KSCE Journal of Civil Engineering最新文献

In-situ testing programs are conducted to evaluate the potential use of the light weight deflectometer (LWD) device for measuring the in-situ deformation modulus of subgrade soil layers stabilized with geosynthetic reinforcement. A series of in-situ field tests are carried out on six test sections that include 1) unstabilized subgrade soil and 2) geogrid- and geocell-reinforced stabilized subgrade soil. Field measurements on the modulus improvement factor (MIF) of stabilized subgrades provide more practical and realistic results. The MIF value depends on the type, geometry, location of geosynthetic reinforcements, and characteristics of subgrade soil. An accurate and quick evaluation of MIF can help in the timely design and execution of new road networks. The novelty of the study comprises of measuring the in-situ MIF of geosynthetic stabilized subgrade soil using a light weight deflectometer (LWD) device and comparing the results with the in-situ plate load test (PLT) and falling weight deflectometer (FWD) devices for the considered test configurations. The deformation modulus from LWD test demonstrated a similar trend to the modulus values obtained from PLT and FWD. The improved in-situ deformation modulus from three different tests (E_PLT, E_LWD, and E_FWD) are found to be 29.5 MPa, 34.5 MPa and 114.8 MPa for geocell; 21.1 MPa, 25.7 MPa and 86.2 MPa for biaxial geogrid; 37.2 MPa, 29.7 MPa and 89.5 MPa for triaxial geogrid, when the geosynthetic reinforcement is embedded at a depth of 100 mm. In addition, the MIF values of geosynthetic stabilized subgrade soil for the considered test sections are found to be in the range of 1.0 to 2.5.

Based on the monitoring temperature field and bearing displacement data of a single-tower cable-stayed bridge, the changing trends of temperatures, temperature differences and displacements are analyzed, and then the correlations between bearing displacements and temperatures as well as temperature differences are analyzed in long-term and short-term periods; furthermore, a time-varying multivariate linear regression model for simulation of temperature-induced displacements is put forward, and the Kalman filtering technique is employed to achieve the accurate values of time-varying coefficients in this model; Finally, the modeling accuracy is verified and compared with the traditional multiple linear model. The results show that the temperature-induced displacements are not only affected by uniform temperature but also affected by gradient temperatures, which should be fully considered during time-varying multiple linear regression modeling; the correlations between bearing displacements and temperatures shows a good linear relationship over a long period of time (such as in several months), and shows obvious nonlinear relationship over a short period of time (such as in one day), indicating that the correlation in different time scales is different; the time-varying multiple linear regression model considering not only the influence of uniform temperature and gradient temperature but also the linear and nonlinear correlations demonstrates better modeling accuracy, with errors of only 0.77%, 2.35%, and 2.58% for daily, monthly, and quarterly data, respectively, and the simulated values of bearing displacements are very close to the measured values, with the root mean square errors of only 0.8479 and 0.7149, indicating that the proposed time-varying multiple linear regression model has a good simulation accuracy of bearing displacements.

This study proposes a framework for developing an automated compliance checking (ACC) system that supports the verification of structural calculations in openBIM format. The framework consists of four main aspects: 1) classification of design codes depending on structural components and limit states to be checked; 2) preparation of a structural model according to Industry Foundation Classes; 3) interpretation of conditions in design codes into a machine-readable rule language; and 4) design of an ACC system, with a focus on the rule flow in the structural design code. The framework was demonstrated through a detailed example of an ACC system using ifcOWL, SWQRL, and the Drools inference engine. The ACC system was developed to check the strength and serviceability of a prestressed concrete girder bridge and its components. The developed ACC system verified the actual design example. The proposed framework can help develop ACC systems in structural engineering and can be customized to meet user requirements and accommodate various data sources and design codes.

Tunnels that cross active faults will inevitably be severely damaged, and there are mainly five fault types. There are five main fault types: strike-slip fault, normal fault, reverse fault, and oblique-slip fault (normal or reverse strike-slip fault). However, there is no calculation method of tunnel longitudinal mechanical analysis for all fault types, and the calculation accuracy is reduced by the assumptions used in the existing calculation models to simplify the solution of complex differential equations. In pursuit of this objective, this study presents a novel semi-analytical model that accounts for five distinct types of faults and analyzes complex mathematical problems via the finite difference method, thereby circumventing the need to derive intricate analytical solutions. Additionally, an unconventional iterative approach is suggested for the computation of the nonlinear interaction between the tunnel and soil. This method exhibits exceptional efficiency, requiring less than one second per calculation on a laptop. Furthermore, when compared to a numerical model based on finite elements and varying fault displacements, this model demonstrates that the longitudinal forces and displacements are quantitatively in good approval, even when massive fault displacements are considered. Finally, this model is utilized to assess the longitudinal displacements, forces, and safety factors of the Daliang tunnel under faulting, and the failure range and failure modes are consistent with the actual situation. The suggested approach addresses a gap in the existing literature and is valuable for quickly, cost-effectively, and stably analyzing and designing tunnels intersecting with active faults.

A full-scale experimental study on the mechanical properties of a 35 m prestressed concrete trough girder was carried out under simply supported supports. The load-displacement curve and cracking load of the test girder were obtained. In order to study the transverse crack resistance of single-line trough girder, a nonlinear finite element model was established by using ABAQUS. The accuracy of the finite element model was verified by comparing with the experimental results. The verified finite element model was used to study the crack resistance of the trough girder under different transverse prestressed reinforcement schemes, and then the reasonable reinforcement form of transverse prestress of the trough girder was discussed. The results show that for the test girder, the model without transverse prestressed reinforcement does not meet the requirements of Chinese code. It is very necessary for single-line trough girder to install transverse prestressed reinforcement, and the spacing of transverse prestressed reinforcement can be reduced from 2 m to 1.25 m.

