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.
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.
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.
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.
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.
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.
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–,
Steel dome structures, with their striking structural forms, take a place among the impressive and aesthetic load bearing systems featuring large internal spaces without internal columns. In this paper, the seismic design optimization of spatial steel dome structures is achieved through three recent metaheuristic algorithms that are water strider (WS), grey wolf (GW), and brain storm optimization (BSO). The structural elements of the domes are treated as design variables collected in member groups. The structural stress and stability limitations are enforced by ASD-AISC provisions. Also, the displacement restrictions are considered in design procedure. The metaheuristic algorithms are encoded in MATLAB interacting with SAP2000 for gathering structural reactions through open application programming interface (OAPI). The optimum spatial steel dome designs achieved by proposed WS, GW, and BSO algorithms are compared with respect to solution accuracy, convergence rates, and reliability, utilizing three real-size design examples for considering both the previously reported optimum design results obtained by classical metaheuristic algorithms and a gradient descent-based hyperband optimization (HBO) algorithm.
The present study describes a reliability analysis of the strength model for predicting concrete columns confinement influence with Fabric-Reinforced Cementitious Matrix (FRCM). through both physical models and Deep Neural Network model (artificial neural network (ANN) with double and triple hidden layers). The database of 330 samples collected for the training model contains many important parameters, i.e., section type (circle or square), corner radius rc, unconfined concrete strength fco, thickness nt, the elastic modulus of fiber Ef , the elastic modulus of mortar Em. The results revealed that the proposed ANN models well predicted the compressive strength of FRCM with high prediction accuracy. The ANN model with double hidden layers (APDL-1) was shown to be the best to predict the compressive strength of FRCM confined columns compared with the ACI design code and five physical models. Furthermore, the results also reveal that the unconfined compressive strength of concrete, type of fiber mesh for FRCM, type of section, and the corner radius ratio, are the most significant input variables in the efficiency of FRCM confinement prediction. The performance of the proposed ANN models (including double and triple hidden layers) had high precision with R higher than 0.93 and RMSE smaller than 0.13, as compared with other models from the literature available.
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.
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.
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.
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.
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.
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.
This paper studied the effect of incorporation of carbon nanotubes (CNTs) in carbon fiber reinforced polymer (CFRP) on strengthening of reinforced concrete (RC) beams. The RC beams were prepared, strengthened in flexure by externally bonded CFRP or CNTs-modified CFRP sheets, and tested under four-point loading. The experimental results showed the ability of the CNTs to delay the initiation of the cracks and to enhance the flexural capacity of the beams strengthened with CFRP. A nonlinear finite element (FE) model was built, validated, and used to study the effect of various parameters on the strengthening efficiency of CNTs-modified CFRP. The studied parameters included concrete strength, flexural reinforcement ratio, and CFRP sheet configuration. The numerical results showed that utilization of CNTs in CFRP production improved the flexural capacity of the strengthened beams for U-shape and underside-strip configurations. The enhancement was more pronounced in the case of U-shape than in the case of use of sheet strip covers on the underside of the beam. In case of using underside-strip, the longer or the wider the sheet, the higher was the flexural capacity of the beams. The flexural enhancement of RC beams by strengthening with CNTs-modified CFRP decreased with increasing the rebar diameter and was not affected by concrete strength.
Three different structural engineering designs were investigated to determine optimum design variables, and then to estimate design parameters and the main objective function of designs directly, speedily, and effectively. Two different optimization operations were carried out: One used the harmony search (HS) algorithm, combining different ranges of both HS parameters and iteration with population numbers. The other used an estimation application that was done via artificial neural networks (ANN) to find out the estimated values of parameters. To explore the estimation success of ANN models, different test cases were proposed for the three structural designs. Outcomes of the study suggest that ANN estimation for structures is an effective, successful, and speedy tool to forecast and determine the real optimum results for any design model.
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 objective of the current study is to propose an expert system framework based on a supervised machine learning technique (MLT) to predict the seismic performance of low- to mid-rise frame structures considering soil-structure interaction (SSI). The methodology of the framework is based on examining different MLTs to obtain the highest possible accuracy for prediction. Within the MLT, a sensitivity analysis was conducted on the main SSI parameters to select the most effective input parameters. Multiple limit state criteria were used for the seismic evaluation within the process. A new global seismic assessment ratio was introduced that considers both serviceability and strength aspects by utilizing three different engineering demand parameters (EDPs). The proposed framework is novel because it enables the designer to seismically assess the structure, while simultaneously considering different EDPs and multiple limit states. Moreover, the framework provides recommendations for building component design based on the newly introduced global seismic assessment ratio, which considers different levels of seismic hazards. The proposed framework was validated through comparison using non-linear time history (NLTH) analysis. The results show that the proposed framework provides more accurate results than conventional methods. Finally, the generalization potential of the proposed framework was tested by investigating two different types of structural irregularities, namely, stiffness and mass irregularities. The results from the framework were in good agreement with the NLTH analysis results for the selected case studies, and peak ground acceleration (PGA) was found to be the most influential input parameter in the assessment process for the case study models investigated. The proposed framework shows high generalization potential for low- to mid-rise structures.
