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.

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.

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.

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.

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.

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.

Because of favorable mechanical properties, deformation resistance and being conducive to environmental protection, honeycomb fabricated plastic pavement slabs are highly recommended these years. At present, most studies focus on the performance of plastic materials, however, the dimension optimization of fabricated plastic pavement slab is rarely mentioned. In this paper, an optimized geometry of the honeycomb pavement slab was determined through finite element analysis. Mechanical response of honeycomb slabs with different internal dimensions and external dimensions were explored. Several dimension factors were taken into consideration including the side length, rib thickness, the thickness of both top and bottom slabs of honeycomb structure and the length, the width and the thickness of the fabricated plastic slab. The results showed that honeycomb pavement slab with 6 cm bottom slab, 12 cm top slab,18 cm side length and 6 cm rib thickness is recommended, additionally, an external dimension of 4 m × 4 m × 0.45 m is suggested. Then, the mechanical responses of this optimized fabricated plastic slab were further investigated. Significance of different influencing factors, including wheel load, elastic modulus of plastic material, base layer thickness, soil foundation modulus and base layer modulus were ranked.

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.

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.

Acoustic emission and digital image correlation were used to study the spatiotemporal evolution characteristics of crack extension of soft and hard composite laminated rock masses (SHCLRM) containing double fissures under uniaxial compression. The effects of different rock combination methods and prefabricated fissures with different orientations on mechanical properties and crack coalescence patterns were analyzed. The characteristics of the acoustic emission source location distribution, and frequency changes of the crack evolution process were also investigated. The test results show that the damage mode of SHCLRM is related to the combination mode of rock layers and the orientation of fractures. Hard layers predominantly produce tensile cracks; soft layers produce shear cracks. The first crack always sprouts at the tip or middle of prefabricated fractures in hard layers. The acoustic emission signal of SHCLRM with double fractures has clear stage characteristics, and the state of crack development can be inferred from this signal to provide early warning for rock fracture instability. This study can provide a reference for the assessment of the fracture development status between adjacent roadways in SHCLRM in underground mines, as well as in roadway layout and support.

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 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.

This paper proposes a new Deep Feed-forward Neural Network (DFNN) approach for damage detection in functionally graded carbon nanotube-reinforced composite (FG-CNTRC) plates. In the proposed approach, the DFNN model is developed based on a data set containing 20 000 samples of damage scenarios, obtained via finite element (FE) simulation, of the FG-CNTRC plates. The elemental modal kinetic energy (MKE) values, calculated from natural frequencies and translational nodal displacements of the structures, are utilized as input of the DFNN model while the damage locations and corresponding severities are considered as output. The state-of-the art Exponential Linear Units (ELU) activation function and the Adamax algorithm are employed to train the DFNN model. Additionally, in order to enhance the performance of the DFNN model, the mini-batch and early-stopping techniques are applied to the training process. A trial-and-error procedure is implemented to determine suitable parameters of the network such as the number of hidden layers and the number of neurons in each layer. The accuracy and capability of the proposed DFNN model are illustrated through two distinct configurations of the CNT-fibers constituting the FG-CNTRC plates including uniform distribution (UD) and functionally graded-V distribution (FG-VD). Furthermore, the performance and stability of the DFNN model with the consideration of noise effects on the input data are also investigated. Obtained results indicate that the proposed DFNN model is able to give sufficiently accurate damage detection outcomes for the FG-CNTRC plates for both cases of noise-free and noise-influenced data.

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^–, {\mathrm{S}\mathrm{O}}_4^{2-}, and Mg²⁺, etc.) on the tensile and compressive strength of concrete are discussed based on measurement in different salt solutions. The sensitivity analysis method for concrete strength is used to quantitatively analyze the sensitivity of concrete strength to the effects of each ion in a multi-salt solution without considering the influence of continued hydration. The quantitative results indicate that the addition of Cl^– can weaken the corrosion effect of {\mathrm{S}\mathrm{O}}_4^{2-} by about 20%, while the addition of Mg²⁺ or Mg²⁺ and Cl^– can strengthen it by 10%–20% during a 600-d corrosion process.

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.

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.

