A method for strengthening damaged tubular steel T-joints under axial compression by wrapping them with carbon fiber-reinforced polymer (CFRP) sheets was proposed and evaluated. The influence of the CFRP strengthening on the failure mode and load capacity of T-joints with different degrees of damage was investigated using experiments and finite element analyses. Five T-joints were physically tested: one bare joint to obtain the peak load and corresponding displacement (D1m), two reinforced joints to provide a reference, and two pre-damaged then retrofitted joints to serve as the primary research objects. The ratio of the pre-loaded specimen chord displacement to the value of D1m was considered to be the degree of damage of the two retrofitted joints, and was set to 0.80 and 1.20. The results demonstrate that the maximum capacity of the retrofitted specimen was increased by 0.83%–15.06% over the corresponding unreinforced specimens. However, the capacity of the retrofitted specimen was 2.51%–22.77% lesser compared with that of the directly reinforced specimens. Next, 111 numerical analysis models (0.63≤b≤0.76, 9.70≤g≤16.92) were established to parametrically evaluate the effects of different geometric and strengthening parameters on the load capacity of strengthened tubular T-joints under different degrees of damage. The numerical analysis results revealed that the development of equivalent plastic strain at the selected measuring points was moderated by strengthening with CFRP wrapping, and indicated the optimal CFRP strengthening thickness and wrapping orientation according to tubular T-joint parameters. Finally, reasonable equations for calculating the load capacity of CFRP-strengthened joints were proposed and demonstrated to provide accurate results. The findings of this study can be used to inform improved CFRP strengthening of damaged tubular steel structures.
The loading capacity in the axial direction of a bolted thin steel plate was investigated. A refined numerical model of bolt was first constructed and then validated using existing experiment results. Parametrical analysis was performed to reveal the influences of geometric parameters, including the effective depth of the cap nut, the yield strength of the steel plate, the preload of the bolt, and shear force, on the ultimate loading capacity. Then, an analytical method was proposed to predict the ultimate load of the bolted thin steel plate. Results derived using the numerical and analytical methods were compared and the results indicated that the analytical method can accurately predict the pull-through capacity of bolted thin steel plates. The work reported in this paper can provide a simplified calculation method for the loading capacity in the axial direction of a bolt.
A constrained back propagation neural network (C-BPNN) model for standard penetration test based soil liquefaction assessment with global applicability is developed, incorporating existing knowledge for liquefaction triggering mechanism and empirical relationships. For its development and validation, a comprehensive liquefaction data set is compiled, covering more than 600 liquefaction sites from 36 earthquakes in 10 countries over 50 years with 13 complete information entries. The C-BPNN model design procedure for liquefaction assessment is established by considering appropriate constraints, input data selection, and computation and calibration procedures. Existing empirical relationships for overburden correction and fines content adjustment are shown to be able to improve the prediction success rate of the neural network model, and are thus adopted as constraints for the C-BPNN model. The effectiveness of the C-BPNN method is validated using the liquefaction data set and compared with that of several liquefaction assessment methods currently adopted in engineering practice. The C-BPNN liquefaction model is shown to have improved prediction accuracy and high global adaptability.
The most common index for representing structural condition of the pavement is the structural number. The current procedure for determining structural numbers involves utilizing falling weight deflectometer and ground-penetrating radar tests, recording pavement surface deflections, and analyzing recorded deflections by back-calculation manners. This procedure has two drawbacks: falling weight deflectometer and ground-penetrating radar are expensive tests; back-calculation ways has some inherent shortcomings compared to exact methods as they adopt a trial and error approach. In this study, three machine learning methods entitled Gaussian process regression, M5P model tree, and random forest used for the prediction of structural numbers in flexible pavements. Dataset of this paper is related to 759 flexible pavement sections at Semnan and Khuzestan provinces in Iran and includes “structural number” as output and “surface deflections and surface temperature” as inputs. The accuracy of results was examined based on three criteria of R, MAE, and RMSE. Among the methods employed in this paper, random forest is the most accurate as it yields the best values for above criteria (R=0.841, MAE=0.592, and RMSE=0.760). The proposed method does not require to use ground penetrating radar test, which in turn reduce costs and work difficulty. Using machine learning methods instead of back-calculation improves the calculation process quality and accuracy.
A series of comprehensive parametric studies are conducted on a steel-frame structure Finite-Element (FE) model with the Multangular-Pyramid Concave Friction System (MPCFS) installed as isolators. This new introduced MPCFS system has some distinctive features when compared with conventional isolation techniques, such as increased uplift stability, improved self-centering capacity, non-resonance when subjected to near-fault earthquakes, and so on. The FE model of the MPCFS is first established and evaluated by comparison between numerical and theoretical results. The MPCFS FE model is then incorporated in a steel-frame structural model, which is subjected to three chosen earthquakes, to verify its seismic isolation. Further, parametric study with varying controlling parameters, such as isolation foundation, inclination angle, friction coefficient, and earthquake input, is carried out to extract more detailed dynamic response of the MPCFS structure. Finally, limitations of this study are discussed, and conclusions are made. The simulations testify the significant seismic isolation of the MPCFS. This indicates the MPCFS, viewed as the beneficial complementary of the existing well-established and matured isolation techniques, may be a promising tool for seismic isolation of near-fault earthquake prone zones. This verified MPCFS FE model can be incorporated in future FE analysis. The results in this research can also guide future optimal parameter design of the MPCFS.
