluated based on the generation and dissipation of excess pore water pressure (EPWP), induced sand densification, subsidence, and cyclic stress ratio. In addition, the strain accumulated in fresh and exhumed PVD was investigated using geotextile tensile testing apparatus aided with digital image correlation. No evidence of pore pressure was reported up to 0.2g peak acceleration for 40% and 60% relative density specimens. The continuous occurrence of soil densification and drainage medium restrained and delayed the generation of EPWP and expedited the dissipation process. This study demonstrates PVD can mitigate re-liquefaction, without suffering from deterioration, when subjected to medium to high intense repeated shaking events.
Fault movement during earthquakes is a geotechnical phenomenon threatening buried pipelines and with the potential to cause severe damage to critical infrastructures. Therefore, effective prediction of pipe displacement is crucial for preventive management strategies. This study aims to develop a fast, hybrid model for predicting vertical displacement of pipe networks when they experience faulting. In this study, the complex behavior of soil and a buried pipeline system subjected to a normal fault is analyzed by using an artificial neural network (ANN) to generate predictions the behavior of the soil when different parameters of it are changed. For this purpose, a finite element model is developed for a pipeline subjected to normal fault displacements. The data bank used for training the ANN includes all the critical soil parameters (cohesion, internal friction angle, Young’s modulus, and faulting). Furthermore, a mathematical formula is presented, based on biases and weights of the ANN model. Experimental results show that the maximum error of the presented formula is 2.03%, which makes the proposed technique efficiently predict the vertical displacement of buried pipelines and hence, helps to optimize the upcoming pipeline projects.
Due to the complicated three-dimensional behaviors and testing limitations of reinforced concrete (RC) members in torsion, torsional mechanism exploration and torsional performance prediction have always been difficult. In the present paper, several machine learning models were applied to predict the torsional capacity of RC members. Experimental results of a total of 287 torsional specimens were collected through an overall literature review. Algorithms of extreme gradient boosting machine (XGBM), random forest regression, back propagation artificial neural network and support vector machine, were trained and tested by 10-fold cross-validation method. Predictive performances of proposed machine learning models were evaluated and compared, both with each other and with the calculated results of existing design codes, i.e., GB 50010, ACI 318-19, and Eurocode 2. The results demonstrated that better predictive performance was achieved by machine learning models, whereas GB 50010 slightly overestimated the torsional capacity, and ACI 318-19 and Eurocode 2 underestimated it, especially in the case of ACI 318-19. The XGBM model gave the most favorable predictions with R2 = 0.999, RMSE = 1.386, MAE = 0.86, and
In this work, a novel refined higher-order shear deformation plate theory is integrated with nonlocal elasticity theory for analyzing the free vibration, bending, and transient behaviors of fluid-infiltrated porous metal foam piezoelectric nanoplates resting on Pasternak elastic foundation with flexoelectric effects. Isogeometric analysis (IGA) and the Navier solution are applied to the problem. The innovation in the present study is that the influence of the in-plane variation of the nonlocal parameter on the free and forced vibration of the piezoelectric nanoplates is investigated for the first time. The nonlocal parameter and material characteristics are assumed to be material-dependent and vary gradually over the thickness of structures. Based on Hamilton’s principle, equations of motion are built, then the IGA approach combined with the Navier solution is used to analyze the static and dynamic response of the nanoplate. Lastly, we investigate the effects of the porosity coefficients, flexoelectric parameters, elastic stiffness, thickness, and variation of the nonlocal parameters on the mechanical behaviors of the rectangular and elliptical piezoelectric nanoplates.