For open car park structures, adopting a performance-based structural fire design is often justified and allowed because the fire does not reach flashover. However, this design approach requires an accurate assessment of temperatures in structural members exposed to car fires. This paper describes a numerical study on the thermal exposure on steel framing members in open car park fires. Steel temperatures are computed by the coupling of computational fluid dynamics and finite element modeling, and by analytical models from the Eurocodes. In addition, the influence of galvanization on the steel temperature evolution is assessed. Results show that temperatures in unprotected beams and columns are influenced by the section geometry, car fire scenario, modeling approach, and use of galvanization. Galvanization slightly delays and reduces peak temperature. Regarding the different models, CFD-FEM (CFD: computational fluid dynamics, FEM: finite-element method) coupled models predict lower temperatures than the Hasemi model, because the latter conservatively assumes that the fire flame continuously touches the ceiling. Further, the Hasemi model cannot account for the effect of reduced emissivity from galvanization on the absorbed heat flux. Detailed temperature distributions obtained in the steel members can be used to complete efficient structural fire designs based on the member sections, structure layout, and use of galvanization.

In this study, a static shear energy algorithm is presented for the damage assessment of beam-like structures. According to the energy release principle, the strain energy of a damaged element suddenly changes when structural damage occurs. Therefore, the change in the static shear energy is employed to determine the damage locations in beam-like structures. The static shear energy is derived from the spectral factorization of the elementary stiffness matrix and structural deflection variation. The advantage of using shear energy as opposed to total energy is that only a few deflection data points of the beam structure are required during the process of damage identification. Another advantage of the proposed approach is that damage detection can be performed without establishing a structural finite-element model in advance. The proposed technique is first validated using a numerical example with single, multiple, and adjacent damage scenarios. A channel steel beam and rectangular concrete beam are employed as experimental cases to further verify the proposed approach. The results of the simulation and experiment examples indicate that the proposed algorithm provides a simple and effective method for defect localization in beam-like structures.

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).

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 (α_cr) between the cohesive soil and rock mass, the ultimate bearing capacity of strip footing is expressed in terms of influence factor (I_f) for different values of cohesive soil layer cover ratio (T_cs/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 ofI_f with different magnitudes of α_cr 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.

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.

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 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.

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.

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.

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.

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.

To study the behavior of coral aggregate concrete (CAC) column under axial and eccentric compression, the compression behavior of CAC column with different types of steel and initial eccentricity (e_i) were tested, and the deformation behavior and ultimate bearing capacity (N_u) were studied. The results showed that as the e_i increases, the N_u of CAC column decreases nonlinearly. Besides, the steel corrosion in CAC column is severe, which reduces the steel section and steel strength, and decreases the N_u of CAC column. The durability of CAC structures can be improved by using new organic coated steel. Considering the influence of steel corrosion and interfacial bond deterioration, the calculation models of N_u under axial and eccentric compression were presented.

Real-time perception of rock conditions based on continuously collected data to meet the requirements of continuous Tunnel Boring Machine (TBM) construction presents a critical challenge that warrants increased attention. To achieve this goal, this paper establishes real-time prediction models for fractured and weak rock mass by comparing 6 different algorithms using real-time data collected by the TBM. The models are optimized in terms of selecting metric, selecting input features, and processing imbalanced data. The results demonstrate the following points. (1) The Youden’s index and area under the ROC curve (AUC) are the most appropriate performance metrics, and the XGBoost Random Forest (XGBRF) algorithm exhibits superior prediction and generalization performance. (2) The duration of the TBM loading phase is short, usually within a few minutes after the disc cutter contacts the tunnel face. A model based on the features during the loading phase has a miss rate of 21.8%, indicating that it can meet the early warning needs of TBM construction well. As the TBM continues to operate, the inclusion of features calculated from subsequent data collection can continuously correct the results of the real-time prediction model, ultimately reducing the miss rate to 16.1%. (3) Resampling the imbalanced data set can effectively improve the prediction by the model, while the XGBRF algorithm has certain advantages in dealing with the imbalanced data issue. When the model gives an alarm, the TBM operator and on-site engineer can be reminded and take some necessary measures for avoiding potential tunnel collapse. The real-time predication model can be a useful tool to increase the safety of TBM excavation.