Research of reliability of engineering structures has experienced a developing history for more than 90 years. However, the problem of how to resolve the global reliability of structural systems still remains open, especially the problem of the combinatorial explosion and the challenge of correlation between failure modes. Benefiting from the research of probability density evolution theory in recent years, the physics-based system reliability researches open a new way for bypassing this dilemma. The present paper introduces the theoretical foundation of probability density evolution method in view of a broad background, whereby a probability density evolution equation for probability dissipative system is deduced. In conjunction of physical equations and structural failure criteria, a general engineering reliability analysis frame is then presented. For illustrative purposes, several cases are studied which prove the value of the proposed engineering reliability analysis method.
Shear stress distribution prediction in open channels is of utmost importance in hydraulic structural engineering as it directly affects the design of stable channels. In this study, at first, a series of experimental tests were conducted to assess the shear stress distribution in prismatic compound channels. The shear stress values around the whole wetted perimeter were measured in the compound channel with different floodplain widths also in different flow depths in subcritical and supercritical conditions. A set of, data mining and machine learning algorithms including Random Forest (RF), M5P, Random Committee, KStar and Additive Regression implemented on attained data to predict the shear stress distribution in the compound channel. Results indicated among these five models; RF method indicated the most precise results with the highest R2 value of 0.9. Finally, the most powerful data mining method which studied in this research compared with two well-known analytical models of Shiono and Knight method (SKM) and Shannon method to acquire the proposed model functioning in predicting the shear stress distribution. The results showed that the RF model has the best prediction performance compared to SKM and Shannon models.
A real-time vehicle monitoring is crucial for effective bridge maintenance and traffic management because overloaded vehicles can cause damage to bridges, and in some extreme cases, it will directly lead to a bridge failure. Bridge weigh-in-motion (BWIM) system as a high performance and cost-effective technology has been extensively used to monitor vehicle speed and weight on highways. However, the dynamic effect and data noise may have an adverse impact on the bridge responses during and immediately following the vehicles pass the bridge. The fast Fourier transform (FFT) method, which can significantly purify the collected structural responses (dynamic strains) received from sensors or transducers, was used in axle counting, detection, and axle weighing technology in this study. To further improve the accuracy of the BWIM system, the field-calibrated influence lines (ILs) of a continuous multi-girder bridge were regarded as a reference to identify the vehicle weight based on the modified Moses algorithm and the least squares method. In situ experimental results indicated that the signals treated with FFT filter were far better than the original ones, the efficiency and the accuracy of axle detection were significantly improved by introducing the FFT method to the BWIM system. Moreover, the lateral load distribution effect on bridges should be considered by using the calculated average ILs of the specific lane individually for vehicle weight calculation of this lane.
Superabsorbent Polymer (SAP) has emerged as a topic of considerable interest in recent years. The present study systematically and quantitively investigated the effect of SAP on hydration, autogenous shrinkage, mechanical properties, and microstructure of cement mortars. Influences of SAP on hydration heat and autogenous shrinkage were studied by utilizing TAM AIR technology and a non-contact autogenous shrinkage test method. Scanning Electron Microscope (SEM) was employed to assess the microstructure evolution. Although SAP decreased the peak rate of hydration heat and retarded the hydration, it significantly increased the cumulative heat, indicating SAP helps promote the hydration. Hydration promotion caused by SAP mainly occurred in the deceleration period and attenuation period. SAP can significantly mitigate the autogenous shrinkage when the content ranged from 0 to 0.5%. Microstructure characteristics showed that pores and gaps were introduced when SAP was added. The microstructure difference caused by SAP contributed to the inferior mechanical behaviors of cement mortars treated by SAP.
In recent years, there has been an increased interest in the use of fiber reinforced polymer (FRP) in the construction industry. However, the E-modulus and strength of such members at high service temperatures is still unknown. Modulus and strength of FRP at high service temperatures are highly required parameters for full design. The knowledge and application of this could lead to a cost effective and practical consideration in fire safety design. Thus, this paper proposes design methods for calculating the E-modulus and strength of FRP members at different temperatures. Experimental data from literature were normalized and compared with the results predicted by this method. It was found that the proposed design methods conservatively estimate the E-modulus and strength of FRP structural members. In addition, comparison was also made with direct references to the real behavior of materials. It was found to be satisfactory. Finally, an application is provided.