Super-long span bridges demand high design requirements and involve many difficulties when constructed, which is an important indicator to reflect the bridge technical level of a country. Over the past three decades, a large percentage of the new long-span bridges around the world were built in China, and thus, abundant technological innovations and experience have been accumulated during the design and construction. This paper aims to review and summarize the design and construction practices of the superstructure, the substructure, and the steel deck paving of the long-span bridges during the past decades as well as the current operation status of the existing long-span bridges in China. A future perspective was given on the developing trend of high-speed railway bridge, bridge over deep-sea, health monitoring and maintenance, intellectualization, standard system, and information technology, which is expected to guide the development direction for the construction of future super long-span bridges and promote China to become a strong bridge construction country.
Reinforced concrete beams consisting of both steel and glass-fiber-reinforced polymer rebars exhibit excellent strength, serviceability, and durability. However, the fatigue shear performance of such beams is unclear. Therefore, beams with hybrid longitudinal bars and hybrid stirrups were designed, and fatigue shear tests were performed. For specimens that failed by fatigue shear, all the glass-fiber-reinforced polymer stirrups and some steel stirrups fractured at the critical diagonal crack. For the specimen that failed by the static test after 8 million fatigue cycles, the static capacity after fatigue did not significantly decrease compared with the calculated value. The initial fatigue level has a greater influence on the crack development and fatigue life than the fatigue level in the later phase. The fatigue strength of the glass-fiber-reinforced polymer stirrups in the specimens was considerably lower than that of the axial tension tests on the glass-fiber-reinforced polymer bar in air and beam-hinge tests on the glass-fiber-reinforced polymer bar, and the failure modes were different. Glass-fiber-reinforced polymer stirrups were subjected to fatigue tension and shear, and failed owing to shear.
This paper presents a new approach for automatical classification of structural state through deep learning. In this work, a Convolutional Neural Network (CNN) was designed to fuse both the feature extraction and classification blocks into an intelligent and compact learning system and detect the structural state of a steel frame; the input was a series of vibration signals, and the output was a structural state. The digital image correlation (DIC) technology was utilized to collect vibration information of an actual steel frame, and subsequently, the raw signals, without further pre-processing, were directly utilized as the CNN samples. The results show that CNN can achieve 99% classification accuracy for the research model. Besides, compared with the backpropagation neural network (BPNN), the CNN had an accuracy similar to that of the BPNN, but it only consumes 19% of the training time. The outputs of the convolution and pooling layers were visually displayed and discussed as well. It is demonstrated that: 1) the CNN can extract the structural state information from the vibration signals and classify them; 2) the detection and computational performance of the CNN for the incomplete data are better than that of the BPNN; 3) the CNN has better anti-noise ability.
In waterfront geotechnical engineering, seismic and drainage conditions must be considered in the design of retaining structures. This paper proposes a general analytical method to evaluate the seismic active earth pressure on a retaining wall with backfill subjected to partial steady seepage flow under seismic conditions. The method comprises the following steps: i) determination of the total head, ii) upper bound solution of seismic active earth thrust, and iii) deduction for the earth pressure distribution. The determination of total head h(x,z) relies on the Fourier series expansions, and the expressions of the seismic active earth thrust and pressure are derived by using the upper bound theorem. Parametric studies reveal that insufficient drainage and earthquakes are crucial factors that cause unfavorable earth pressure. The numerical results confirm the validity of the total head distribution. Comparisons indicate that the proposed method is consistent with other relevant existing methods in terms of predicting seismic active earth pressure. The method can be applied to the seismic design of waterfront retaining walls.
Real-time dynamic adjustment of the tunnel bore machine (TBM) advance rate according to the rock-machine interaction parameters is of great significance to the adaptability of TBM and its efficiency in construction. This paper proposes a real-time predictive model of TBM advance rate using the temporal convolutional network (TCN), based on TBM construction big data. The prediction model was built using an experimental database, containing 235 data sets, established from the construction data from the Jilin Water-Diversion Tunnel Project in China. The TBM operating parameters, including total thrust, cutterhead rotation, cutterhead torque and penetration rate, are selected as the input parameters of the model. The TCN model is found outperforming the recurrent neural network (RNN) and long short-term memory (LSTM) model in predicting the TBM advance rate with much smaller values of mean absolute percentage error than the latter two. The penetration rate and cutterhead torque of the current moment have significant influence on the TBM advance rate of the next moment. On the contrary, the influence of the cutterhead rotation and total thrust is moderate. The work provides a new concept of real-time prediction of the TBM performance for highly efficient tunnel construction.