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.

It is of great significance to quickly detect underwater cracks as they can seriously threaten the safety of underwater structures. Research to date has mainly focused on the detection of above-water-level cracks and hasn’t considered the large scale cracks. In this paper, a large-scale underwater crack examination method is proposed based on image stitching and segmentation. In addition, a purpose of this paper is to design a new convolution method to segment underwater images. An improved As-Projective-As-Possible (APAP) algorithm was designed to extract and stitch keyframes from videos. The graph convolutional neural network (GCN) was used to segment the stitched image. The GCN’s m-IOU is 24.02% higher than Fully convolutional networks (FCN), proving that GCN has great potential of application in image segmentation and underwater image processing. The result shows that the improved APAP algorithm and GCN can adapt to complex underwater environments and perform well in different study areas.

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.

One of the strategic materials used in earth-fill embankment dams and in modifying and preventing groundwater flow is plastic concrete (PlC). PlC is comprised of aggregates, water, cement, and bentonite. Natural zeolite (NZ) is a relatively abundant mineral resource and in this research, the microstructure, unconfined strength, triaxial behavior, and permeability of PlC made with 0%, 10%, 15%, 20%, and 25% replacement of cement by NZ were studied. Specimens of PIC-NZ were subjected to confined conditions and three different confining pressures of 200, 350, and 500 kPa were used to investigate their mechanical behavior and permeability. To study the effect of sulfate ions on the properties of PlC-NZ specimens, the specimens were cured in one of two different environments: normal condition and in the presence of sulfate ions. Results showed that increasing the zeolite content decreases the unconfined strength, elastic modulus, and peak strength of PlC-NZ specimens at the early ages of curing. However, at the later ages, increasing the zeolite content increases unconfined strength as well as the peak strength and elastic modulus. Specimens cured in the presence of sulfate ions indicated lower permeability, higher unconfined strength, elastic modulus, and peak strength due to having lower porosity.

Owing to long-distance advancement or obstacles, shield tunneling machines are typically shut down for maintenance. Engineering safety during maintenance outages is determined by the stability of the tunnel face. Pressure maintenance openings are typically used under complicated hydrogeological conditions. The tunnel face is supported by a medium at the bottom of the excavation chamber and compressed air at the top. Owing to the high risk of face failure, the necessity of support pressure when cutterhead support is implemented and a method for determining the value of compressed air pressure using different support ratios must to be determined. In this study, a non-fully chamber supported rotational failure model considering cutterhead support is developed based on the upper-bound theorem of limit analysis. Numerical simulation is conducted to verify the accuracy of the proposed model. The results indicate that appropriately increasing the specific gravity of the supporting medium can reduce the risk of collapse. The required compressed air pressure increases significantly as the support ratio decreases. Disregarding the supporting effect of the cutterhead will result in a tunnel face with underestimated stability. To satisfy the requirement of chamber openings at atmospheric pressure, the stratum reinforcement strength and range at the shield end are provided based on different cutterhead aperture ratios.

The stress concentration of pipe structure or cavity defect has a great effect on the mechanical properties of the high-performance concrete (HPC) members in deep underground locations. However, the behaviour of HPC with cavities under triaxial compression is not understood, especially when pressurized liquid flows into the fractures from the cavity. This study aims to investigate the effect of the cavity and the confining pressure on the failure mechanisms, strengths, and deformation properties of HPC with a new experimental scheme. In this experiment, the pressurized liquid can only contact the surface of the sample in the cavity, while the other surfaces are isolated from the pressurized liquid. To further explore the effect of the cavity, the same experiments are also conducted on sealed and unsealed intact samples without a cavity. The failure modes and stress-strain curves of all types of the samples are presented. Under various confining pressures, all the samples with a cavity suffer shear failure, and there are always secondary tensile fractures initiating from the cavity sidewall. Additionally, it can be determined from the failure modes and the stress-strain curves that the shear fractures result from the sidewall failure. Based on the different effects of the cavity on the lateral deformations in different directions, the initiation of the sidewall fracture is well predicted. The experimental results show that both the increase of the confining pressure and the decrease of the cavity size are conducive to the initiation of sidewall fracture. Moreover, the cavity weakens the strength of the sample, and this study gives a modified Power-law criterion in which the cavity size is added as an impact factor to predict the strength of the sample.

