Owing to advancement in advanced manufacturing technology, the reinforcement design of concrete structures has become an important topic in structural engineering. Based on bi-directional evolutionary structural optimization (BESO), a new approach is developed in this study to optimize the reinforcement layout in steel-reinforced concrete (SRC) structures. This approach combines a minimum compliance objective function with a hybrid truss-continuum model. Furthermore, a modified bi-directional evolutionary structural optimization (M-BESO) method is proposed to control the level of tensile stress in concrete. To fully utilize the tensile strength of steel and the compressive strength of concrete, the optimization sensitivity of steel in a concrete–steel composite is integrated with the average normal stress of a neighboring concrete. To demonstrate the effectiveness of the proposed procedures, reinforcement layout optimizations of a simply supported beam, a corbel, and a wall with a window are conducted. Clear steel trajectories of SRC structures can be obtained using both methods. The area of critical tensile stress in concrete yielded by the M-BESO is more than 40% lower than that yielded by the uniform design and BESO. Hence, the M-BESO facilitates a fully digital workflow that can be extremely effective for improving the design of steel reinforcements in concrete structures.
The interactions between reinforced concrete (RC) frames and infill walls play an important role in the seismic response of frames, particularly for low-rise frames. Infill walls can increase the overall lateral strength and stiffness of the frame owing to their high strength and stiffness. However, local wall-frame interactions can also lead to increased shear demand in the columns owing to the compressive diagonal strut force from the infill wall, which can result in failure or in serious situations, collapse. In this study, the effectiveness of a design strategy to consider the complex infill wall interaction was investigated. The approach was used to design example RC frames with infill walls in locations with different seismicity levels in Thailand. The performance of these frames was assessed using nonlinear static, and dynamic analyses. The performance of the frames and the failure modes were compared with those of frames designed without considering the infill wall or the local interactions. It was found that even though the overall responses of the buildings designed with and without consideration of the local interaction of the infill walls were similar in terms the overall lateral strength, the failure modes were different. The proposed method can eliminate the column shear failure from the building. Finally, the merits and limitations of this approach are discussed and summarized.
In this study, the flexural and longitudinal shear performances of two types of precast lightweight steel–ultra-high performance concrete (UHPC) composite beams are investigated, where a cluster UHPC slab (CUS) and a normal UHPC slab (NUS) are connected to a steel beam using headed studs through discontinuous shear pockets and full-length shear pockets, respectively. Results show that the longitudinal shear force of the CUS is greater than that of the NUS, whereas the interfacial slip of the former is smaller. Owing to its better integrity, the CUS exhibits greater flexural stiffness and a higher ultimate bearing capacity than the NUS. To further optimize the design parameters of the CUS, a parametric study is conducted to investigate their effects on the flexural and longitudinal shear performances. The square shear pocket is shown to be more applicable for the CUS, as the optimal spacing between two shear pockets is 650 mm. Moreover, a design method for transverse reinforcement is proposed; the transverse reinforcement is used to withstand the splitting force caused by studs in the shear pocket and prevent the UHPC slab from cracking. According to calculation results, the transverse reinforcement can be canceled when the compressive strength of UHPC is 150 MPa and the volume fraction of steel fiber exceeds 2.0%.
The aim of this study is to appraise the potential of calcium sulfoaluminate (CSA) cement-based grouts in simulated permafrost environments. The hydration and performance of CSA cement-based grouts cured in cold environments (10, 0, and −10 °C) are investigated using a combination of tests, including temperature recording, X-ray diffraction (XRD) tests, thermogravimetric analysis (TGA), and unconfined compressive strength (UCS) tests. The recorded temperature shows a rapid increase in temperature at the early stage in all the samples. Meanwhile, results of the TGA and XRD tests show the generation of a significant quantity of hydration products, which indicates the rapid hydration of CSA cement-based grouts at the early stage at low temperatures. Consequently, the CSA cement-based grouts exhibit remarkably high early strength. The UCS values of the samples cured for 2 h at −10, 0, and 10 °C are 6.5, 12.0, and 12.3 MPa, respectively. The UCS of the grouts cured at −10, 0, and 10 °C increases continuously with age and ultimately reached 14.9, 19.0, and 30.6 MPa at 28 d, respectively. The findings show that the strength of grouts fabricated using CSA cement can develop rapidly in cold environments, thus rendering them promising for permafrost applications.
An algorithm based on deep semantic segmentation called LC-DeepLab is proposed for detecting the trends and geometries of cracks on tunnel linings at the pixel level. The proposed method addresses the low accuracy of tunnel crack segmentation and the slow detection speed of conventional models in complex backgrounds. The novel algorithm is based on the DeepLabv3+ network framework. A lighter backbone network was used for feature extraction. Next, an efficient shallow feature fusion module that extracts crack features across pixels is designed to improve the edges of crack segmentation. Finally, an efficient attention module that significantly improves the anti-interference ability of the model in complex backgrounds is validated. Four classic semantic segmentation algorithms (fully convolutional network, pyramid scene parsing network, U-Net, and DeepLabv3+) are selected for comparative analysis to verify the effectiveness of the proposed algorithm. The experimental results show that LC-DeepLab can accurately segment and highlight cracks from tunnel linings in complex backgrounds, and the accuracy (mean intersection over union) is 78.26%. The LC-DeepLab can achieve a real-time segmentation of 416 × 416 × 3 defect images with 46.98 f/s and 21.85 Mb parameters.