In order to study the bearing performance of a new type of prefabricated subway station structure (PSSS), firstly, a three-dimensional finite element model of the PSSS was established to study the nonlinear mechanics and deformation performance. Secondly, the bearing mechanism of a PSSS was investigated in detail. Finally, the development law of damages to a thin-walled prefabricated component and the failure evolution mechanism of a PSSS were discussed. The results showed that this new type of the PSSS had good bearing capacity. The top arch structure was a three-hinged arch bearing system, and the enclosure structure and the substructure were respectively used as the horizontal and vertical support systems of the three-hinged arch structure to ensure the integrity and stability of the overall structure. Moreover, the tongue-and-groove joints could effectively transmit the internal force between the components and keep the components deformed in harmony. The rigidity degradation of the PSSS caused by the accumulation of damages to the spandrel, hance, arch foot, and enclosure structure was the main reason of its loss of bearing capacity. The existing thin-walled components design had significant advantages in weight reduction, concrete temperature control, components hoisting, transportation and assembly construction, which achieved a good balance between safety, usability and economy.
In structural health monitoring (SHM), the measurement is point-wise but structures are continuous. Thus, input estimation has become a hot research subject with which the full-field structural response can be calculated with a finite element model (FEM). This paper proposes a framework based on the dynamic stiffness theory, to estimate harmonic input, reconstruct responses, and to localize damages from seriously deficient measurements. To begin, Fourier transform converts the dynamic equilibrium equation to an equivalent static one in the frequency domain, which is under-determined since the dimension of measurement vector is far less than the FEM-node number. The principal component analysis has been adopted to “compress” the under-determined equation, and formed an over-determined equation to estimate the unknown input. Then, inverse Fourier transform converts the estimated input in the frequency domain to the time domain. Applying this to the FEM can reconstruct the target responses. If a structure is damaged, the estimated nodal force can localize the damage. To improve the damage-detection accuracy, a multi-measurement-based indicator has been proposed. Numerical simulations have validated that the proposed framework can capably estimate input and reconstruct multi-types of full-field responses, and the damage indicator can localize minor damages even with the existence of noise.
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.
Clear asphalt (CA) currently used in light-colored asphalt mixtures (LCAM) exhibits poor transparency and adhesion. Therefore, a highly transparent CA (HCA) modified using a silane coupling agent (KH550) was prepared. Furthermore, LCAM was prepared by mixing CA and limestone aggregates. The properties of the HCA and ordinary CA (OCA) were characterized using conventional asphalt tests, optical tests, pull-off tests, ultraviolet aging tests, dynamic shear rheometry, Fourier-transform infrared spectroscopy, differential scanning calorimetry, and scanning electron microscopy. Whereas Marshall, moisture resistance, wheel tracking, trabecular bending, and British pendulum tests were employed for the LCAM. The transmittance and spectral reflectance of the HCA were 123.30 and 3.74 times greater than those of the OCA, respectively. The complex modulus and viscosity-aging index of the HCA were 48% and 53% less than those of the OCA, respectively. After modification with KH550, the Marshall stability ratio, tensile strength ratio, and flexural strain of the HCA-prepared LCAM increased by 12.92%, 25.06%, and 23.90%, respectively. However, the rutting resistance of the HCA-prepared LCAM was 14.3% less than that of the OCA-prepared LCAM. The comprehensive performances of the HCA and HCA-prepared LCAM were 49.2% and 10.3% greater than those of the OCA and OCA-prepared LCAM, respectively, indicating a high application value in the future.
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.
An analytical model is proposed to estimate the discontinuous mechanical behavior of an existing shield tunnel above a new tunnel. The existing shield tunnel is regarded as a Timoshenko beam with longitudinal joints. The opening and relative dislocation of the longitudinal joints can be calculated using Dirac delta functions. Compared with other approaches, our method yields results that are consistent with centrifugation test data. The effects of the stiffness reduction at the longitudinal joints (α and β), the shearing stiffness of the Timoshenko beam GA, and different additional pressure profiles on the responses of the shield tunnel are investigated. The results indicate that our proposed method is suitable for simulating the discontinuous mechanical behaviors of existing shield tunnels with longitudinal joints. The deformation and internal forces decrease as α, β, and GA increase. The bending moment and shear force are discontinuous despite slight discontinuities in the deflection, opening, and dislocation. The deflection curve is consistent with the additional pressure profile. Extensive opening, dislocation, and internal forces are induced at the location of mutation pressures. In addition, the joints allow rigid structures to behave flexibly in general, as well as allow flexible structures to exhibit locally rigid characteristics. Owing to the discontinuous characteristics, the internal forces and their abrupt changes at vulnerable sections must be monitored to ensure the structural safety of existing shield tunnels.