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.

An experimental study is performed on five post-tensioned concrete beams to explore the effects of different fracture positions on secondary transfer length and residual prestress of fractured strand. A numerical model is developed and used to predict the secondary transfer length and residual prestress of fractured strand in post-tensioned concrete beams. The model change interaction, which can deactivate and reactivate the elements for simulating the removal and reproduction of parts of the model, is used to reproduce the secondary anchorage of fractured strand. The numerical model is verified by experimental results. Results shows that the fractured strand can be re-anchored in concrete through the secondary anchorage, and the secondary transfer length of fractured strand with the diameter of 15.2 mm is 1133 mm. The residual prestress of fractured strand increases gradually in the secondary transfer length, and tends to be a constant beyond it. When the fractured strand is fully anchored in concrete, a minor prestress loss will appear, and the average prestress loss is 2.28% in the present study.

The seismic analysis of a viscoelastic half-space under two-dimensional (2D) oblique incident waves is carried out by the finite/infinite element method (FIEM). First, the frequency-domain exact solutions for the displacements and stresses of the free field are derived in general form for arbitrary incident P and SV waves. With the present formulation, no distinction needs to be made for SV waves with over-critical incident angles that make the reflected P waves disappear, while no critical angle exists for P waves. Next, the equivalent seismic forces of the earthquake (Taft Earthquake 1952) imposed on the near-field boundary are generated by combining the solutions for unit ground accelerations with the earthquake spectrum. Based on the asymmetric finite/infinite element model, the frequency-domain motion equations for seismic analysis are presented with the key parameters selected. The results obtained in frequency and time domain are verified against those of Wolf’s, Luco and de Barros’ and for inversely computed ground motions. The parametric study indicated that distinct phase difference exists between the horizontal and vertical responses for SV waves with over-critical incident angles, but not for under-critical incident angles. Other observations were also made for the numerical results inside the text.

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.

High viscosity asphalt (HVA) has been a great success as a drainage pavement material. However, the larger porosity of drainage asphalt mixtures weakens the cohesion and adhesion and leads to premature rutting, water damage, spalling and cracking. The purpose of this study was to investigate the rheological properties of HVA prepared using different high viscosity modifiers through conventional tests, Brookfield viscosity tests, dynamic shear rheometer tests and bending beam rheometer tests. The conventional performance results demonstrated SBS + rubber asphalt (SRA-1/2) exhibited excellent elastic recovery and low-temperature flexibility. The 60°C dynamic viscosity results indicated TPS + rubber asphalt (TRA) had the excellent adhesion. The rotational viscosity results and rheological results indicated that SRA-2 not only exhibited excellent temperature stability and workability, as well as excellent resistance to deformation and rutting resistance, but also exhibited excellent low-temperature cracking resistance and relaxation performance. Based on rheological results, the PG classification of HVA was 16% rubber + asphalt for PG76-22, 20% rubber + asphalt for PG88-22, TRA and SRA-1/2 for PG88-28. From comprehensive evaluation of the viscosity, temperature stability and sensitivity, as well as high/low temperature performance of HVA, SRA-2 was found to be more suited to the requirements of drainage asphalt pavement materials.

We present a three-dimensional (3D) numerical model to investigate complex fracture behavior using cohesive elements. An efficient packing algorithm is employed to create the mesoscale model of heterogeneous capsule-based self-healing concrete. Spherical aggregates are used and directly generated from specified size distributions with different volume fractions. Spherical capsules are also used and created based on a particular diameter, and wall thickness. Bilinear traction-separation laws of cohesive elements along the boundaries of the mortar matrix, aggregates, capsules, and their interfaces are pre-inserted to simulate crack initiation and propagation. These pre-inserted cohesive elements are also applied into the initial meshes of solid elements to account for fracture in the mortar matrix. Different realizations are carried out and statistically analyzed. The proposed model provides an effective tool for predicting the complex fracture response of capsule-based self-healing concrete at the meso-scale.