For localized fires, it is necessary to consider the thermal and mechanical responses of building elements subject to uneven heating under the influence of wind. In this paper, the thermomechanical phenomena experienced by a ceiling jet and I-beam in a structural fire were simulated. Instead of applying the concept of adiabatic surface temperature (AST) to achieve fluid–structure coupling, this paper proposes a new computational fluid dynamics–finite element method numerical simulation that combines wind, fire, thermal, and structural analyses. First, to analyze the velocity and temperature distributions, the results of the numerical model and experiment were compared in windless conditions, showing good agreement. Vortices were found in the local area formed by the upper and lower flanges of the I-beam and the web, generating a local high-temperature zone and enhancing the heat transfer of convection. In an incoming-flow scenario, the flame was blown askew significantly; the wall temperature was bimodally distributed in the axial direction. The first temperature peak was mainly caused by radiative heat transfer, while the second resulted from convective heat transfer. In terms of mechanical response, the yield strength degradation in the highest-temperature region in windless conditions was found to be significant, thus explaining the stress distribution of steel beams in the fire field. The mechanical response of the overall elements considering the incoming flows was essentially elastic.
Homogenization methods can be used to predict the effective macroscopic properties of materials that are heterogenous at micro- or fine-scale. Among existing methods for homogenization, computational homogenization is widely used in multiscale analyses of structures and materials. Conventional computational homogenization suffers from long computing times, which substantially limits its application in analyzing engineering problems. The neural networks can be used to construct fully decoupled approaches in nonlinear multiscale methods by mapping macroscopic loading and microscopic response. Computational homogenization methods for nonlinear material and implementation of offline multiscale computation are studied to generate data set. This article intends to model the multiscale constitution using feedforward neural network (FNN) and recurrent neural network (RNN), and appropriate set of loading paths are selected to effectively predict the materials behavior along unknown paths. Applications to two-dimensional multiscale analysis are tested and discussed in detail.
This review of the added value of multi-scale modeling of concrete is based on three representative examples. The first one is concerned with the analysis of experimental data, taken from four high-dynamic tests. The structural nature of the high-dynamic strength increase can be explained by using a multi-scale model. It accounts for the microstructure of the specimens. The second example refers to multi-scale thermoelastic analysis of concrete pavements, subjected to solar heating. A sensitivity analysis with respect to the internal relative humidity (RH) of concrete has underlined the great importance of the RH for an assessment of the risk of microcracking of concrete. The third example deals with multi-scale structural analysis of a real-scale test of a segmental tunnel ring. It has turned out that multi-scale modeling of concrete enables more reliable predictions of crack opening displacements in tunnel segments than macroscopic models taken from codes of practice. Overall, it is concluded that multi-scale models have indeed a significant added value. However, its degree varies with these examples. In any case, it can be assessed by means of a comparison of the results from three sources, namely, multi-scale structural analysis, conventional structural analysis, and experiments.
The tensile behavior of hybrid fiber reinforced concrete (HFRC) is important to the design of HFRC and HFRC structure. This study used an artificial neural network (ANN) model to describe the tensile behavior of HFRC. This ANN model can describe well the tensile stress-strain curve of HFRC with the consideration of 23 features of HFRC. In the model, three methods to process output features (no-processed, mid-processed, and processed) are discussed and the mid-processed method is recommended to achieve a better reproduction of the experimental data. This means the strain should be normalized while the stress doesn’t need normalization. To prepare the database of the model, both many direct tensile test results and the relevant literature data are collected. Moreover, a traditional equation-based model is also established and compared with the ANN model. The results show that the ANN model has a better prediction than the equation-based model in terms of the tensile stress-strain curve, tensile strength, and strain corresponding to tensile strength of HFRC. Finally, the sensitivity analysis of the ANN model is also performed to analyze the contribution of each input feature to the tensile strength and strain corresponding to tensile strength. The mechanical properties of plain concrete make the main contribution to the tensile strength and strain corresponding to tensile strength, while steel fibers tend to make more contributions to these two items than PVA fibers.