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.
Asphalt pavement is a key component of highway infrastructures in China and worldwide. In asphalt pavement design and condition assessment, the modulus of the asphalt mixture layer is a crucial parameter. However, this parameter varies between the laboratory and field loading modes (i.e., loading frequency, compressive or tensile loading pattern), due to the viscoelastic property and composite structure of the asphalt mixture. The present study proposes a comprehensive frequency-based approach to correlate the asphalt layer moduli obtained under two field and three laboratory loading modes. The field modes are vehicular and falling weight deflectometer (FWD) loading modes, and the laboratory ones are uniaxial compressive (UC), indirect tensile (IDT), and four-point bending (4PB) loading modes. The loading frequency is used as an intermediary parameter for correlating the asphalt layer moduli under different loading modes. The observations made at two field large-scale experimental pavements facilitate the correlation analysis. It is found that the moduli obtained via laboratory 4PB tests are pretty close to those of vehicular loading schemes, in contrast to those derived in UC, IDT, and FWD modes, which need to be adjusted. The corresponding adjustment factors are experimentally assessed. The applications of those adjustment factors are expected to ensure that the moduli measured under different loading modes are appropriately used in asphalt mixture pavement design and assessment.
The problem related to bearing capacity of footing either on pure soil or on pure rock mass has been investigated over the years. Currently, no study deals with the bearing capacity of strip footing on a cohesive soil layer overlying rock mass. Therefore, by implementing the lower bound finite element limit analysis in conjunction with the second-order cone programming and the power cone programming, the ultimate bearing capacity of a strip footing located on a cohesive soil overlying rock mass is determined in this study. By considering the different values of interface adhesion factor (αcr) between the cohesive soil and rock mass, the ultimate bearing capacity of strip footing is expressed in terms of influence factor (If) for different values of cohesive soil layer cover ratio (Tcs/B). The failure of cohesive soil is modeled by using Mohr−Coulomb yield criterion, whereas Generalized Hoek−Brown yield criterion is utilized to model the rock mass at failure. The variations ofIf with different magnitudes of αcr are studied by considering the influence of the rock mass strength parameters of beneath rock mass layer. To examine stress distribution at different depths, failure patterns are also plotted.
Noncorrosive reinforcement materials facilitate producing structural concrete with seawater and sea sand. This study investigated the properties of seawater and sea sand concrete (SSC), considering the curing age (3, 7, 14, 21, 28, 60, and 150 d) and strength grade (C30, C40, and C60). The compressive behavior of SSC was obtained by compressive tests and digital image correction (DIC) technique. Scanning electron microscope (SEM) and X-ray powder diffraction (XRD) methods were applied to understand the microstructure and hydration products of cement in SSC. Results revealed a 30% decrease in compressive strength for C30 and C40 SSC from 60 to 150 d, and a less than 5% decrease for C60 from 28 to 150 d. DIC results revealed significant cracking and crushing from 80% to 100% of compressive strength. SEM images showed a more compact microstructure in higher strength SSC. XRD patterns identified Friedel’s salt phase due to the chlorides brought by seawater and sea sand. The findings in this study can provide more insights into the microstructure of SSC along with its short- and long-term compressive behavior.
The horizontal bearing behavior of a single batter pile (SBP) is vital to its application in practical engineering; however, the horizontal responses of SBPs change with the directions of horizontal loads, and this phenomenon is rarely investigated. Therefore, the directional differences in the horizontal bearing behaviors of SBPs are investigated in this study. Four model tests are conducted to preliminarily examine the effects of the skew angle of horizontal loads on the horizontal bearing capacities and distributions of the bending moments of the SBPs. Subsequently, the differences in the responses of the SBPs under horizontal loads in various directions at full scale are analyzed comprehensively via finite-element (FE) analysis. The effects of the skew angle on SBP-soil interaction are discussed. Moreover, an empirical design method is proposed based on the FE analysis results to predict the bearing ratios of SBPs in medium-dense and dense sand while considering the effects of the skew angle, batter angle, and pile diameter. The method is confirmed to be effective, as confirmed by the close agreement between the predicting results with the model test (reported in this study) and centrifuge model test results (reported in the literature).
