Frontiers of Structural and Civil Engineering最新文献

This paper presents an analytical approach for estimating frictional resistance to pipe movement at soil and external pipe surface of buried coated pressurized steel pipes relative to the internal thrust force. The proposed analytical method was developed based on 36 experiments, which involved three coating types (cement mortar (CM), polyurethane type-I (PT-I), prefabricated plastic tape (PPT)) on pipes’ surfaces, three different soils (pea-gravel (PG), sand (S), silty-clay (SC)), and four simulated over burden depths above the pipe’s crown. Investigation showed frictional resistance decreased with increasing over burden depth above the pipe’s crown. The degree of frictional resistance at the pipe-soil interface was found to be in the order of PG > SC > S for all coating variations and overburden depths. CM coated pipe buried in all three types of soil produced significantly higher frictional resistance as compared to other coating types. Based on experimental data, the developed analytical introduced a dimensionless factor “Z”, which included effects of types of coatings, soil, and overburden depths for simplified rapid calculation. Analysis showed that the method provided a better prediction of frictional resistance forces, in comparison to previous analytical methods, which were barely close in predicting friction resistance for different coating variations, soil types, and overburden depths. Friction resistance force values reported herein could be considered conservative.

In tunnel construction with tunnel boring machines (TBMs), accurate prediction of the remaining useful life (RUL) of disc cutters is critical for timely maintenance and replacement to avoid delays and cost overruns. This paper introduces a novel hybrid model, integrating fundamental and data-driven approaches, to enhance wear prediction of TBM disc cutters and enable accurate RUL estimation. The fundamental model is improved by incorporating composite wear mechanisms and load estimation techniques, showcasing superior prediction accuracy compared to single-mechanism models. Additionally, the hybrid model innovatively incorporates a data-driven supplementary residual term into the improved fundamental model, leading to a high-performance wear prediction model. Using actual field data from a highway tunnel project in Shenzhen, the performance of the hybrid model is rigorously tested and compared with pure fundamental and data-driven models. The hybrid model outperforms the other models, achieving the highest accuracy in predicting TBM disc cutter wear (mean absolute error (MAE) = 0.53, root mean square error (RMSE) = 0.64). Furthermore, this study thoroughly analyzes the hybrid model’s generalization capability, revealing significant impacts of geological conditions on prediction accuracy. The model’s generalization capability is also improved by expanding and updating the data sets. The RUL estimation results provided by the hybrid model are straightforward and effective, making it a valuable tool by which construction staff can monitor TBM disc cutters.

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.

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 inplane 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.

In engineering applications, concrete crack monitoring is very important. Traditional methods are of low efficiency, low accuracy, have poor timeliness, and are applicable in only a limited number of scenarios. Therefore, more comprehensive detection of concrete damage under different scenarios is of high value for practical engineering applications. Digital image correlation (DIC) technology can provide a large amount of experimental data, and neural network (NN) can process very rich data. Therefore, NN, including convolutional neural networks (CNN) and back propagation neural networks (BP), can be combined with DIC technology to analyze experimental data of three-point bending of plain concrete and four-point bending of reinforced concrete. In addition, strain parameters can be used for training, and displacement parameters can be added for comprehensive consideration. The data obtained by DIC technology are grouped for training, and the recognition results of NN show that the combination of strain and displacement parameters, i.e., the response of specimen surface and whole body, can make results more objective and comprehensive. The identification results obtained by CNN and BP show that these technologies can accurately identify cracks. The identification results for reinforced concrete specimens are less affected by noise than those of plain concrete specimens. CNN is more convenient because it can identify some features directly from images, recognizing the cracks formed by macro development. BP can issue early warning of the microscopic cracks, but it requires a large amount of data and computation. It can be seen that CNN is more intuitive and efficient in image processing, and is suitable when low accuracy is adequate, while BP is suitable for occasions with greater accuracy requirements. The two tools have advantages in different situations, and together they can play an important role in engineering monitoring.

Face passive failure can severely damage existing structures and underground utilities during shallow shield tunneling, especially in coastal backfill sand. In this work, a series of laboratory model tests were developed and conducted to investigate such failure, for tunnels located at burial depth ratios for which C/D = 0.5, 0.8, 1, and 1.3. Support pressures, the evolution of failure processes, the failure modes, and the distribution of velocity fields were examined through model tests and numerical analyses. The support pressure in the tests first rose rapidly to the elastic limit and then gradually increased to the maximum value in all cases. The maximum support pressure decreased slightly in cases where C/D = 0.8, 1, and 1.3, but the rebound was insignificant where C/D = 0.5. In addition, the configuration of the failure mode with C/D = 0.5 showed a wedge-shaped arch, which was determined by the outcropping shear failure. The configuration of failure modes was composed of an arch and the inverted trapezoid when C/D = 0.8, 1, and 1.3, in which the mode was divided into lower and upper failure zones.

Researchers are paying increasing attention to the development of low-cost and microcontroller-based accelerometers, in order to make structural health monitoring feasible for conventional bridges with limited monitoring budget. Parallel with the low-cost sensor development, the use of the embedded accelerometers of smartphones for eigenfrequency analysis of bridges is becoming popular in the civil engineering literature. This paper, for the first time in the literature, studies these two promising technologies by comparing the noise density and eigenfrequency analysis of a self-developed, validated and calibrated low-cost Internet of things based accelerometer LARA (low cost adaptable reliable accelerometer) with those of a state of the art smartphone (iPhone XR). The eigenfrequency analysis of a footbridge in San Sebastian, Spain, showed that the embedded accelerometer of the iPhone XR can measure the natural frequencies of the under study bridge.

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 (C_C^a/C_w^b) and time parameter (q_t0(t)) of CDS, and the reinforcement index (k_t-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 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.

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.