In recent few years, significant improvement has been made in developing largescale 3D printers to accommodate the need of industrial-scale 3D printing. It is of great feasibility to construct structural components and buildings by means of 3D concrete printing. The major issues of this innovative technique focus on the preparation and optimization of concrete materials which possess favourable printable properties as well as the measurement and evaluation methods of their workability. This paper firstly introduces three largescale 3D printing systems that have been successfully applied in construction industry. It then summarizes the commonly used raw materials in concrete manufacturing. Critical factors that should be particularly controlled in material preparation are specified. Easy-extrusive, easy-flowing, well-buildable, proper setting time and low shrinkage are significant for concrete mixture to meet the critical requirements of a freeform construction process. Thereafter, measuring methods that can be employed to assess the fresh and hardened properties of concrete at early stages are suggested. Finally, a few of evaluation methods are presented which may offer certain assistance for optimizing material preparation. The objective of this work is to review current design methodologies and experimental measurement and evaluation methods for 3D printable concrete materials and promote its responsible use with largescale 3D printing technology.
An approach to control the profiles of interstory drift ratios along the height of building structures via topology optimization is proposed herein. The theoretical foundation of the proposed approach involves solving a min–max optimization problem to suppress the maximum interstory drift ratio among all stories. Two formulations are suggested: one inherits the bound formulation and the other utilizes a p-norm function to aggregate all individual interstory drift ratios. The proposed methodology can shape the interstory drift ratio profiles into inverted triangular or quadratic patterns because it realizes profile control using a group of shape weight coefficients. The proposed formulations are validated via a series of numerical examples. The disparity between the two formulations is clear. The optimization results show the optimal structural features for controlling the interstory drift ratios under different requirements.
A numerical framework was proposed for the seismic analysis of underground structures in layered ground under inclined P-SV waves. The free-field responses are first obtained using the stiffness matrix method based on plane-wave assumptions. Then, the domain reduction method was employed to reproduce the wavefield in the numerical model of the soil–structure system. The proposed numerical framework was verified by providing comparisons with analytical solutions for cases involving free-field responses of homogeneous ground, layered ground, and pressure-dependent heterogeneous ground, as well as for an example of a soil–structure interaction simulation. Compared with the viscous and viscous-spring boundary methods adopted in previous studies, the proposed framework exhibits the advantage of incorporating oblique incident waves in a nonlinear heterogeneous ground. Numerical results show that SV-waves are more destructive to underground structures than P-waves, and the responses of underground structures are significantly affected by the incident angles.
In this study, the mechanical properties of the composite plate were considered Gaussian random fields and their effects on the buckling load and corresponding mode shapes were studied by developing a semi-analytical non-intrusive approach. The random fields were decomposed by the Karhunen−Loève method. The strains were defined based on the assumptions of the first-order and higher-order shear-deformation theories. Stochastic equations of motion were extracted using Euler–Lagrange equations. The probabilistic response space was obtained by employing the non-intrusive polynomial chaos method. Finally, the effect of spatially varying stochastic properties on the critical load of the plate and the irregularity of buckling mode shapes and their sequences were studied for the first time. Our findings showed that different shear deformation plate theories could significantly influence the reliability of thicker plates under compressive loading. It is suggested that a linear relationship exists between the mechanical properties’ variation coefficient and critical loads’ variation coefficient. Also, in modeling the plate properties as random fields, a significant stochastic irregularity is obtained in buckling mode shapes, which is crucial in practical applications.
This paper reviews the fire problem in critical transportation infrastructures such as bridges and tunnels. The magnitude of the fire problem is illustrated, and the recent increase in fire problems in bridges and tunnels is highlighted. Recent research undertaken to address fire problems in transportation structures is reviewed, as well as critical factors governing the performance of those structures. Furthermore, key strategies recommended for mitigating fire hazards in bridges and tunnels are presented, and their applicability to practical situations is demonstrated through a practical case study. Furthermore, research needs and emerging trends for enhancing the “state-of-the-art” in this area are discussed.