An analytical model based on complex variable theory is proposed to investigate ground responses due to shallow tunneling in multi-layered ground with an arbitrary ground surface load. The ground layers are assumed to be linear-elastic with full-stick contact between them. To solve the proposed multi-boundary problem, a series of analytic functions is introduced to accurately express the stresses and displacements contributed by different boundaries. Based on the principle of linear-elastic superposition, the multi-boundary problem is converted into a superposition of multiple single-boundary problems. The conformal mappings of different boundaries are independent of each other, which allows the stress and displacement fields to be obtained by the sum of components from each boundary. The analytical results are validated based on numerical and in situ monitoring results. The present model is superior to the classical model for analyzing ground responses of shallow tunneling in multi-layered ground; thus, it can be used with assurance to estimate the ground movement and surface building safety of shallow tunnel constructions beneath surface buildings. Moreover, the solution for the ground stress distribution can be used to estimate the safety of a single-layer composite ground.
A disadvantage of the conventional quasi-static test method is that it does not consider the soil restraint effect. A new method to test the seismic performance of prefabricated specimens for underground assembled structures is proposed, which can realistically reflect the strata restraint effect on the underground structure. Laboratory work combined with finite element (FE) analysis is performed in this study. Three full-scale sidewall specimens with different joint forms are designed and fabricated. Indices related to the seismic performance and damage modes are analyzed comprehensively to reveal the mechanism of the strata restraint effect on the prefabricated sidewall components. Test results show that the strata restraint effect effectively improves the energy dissipation capacity, load-bearing capacity, and the recoverability of the internal deformation of the precast sidewall components. However, the strata restraint effect reduces the ductility of the precast sidewall components and aggravates the shear and bending deformations in the core region of the connection joints. Additionally, the strata restraint effect significantly affects the seismic performance and damage mode of the prefabricated sidewall components. An FE model that can be used to conduct a seismic performance study of prefabricated specimens for underground assembled structures is proposed, and its feasibility is verified via comparison with test data.
Metro shield tunnels under the lateral relaxation of soil (LRS) are susceptible to significant lateral deformations, which jeopardizes the structural safety and waterproofing. However, deformation control standards for such situations have not been clearly defined. Therefore, based on a specific case, a model test is conducted to realize the LRS of a shield tunnel in a sandy stratum to reveal its effect on segment liners. Subsequently, a deformation control criterion is established. The LRS is simulated by linearly reducing the loads applied to the lateral sides of the segment structure. During lateral unloading, the lateral earth pressure coefficient on the segment decreases almost exponentially, and the structural deformation is characterized by horizontal expansion at the arch haunches and vertical shrinkage at the arch vault and arch bottom. Based on the mechanical pattern of the segment structure and the acoustic emission, the deformation response of a segment can be classified into three stages: elastic and quasi-elastic, damage, and rapid deformation development. For a shield tunnel with a diameter of approximately 6 m and under the lateral relaxation of sandy soil, when the ellipticity of the segment is less than 2.71%, reinforcement measures are not required. However, the segment deformation must be controlled when the ellipticity is 2.71% to 3.12%; in this regard, an ellipticity of 3% can be used as a benchmark in similar engineering projects.
Conventional geotechnical software limits the use of the strength reduction method (SRM) based on the Mohr–Coulomb failure criterion to analyze the slope safety factor (SF). The use of this constitutive model is impractical for predicting the behavior of all soil types. In the present study, an innovative numerical technique based on SRM was developed to determine SF using the finite element method and considering the extended Cam–clay constitutive model for clayey gravel soil as opposed to the Mohr–Coulomb model. In this regard, a novel user subroutine code was employed in ABAQUS to reduce the stabilizing forces to determine the failure surfaces and resist and drive shear stresses on a slope. After validating the proposed technique, it was employed to investigate the performance of terraced slopes in the context of a case study. The impacts of geometric parameters and different water table elevations on the SF were examined. The results indicated that an increase in the upper and lower slope heights led to a decrease in SF, and a slight increase in the horizontal offset led to an increase in the SF. Moreover, when the water table elevation was lower than the toe of the terraced slope, the SF increased because of the increase in the uplift force as a resistant component.
A falling weight deflectometer is a testing device used in civil engineering to measure and evaluate the physical properties of pavements, such as the modulus of the subgrade reaction (Y1) and the elastic modulus of the slab (Y2), which are crucial for assessing the structural strength of pavements. In this study, we developed a novel hybrid artificial intelligence model, i.e., a genetic algorithm (GA)-optimized adaptive neuro-fuzzy inference system (ANFIS-GA), to predict Y1 and Y2 based on easily determined 13 parameters of rigid pavements. The performance of the novel ANFIS-GA model was compared to that of other benchmark models, namely logistic regression (LR) and radial basis function regression (RBFR) algorithms. These models were validated using standard statistical measures, namely, the coefficient of correlation (R), mean absolute error (MAE), and root mean square error (RMSE). The results indicated that the ANFIS-GA model was the best at predicting Y1 (R = 0.945) and Y2 (R = 0.887) compared to the LR and RBFR models. Therefore, the ANFIS-GA model can be used to accurately predict Y1 and Y2 based on easily measured parameters for the appropriate and rapid assessment of the quality and strength of pavements.