This paper presents a combined method based on optimized neural networks and optimization algorithms to solve structural optimization problems. The main idea is to utilize an optimized artificial neural network (OANN) as a surrogate model to reduce the number of computations for structural analysis. First, the OANN is trained appropriately. Subsequently, the main optimization problem is solved using the OANN and a population-based algorithm. The algorithms considered in this step are the arithmetic optimization algorithm (AOA) and genetic algorithm (GA). Finally, the abovementioned problem is solved using the optimal point obtained from the previous step and the pattern search (PS) algorithm. To evaluate the performance of the proposed method, two numerical examples are considered. In the first example, the performance of two algorithms, OANN + AOA + PS and OANN + GA + PS, is investigated. Using the GA reduces the elapsed time by approximately 50% compared with using the AOA. Results show that both the OANN + GA + PS and OANN + AOA + PS algorithms perform well in solving structural optimization problems and achieve the same optimal design. However, the OANN + GA + PS algorithm requires significantly fewer function evaluations to achieve the same accuracy as the OANN + AOA + PS algorithm.

This paper introduces a machine learning approach to address the challenge of limited data resulting from costly and time-consuming fire experiments by enlarging small fire test data sets and predicting the fire resistance of reinforced concrete columns. Our approach begins by creating deep learning models, namely generative adversarial networks and variational autoencoders, to learn the spatial distribution of real fire tests. We then use these models to generate synthetic tabular samples that closely resemble realistic fire resistance values for reinforced concrete columns. The generated data are employed to train state-of-the-art machine learning techniques, including Extreme Gradient Boost, Light Gradient Boosting Machine, Categorical Boosting Algorithm, Support Vector Regression, Random Forest, Decision Tree, Multiple Linear Regression, Polynomial Regression, Support Vector Machine, Kernel Support Vector Machine, Naive Bayes, and K-Nearest Neighbors, which can predict the fire resistance of the columns through regression and classification. Machine learning analyses achieved highly accurate predictions of fire resistance values, outperforming traditional models that relied solely on limited experimental data. Our study highlights the potential for using machine learning and deep learning analyses to revolutionize the field of structural engineering by improving the accuracy and efficiency of fire resistance evaluations while reducing the reliance on costly and time-consuming experiments.

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.

Blasting engineering in complex urban environments is considered to influence the safety and stability of the overlying drainage box culvert structure owing to vibration. Therefore, field blasting and vibration tests were performed on the blasting engineering of the Wuhan Metro Line 8 connected aisle, and the LS-DYNA software was used to analyze the dynamic response characteristics of an underground drainage box culvert during the blasting test. The vibration response evolution law of the buried drainage box culvert under blasting vibration was investigated, and a safe surface control standard for the blast vibration of a drainage box culvert is proposed. The results reveal that the maximum tensile stress of the box culvert structure was 0.33 MPa. The peak particle velocity (PPV) and peak tensile stress (PTS) of the drainage box culvert decreased as the water level in the box culvert increased. Based on the relationship between the tensile stress of the box culvert, PPV of the box culvert, and PPV of the surface, it is proposed that the surface control velocity of the buried drainage box culvert is 1.36 cm/s.

Proven research output on the behavior of structures made of waste copper slag concrete can improve its utilization in the construction industry and thereby help to develop a sustainable built environment. Although numerous studies on waste copper slag concrete can be found in the published literature, no research has focused on the structural application of this type of concrete. In particular, the variability in the strength properties of waste copper slag concrete, which is required for various structural applications, such as limit state design formulation, reliability-based structural analysis, etc., has so far not attracted the attention of researchers. This paper quantifies the uncertainty associated with the compressive-, flexural- and split tensile strength of hardened concrete with different dosages of waste copper slag as fine aggregate. Best-fit probability distribution models are proposed based on statistical analyses of strength data generated from laboratory experiments. In addition, the paper presents a reliability-based seismic risk assessment of a typical waste copper slag incorporated reinforced concrete framed building, considering the proposed distribution model. The results show that waste copper slag can be safely used for seismic resistant structures as it results in an identical probability of failure and dispersion in the drift demand when compared with a conventional concrete building made of natural sand.