To research the axial compression behavior of steel reinforced recycled concrete (SRRC) short columns confined by carbon fiber reinforced plastics (CFRP) strips, nine scaled specimens of SRRC short columns were fabricated and tested under axial compression loading. Subsequently, the failure process and failure modes were observed, and load-displacement curves as well as the strain of various materials were analyzed. The effects on the substitution percentage of recycled coarse aggregate (RCA), width of CFRP strips, spacing of CFRP strips and strength of recycled aggregate concrete (RAC) on the axial compression properties of columns were also analyzed in the experimental investigation. Furthermore, the finite element model of columns which can consider the adverse influence of RCA and the constraint effect of CFRP strips was founded by ABAQUS software and the nonlinear parameter analysis of columns was also implemented in this study. The results show that the first to reach the yield state was the profile steel in the columns, then the longitudinal rebars and stirrups yielded successively, and finally RAC was crushed as well as the CFRP strips was also broken. The replacement rate of RCA has little effect on the columns, and with the substitution rate of RCA from 0 to 100%, the bearing capacity of columns decreased by only 4.8%. Increasing the CFRP strips width or decreasing the CFRP strips spacing could enhance the axial bearing capacity of columns, the maximum increase was 10.5% or 11.4%, and the ductility of columns was significantly enhanced. Obviously, CFRP strips are conducive to enhance the axial bearing capacity and deformation capacity of columns. On this basis, considering the restraint effect of CFRP strips and the adverse effects of RCA, the revised formulas for calculating the axial bearing capacity of SRRC short columns confined by CFRP strips were proposed.
Research on the quality of data in a structural calculation document (SCD) is lacking, although the SCD of a bridge is used as an essential reference during the entire lifecycle of the facility. XML Schema matching enables qualitative improvement of the stored data. This study aimed to enhance the applicability of XML Schema matching, which improves the speed and quality of information stored in bridge SCDs. First, the authors proposed a method of reducing the computing time for the schema matching of bridge SCDs. The computing speed of schema matching was increased by 13 to 1800 times by reducing the checking process of the correlations. Second, the authors developed a heuristic solution for selecting the optimal weight factors used in the matching process to maintain a high accuracy by introducing a decision tree. The decision tree model was built using the content elements stored in the SCD, design companies, bridge types, and weight factors as input variables, and the matching accuracy as the target variable. The inverse-calculation method was applied to extract the weight factors from the decision tree model for high-accuracy schema matching results.
To study the damage evolution behavior of polypropylene fiber reinforced concrete (PFRC) subjected to sulfate attack, a uniaxial compression test was carried out based on acoustic emission (AE). The effect of sulfate attack relative to time and fiber hybridization were analyzed and the compression damage factor was calculated using a mathematical model. The changes to AE ringing counts during the compression could be divided into compaction, elastic, and AE signal hyperactivity stages. In the initial stage of sulfate attack, the concrete micropores and microcracks were compacted gradually under external load and a corrosion products filling effect, and this corresponded with detection of few AE signals and with concrete compression strength enhancement. With increasing sulfate attack time, AE activity decreased. The cumulative AE ringing counts of PFRC at all corrosion ages were much higher than those for plain concrete. PFRC could still produce AE signals after peak load due to drawing effect of polypropylene fiber. After 150 d of sulfate attack, the cumulative AE ringing counts of plain concrete went down by about an order of magnitude, while that for PFRC remained at a high level. The initial damage factor of hybrid PFRC was −0.042 and −0.056 respectively after 150 d of corrosion, indicating that the advantage of hybrid polypropylene fiber was more obvious than plain concrete and single-doped PFRC. Based on a deterioration equation, the corrosion resistance coefficient of hybrid PFRC would be less than 0.75 after 42 drying−wetting sulfate attack cycles, which was 40% longer than that of plain concrete.
Based on the domain reduction idea and artificial boundary substructure method, this paper proposes an FK-FEM hybrid approach by integrating the advantages of FK and FEM (i.e., FK can efficiently generate high-frequency three translational motion, while FEM has rich elements types and constitutive models). An advantage of this approach is that it realizes the entire process simulation from point dislocation source to underground structure. Compared with the plane wave field input method, the FK-FEM hybrid approach can reflect the spatial variability of seismic motion and the influence of source and propagation path. This approach can provide an effective solution for seismic analysis of underground structures under scenario of earthquake in regions where strong earthquakes may occur but are not recorded, especially when active faults, crustal, and soil parameters are available. Taking Daikai subway station as an example, the seismic response of the underground structure is simulated after verifying the correctness of the approach and the effects of crustal velocity structure and source parameters on the seismic response of Daikai station are discussed. In this example, the influence of velocity structure on the maximum interlayer displacement angle of underground structure is 96.5% and the change of source parameters can lead to the change of structural failure direction.