It is widely recognized that critical crack angle θ is a pre-requisite to calculate the shear capacity of RC elements in traditional modified compression field theory (MCFT), and it is often determined by an iterative calculation or by presupposing an empirical value. This study proposes a straightforward solution of critical crack angle aided by machine learning. Firstly, 215 reinforced concrete T-girder test samples are collected from published literatures, and are analyzed by traditional MCFT to determine their shear capacity and their corresponding critical compressive strain of the upper edge concrete ε_tom and the critical crack angle θ. Subsequently, integrated BP (back propagation) neural network models are established to seek for a quantitative regression between the iteratively obtained θ, ε_tom and other MCFT input parameters. Finally, the obtained regression equations are incorporated into traditional MCFT framework to determine the shear capacity straightforwardly. The results indicate that critical crack angle θ of reinforced concrete T-girder exponentially grows by increasing the strength eigenvalue ρ_v·f_yv/f_c or decreasing the longitudinal reinforcement ratio ρ_l. While the compressive strain of concrete in the compression region ε_tom exhibits a logarithmic function with the strength eigenvalue ρ_v·f_yv/f_c and the shear span ratio λ. The proposed straightforward calculation approach is superior to other methods both in efficiency and accuracy. Specifically, the goodness-of-fit of the proposed approach is 1.7-fold higher than that of the American ACI318-14, and the coefficient of variation is reduced by 43% compared to the European EN 1992-1-1;2004.

A new type of static load test system and test method based on steel screw anchor pile is proposed. Through field uplift tests of single pile and pile group, the influence of soil properties, pile length and other factors on the uplift bearing capacity of steel screw pile is analyzed. The influence range of steel screw pile on the displacement of adjacent soil and pile foundation are revealed. The calculation method of ultimate uplift bearing capacity of single steel screw anchor pile is established. The reliability of the test method is verified by practical cases. The test results show that the uplift bearing capacity of steel screw pile is higher than that of straight rod pile. According to the properties and compactness of the soil, the coefficient of uplift side resistance improvement of steel screw piles can be taken as 1.2 – 1.6. The construction of steel screw pile is convenient and can be reused, without special treatment of the test site. The test method can save up to 70% of the cost compared with traditional surcharge test method, and greatly improve the safety of large-tonnage static load test.

Quick and accurate acquisition of the uniaxial compressive strength (UCS) of the surrounding rock at the tunnel face effectively ensures the safety of tunnel construction. This paper proposes a model for estimating the USC of the tunnel surrounding rock based on boreholes measurement-while-drilling data and stacking ensemble algorithm. Firstly, four original drilling parameters of hammer pressure (P_h), feed pressure (P_f), rotation pressure (P_r), and feed speed (V_p) as well as the rock UCS are collected from 1489 rock UCS test cases. Then, data cleaning and feature extension are carried out, and a UCS estimation database containing 66 features of the drilling parameters is established. Furthermore, traditional machine learning algorithms (SVM, KNN, RF, ET, GB, Bag), Bayesian optimization, cross-validation, and staking ensemble algorithms are employed to build a rock UCS estimation model. The performance of six traditional and integrated machine learning models is comparatively analyzed. The R², RMSE and MAE of the prediction set are used as model performance evaluation metrics. The results show that the ensemble model performs best with an R² of 87.9%. Finally, the reliability of the model is verified by field tests. Compared with the traditional field UCS testing method, this method can accurately and quickly predict the UCS of rocks without additional manpower and material resources, which possesses a greater application prospect.

The optimal design of structures subjected to seismic loading poses significant challenges due to the presence of high nonlinearity and computational complexity. To address these challenges, this paper presents a novel methodology that combines Sequential Quadratic Programming with Trust-Region strategy (SQP-TR) and Endurance Time Method (ETM). SQP-TR is initially presented as a numerical optimization approach to address optimization problems by linearizing the constraints and approximating the objective function with Taylor expansion, as well as employing the filter method and trust region strategy to ensure convergence and feasibility. A five-story linear frame validates its effectiveness and demonstrates promising outcomes. ETM is successfully implemented as a seismic analysis approach to perform nonlinear time history analyses in order to capture the dynamic input feature of the seismic load and evaluate the nonlinear dynamic behaviors of structures. Its practical application is demonstrated by a nine-story structure with nonlinearity, which shows satisfactory results. Finally, the proposed methodology is applied to optimize a twelve-story three-Dimensional (3D) Reinforced Concrete (RC) nonlinear building under seismic load, and the results demonstrate that the method can accomplish optimal seismic design with high accuracy and efficiency.

Currently, most of the concrete crack detection models proposed mainly rely on a single deep learning method, whose performance is limited. To solve the problem, this work presents a deep learning framework for crack identification of concrete. First, a histogram equalization method is adopted to processed the original image, which can effectively enhance the contrast and brightness. Then, to extract effective features of the crack, multiple filters are employed for crack detection, which fusion with original data. In addition, the Unet network is employed as the base classifier for initial diagnosis of concrete crack. To raise the extraction precision, enhanced attention mechanism module is applied to the Unet model for parameter optimization. The combination of Dice function and cross-entropy loss function is applied to evaluate the model performance. The voting integration algorithm is utilized to each prediction result for the decision of the final prediction result. Finally, to demonstrate the effectiveness of the proposed method, a total of 608 steel fiber concrete crack images are collected from laboratory. The results indicate that the proposed deep learning framework offers the optimal comprehensive recognition performance.