A novel floating breakwater-windbreak structure (floating forest) has been designed for the protection of vulnerable coastal areas from extreme wind and wave loadings during storm conditions. The modular arch-shaped concrete structure is positioned perpendicularly to the direction of the prevailing wave and wind. The structure below the water surface acts as a porous breakwater with wave scattering capability. An array of tubular columns on the sloping deck of the breakwater act as an artificial forest-type windbreak. A feasibility study involving hydrodynamic and aerodynamic analyses has been performed, focusing on its capability in reducing wave heights and wind speeds in the lee side. The study shows that the proposed 1 km long floating forest is able to shelter a lee area that stretches up to 600 m, with 40%–60% wave energy reduction and 10%–80% peak wind speed reduction.
In recent years, tunnel boring machines (TBMs) have been widely used in tunnel construction. However, the TBM control parameters set based on operator experience may not necessarily be suitable for certain geological conditions. Hence, a method to optimize TBM control parameters using an improved loss function-based artificial neural network (ILF-ANN) combined with quantum particle swarm optimization (QPSO) is proposed herein. The purpose of this method is to improve the TBM performance by optimizing the penetration and cutterhead rotation speeds. Inspired by the regularization technique, a custom artificial neural network (ANN) loss function based on the penetration rate and rock-breaking specific energy as TBM performance indicators is developed in the form of a penalty function to adjust the output of the network. In addition, to overcome the disadvantage of classical error backpropagation ANNs, i.e., the ease of falling into a local optimum, QPSO is adopted to train the ANN hyperparameters (weight and bias). Rock mass classes and tunneling parameters obtained in real time are used as the input of the QPSO-ILF-ANN, whereas the cutterhead rotation speed and penetration are specified as the output. The proposed method is validated using construction data from the Songhua River water conveyance tunnel project. Results show that, compared with the TBM operator and QPSO-ANN, the QPSO-ILF-ANN effectively increases the TBM penetration rate by 14.85% and 13.71%, respectively, and reduces the rock-breaking specific energy by 9.41% and 9.18%, respectively.
In this study, a new system consisting of a combination of braces and steel infill panels called the braced corrugated steel shear panel (BCSSP) is presented. To obtain the hysteretic behavior of the proposed system, the quasi-static cyclic performances of two experimental specimens were first evaluated. The finite element modeling method was then verified based on the obtained experimental results. Additional numerical evaluations were carried out to investigate the effects of different parameters on the system. Subsequently, a relationship was established to estimate the buckling shear strength of the system without considering residual stresses. The results obtained from the parametric study indicate that the corrugated steel shear panel (CSSP) with the specifications of a = 30 mm, t = 2 mm, and θ = 90° had the highest energy dissipation capacity and ultimate strength while the CSSP with the specifications of a = 30 mm, t = 2 mm, and θ = 30° had the highest initial stiffness. It can thus be concluded that the latter CSSP has the best structural performance and that increasing the number of corrugations, corrugation angle, and plate thickness and decreasing the sub-panel width generally enhance the performance of CSSPs in terms of the stability of their hysteretic behaviors.
Scouring is one of the primary triggers of failure for bridges across rivers or seas. However, research concerning the scour mechanism of multi-wall foundations (MWFs) remains scarce, hindering the further application of MWFs. In this study, for the first time, the scouring effect caused by unidirectional flow around MWFs was examined numerically using FLOW-3D involving a large-eddy simulation. Initially, the applicability of the scouring model and input parameters was validated using a case study based on published measured data. Subsequently, the scouring effects of four MWFs with different wall arrangements and inflow angles, including the flow field analysis and scour pit and depth, were investigated thoroughly. It was found that the maximum scour depth of MWFs with an inflow angle of 0° was smaller than that of those with an inflow angle of 45°, regardless of the wall arrangement. Meanwhile, changing the inflow angle significantly affects the scour characteristics of MWFs arranged in parallel. In practical engineering, MWFs arranged in parallel are preferred considering the need for scouring resistance. However, a comparative analysis should be performed to consider comprehensively whether to adopt the form of a round wall arrangement when the inflow angle is not 0° or the inflow direction is changeable.
The corrosion degradation behavior of concrete materials plays a crucial role in the change of its mechanical properties under multi-ion interaction in the marine environment. In this study, the variation in the macro-physical and mechanical properties of concrete with corrosion time is investigated, and the source of micro-corrosion products under different salt solutions in seawater are analyzed. Regardless of the continuous hydration effect of concrete, the damage effects of various corrosive ions (Cl–,
In this study, the deep learning models for estimating the mechanical properties of concrete containing silica fume subjected to high temperatures were devised. Silica fume was used at concentrations of 0%, 5%, 10%, and 20%. Cube specimens (100 mm × 100 mm × 100 mm) were prepared for testing the compressive strength and ultrasonic pulse velocity. They were cured at 20°C±2°C in a standard cure for 7, 28, and 90 d. After curing, they were subjected to temperatures of 20°C, 200°C, 400°C, 600°C, and 800°C. Two well-known deep learning approaches, i.e., stacked autoencoders and long short-term memory (LSTM) networks, were used for forecasting the compressive strength and ultrasonic pulse velocity of concrete containing silica fume subjected to high temperatures. The forecasting experiments were carried out using MATLAB deep learning and neural network tools, respectively. Various statistical measures were used to validate the prediction performances of both the approaches. This study found that the LSTM network achieved better results than the stacked autoencoders. In addition, this study found that deep learning, which has a very good prediction ability with little experimental data, was a convenient method for civil engineering.
Design is a goal-oriented planning activity for creating products, processes, and systems with desired functions through specifications. It is a decision-making exploration: the design outcome may vary greatly depending on the designer’s knowledge and philosophy. Integrated design is one type of design philosophy that takes an interdisciplinary and holistic approach. In civil engineering, structural design is such an activity for creating buildings and infrastructures. Recently, structural design in many countries has emphasized a performance-based philosophy that simultaneously considers a structure’s safety, durability, serviceability, and sustainability. Consequently, integrated design in civil engineering has become more popular, useful, and important. Material-oriented integrated design and construction of structures (MIDCS) combine materials engineering and structural engineering in the design stage: it fully utilizes the strengths of materials by selecting the most suitable structural forms and construction methodologies. This paper will explore real-world examples of MIDCS, including the realization of MIDCS in timber seismic-resistant structures, masonry arch structures, long-span steel bridges, prefabricated/on-site extruded light-weight steel structures, fiber-reinforced cementitious composites structures, and fiber-reinforced polymer bridge decks. Additionally, advanced material design methods such as bioinspired design and structure construction technology of additive manufacturing are briefly reviewed and discussed to demonstrate how MIDCS can combine materials and structures. A unified strength-durability design theory is also introduced, which is a human-centric, interdisciplinary, and holistic approach to the description and development of any civil infrastructure and includes all processes directly involved in the life cycle of the infrastructure. Finally, this paper lays out future research directions for further development in the field.
This article proposes a novel methodology that uses mathematical and numerical models of a structure to build a data set and determine crucial nodes that possess the highest sensitivity. Regression surfaces between the structural parameters and structural output features, represented by the natural frequencies of the structure and local transmissibility, are built using the numerical data set. A description of a possible experimental application is provided, where sensors are mounted at crucial nodes, and the natural frequencies and local transmissibility at each natural frequency are determined from the power spectral density and the power spectral density ratios of the sensor responses, respectively. An inverse iterative process is then applied to identify the structural parameters by matching the experimental features with the available parameters in the myriad numerical data set. Three examples are presented to demonstrate the feasibility and efficacy of the proposed methodology. The results reveal that the method was able to accurately identify the boundary coefficients and physical parameters of the Euler-Bernoulli beam as well as a highway bridge model with elastic foundations using only two measurement points. It is expected that the proposed method will have practical applications in the identification and analysis of restored structural systems with unknown parameters and boundary coefficients.
The stability of the shield tunneling face is an extremely important factor affecting the safety of tunnel construction. In this study, a transparent clay with properties similar to those of Tianjin clay is prepared and a new transparent clay model test apparatus is developed to overcome the “black box” problem in the traditional model test. The stability of the shield tunneling face (failure mode, influence range, support force, and surface settlement) is investigated in transparent clay under active failure. A series of transparent clay model tests is performed to investigate the active failure mode, influence range, and support force of the shield tunneling face under different burial depth conditions, whereas particle flow code three-dimensional numerical simulations are conducted to verify the failure mode of the shield tunneling face and surface settlement along the transverse section under different burial depth conditions. The results show that the engineering characteristics of transparent clay are similar to those of soft clay in Binhai, Tianjin and satisfy visibility requirements. Two types of failure modes are obtained: the overall failure mode (cover/diameter: C/D≤1.0) and local failure mode (C/D≥2.0). The influence range of the transverse section is wider than that of the longitudinal section when C/D≥2.0. Additionally, the normalized thresholds of the relative displacement and support force ratio are 3%–6% and 0.2–0.4, respectively. Owing to the cushioning effect of the clay layer, the surface settlement is significantly reduced as the tunnel burial depth increases.
Cracking in wading-concrete structures has a worse impact on structural safety compared with conventional concrete structures. The accurate and timely monitoring of crack development plays a significant role in the safety of wading-concrete engineering. The heat-transfer rate near a crack is related to the flow velocity of the fluid in the crack. Based on this, a novel crack-identification method for underwater concrete structures is presented. This method uses water irrigation to generate seepage at the interface of a crack; then, the heat-dissipation rate in the crack area will increase because of the convective heat-transfer effect near the crack. Crack information can be identified by monitoring the cooling law and leakage flow near cracks. The proposed mobile crack-monitoring system consists of a heating system, temperature-measurement system, and irrigation system. A series of tests was conducted on a reinforced-concrete beam using this system. The crack-discrimination index ψ was defined, according to the subsection characteristics of the heat-source cooling curve. The effects of the crack width, leakage flow, and relative positions of the heat source and crack on ψ were studied. The results showed that the distribution characteristics of ψ along the monitoring line could accurately locate the crack, but not quantify the crack width. However, the leakage flow is sensitive to the crack width and can be used to identify it.
The effective notch stress approach for evaluating the fatigue strength of rib–deck welds requires notch stress concentration factors obtained from complex finite element analysis. To improve the efficiency of the approach, the notch stress concentration factors for three typical fatigue-cracking modes (i.e., root–toe, root–deck, and toe–deck cracking modes) were thoroughly investigated in this study. First, we developed a model for investigating the effective notch stress in rib–deck welds. Then, we performed a parametric analysis to investigate the effects of multiple geometric parameters of a rib–deck weld on the notch stress concentration factors. On this basis, the multiple linear stepwise regression analysis was performed to obtain the optimal regression functions for predicting the notch stress concentration factors. Finally, we employed the proposed formulas in a case study. The notch stress concentration factors estimated from the developed formulas show agree well with the finite element analysis results. The results of the case study demonstrate the feasibility and reliability of the proposed formulas. It also shows that the fatigue design curve of FAT225 seems to be conservative for evaluating the fatigue strength of rib–deck welds.
In this paper, the machine learning (ML) model is built for slope stability evaluation and meets the high precision and rapidity requirements in slope engineering. Different ML methods for the factor of safety (FOS) prediction are studied and compared hoping to make the best use of the large variety of existing statistical and ML regression methods collected. The data set of this study includes six characteristics, namely unit weight, cohesion, internal friction angle, slope angle, slope height, and pore water pressure ratio. The whole ML model is primarily divided into data preprocessing, outlier processing, and model evaluation. In the data preprocessing, the duplicated data are first removed, then the outliers are filtered by the LocalOutlierFactor method and finally, the data are standardized. 11 ML methods are evaluated for their ability to learn the FOS based on different input parameter combinations. By analyzing the evaluation indicators R 2, MAE, and MSE of these methods, SVM, GBR, and Bagging are considered to be the best regression methods. The performance and reliability of the nonlinear regression method are slightly better than that of the linear regression method. Also, the SVM-poly method is used to analyze the susceptibility of slope parameters.
