On June 24, 2021, a 40-year-old reinforced concrete flat plate structure building in Miami suffered a sudden partial collapse. This study analyzed the overall performance and key components of the collapsed building based on the building design codes (ACI-318 and GB 50010). Punching shear and post-punching performances of typical slab-column joints are also studied through the refined finite element analysis. The collapse process was simulated and visualized using a physics engine. By way of these analyses, weak design points of the collapsed building are highlighted. The differences between the reinforcement detailing of the collapsed building and the requirements of the current Chinese code are discussed, together with a comparison of the punching shear and post-punching performances. The simulated collapse procedure and debris distribution are compared with the actual collapse scenes.

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.

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.

The oceans are crucial to human civilization. They provide core support for exploitation and utilization of marine space, resources, and energy. Thus, marine infrastructures are vital to a nation’s economic sustainable development. To this end, this article first describes the main challenges in current ocean utilization, and then reviews the China’s ocean engineering progress. As such, six major sectors are evaluated: 1) global climate change and marine environment, 2) comprehensive utilization of marine space, 3) marine transportation infrastructure interconnection, 4) ocean clean energy development and maricultural facilities, 5) ecological crisis and marine engineering countermeasures, and 6) marine infrastructure operation safety and maintenance. Finally, perspectives on future directions of ocean utilization and marine infrastructure construction in China are provided.

Microbial geotechnology or biogeotechnology is a new branch of geotechnical engineering. It involves the use of microbiology for traditional geotechnical applications. Many new innovative soil improvement methods have been developed in recent years based on this approach. A proper understanding of the various approaches and the performances of different methods can help researchers and engineers to develop the most appropriate geotechnical solutions. At present, most of the methods can be categorized into three major types, biocementation, bioclogging, and biogas desaturation. Similarities and differences of different approaches and their potential applications are reviewed. Factors affecting the different processes are also discussed. Examples of up-scaled model tests and pilot trials are presented to show the emerging applications. The challenges and problems of biogeotechnology are also discussed.

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.

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 study presents stability analyses of layered soil slopes in unsaturated conditions and uses a limit equilibrium method to determine the factor of safety involving suction stress of unsaturated soil. One-dimensional steady infiltration and evaporation conditions are considered in the stability analyses. An example of a two-layered slope in clay and silt is selected to verify the used method by comparing with the results of other methods. Parametric analyses are conducted to explore the influences of the matric suction on the stability of layered soil slopes. The obtained results show that larger suction stress provided in unsaturated clay dominates the stability of the layered slopes. Therefore, the location and thickness of the clay layer have significant influences on slope stability. As the water level decreases, the factor of safety reduces and then increases gradually in most cases. Infiltration/evaporation can obviously affect the stability of unsaturated layered slopes, but their influences depend on the soil property and thickness of the lower soil layer.

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.

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.

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.

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.

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.

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 this study, sprayable strain-hardening fiber-reinforced cementitious composites (FRCC) were applied to strengthen the concrete slabs in a concrete-face rockfill dam (CFRD) for the first time. Experimental, numerical, and analytical investigations were carried out to understand the flexural properties of FRCC-layered concrete slabs. It was found that the FRCC layer improved the flexural performance of concrete slabs significantly. The cracking and ultimate loads of a concrete slab with an 80 mm FRCC layer were 132% and 69% higher than those of the unstrengthened concrete slab, respectively. At the maximum crack width of 0.2 mm, the deflection of the 80-mm FRCC strengthened concrete slab was 144% higher than that of the unstrengthened concrete slab. In addition, a FE model and a simplified analytical method were developed for the design and analysis of FRCC-layered concrete slabs. Finally, the test result of FRCC leaching solution indicated that the quality of the water surrounding FRCC satisfied the standard for drinking water. The findings of this study indicate that the sprayable strain-hardening FRCC has a good potential for strengthening hydraulic structures such as CFRDs.

In recent years, great attention has focused on the development of automated procedures for infrastructures control. Many efforts have aimed at greater speed and reliability compared to traditional methods of assessing structural conditions. The paper proposes a multi-level strategy, designed and implemented on the basis of periodic structural monitoring oriented to a cost- and time-efficient tunnel control plan. Such strategy leverages the high capacity of convolutional neural networks to identify and classify potential critical situations. In a supervised learning framework, Ground Penetrating Radar (GPR) profiles and the revealed structural phenomena have been used as input and output to train and test such networks. Image-based analysis and integrative investigations involving video-endoscopy, core drilling, jacking and pull-out testing have been exploited to define the structural conditions linked to GPR profiles and to create the database. The degree of detail and accuracy achieved in identifying a structural condition is high. As a result, this strategy appears of value to infrastructure managers who need to reduce the amount and invasiveness of testing, and thus also to reduce the time and costs associated with inspections made by highly specialized technicians.

The ultra-high-performance concrete (UHPC) and fiber-reinforced polymer (FRP) are well-accepted high-performance materials in the field of civil engineering. The combination of these advanced materials could contribute to improvement of structural performance and corrosion resistance. Unfortunately, only limited studies are available for shear behavior of UHPC beams reinforced with FRP bars, and few suggestions exist for prediction methods for shear capacity. This paper presents an experimental investigation on the shear behavior of UHPC beams reinforced with glass FRP (GFRP) and prestressed with external carbon FRP (CFRP) tendons. The failure mode of all specimens with various shear span to depth ratios from 1.7 to 4.5 was diagonal tension failure. The shear span to depth ratio had a significant influence on the shear capacity, and the effective prestressing stress affected the crack propagation. The experimental results were then applied to evaluate the equations given in different codes/recommendations for FRP-reinforced concrete structures or UHPC structures. The comparison results indicate that NF P 18-710 and JSCE CES82 could appropriately estimate shear capacity of the slender specimens with a shear span to depth ratio of 4.5. Further, a new shear design equation was proposed to take into account the effect of the shear span to depth ratio and the steel fiber content on shear capacity.

Reinforced concrete beams consisting of both steel and glass-fiber-reinforced polymer rebars exhibit excellent strength, serviceability, and durability. However, the fatigue shear performance of such beams is unclear. Therefore, beams with hybrid longitudinal bars and hybrid stirrups were designed, and fatigue shear tests were performed. For specimens that failed by fatigue shear, all the glass-fiber-reinforced polymer stirrups and some steel stirrups fractured at the critical diagonal crack. For the specimen that failed by the static test after 8 million fatigue cycles, the static capacity after fatigue did not significantly decrease compared with the calculated value. The initial fatigue level has a greater influence on the crack development and fatigue life than the fatigue level in the later phase. The fatigue strength of the glass-fiber-reinforced polymer stirrups in the specimens was considerably lower than that of the axial tension tests on the glass-fiber-reinforced polymer bar in air and beam-hinge tests on the glass-fiber-reinforced polymer bar, and the failure modes were different. Glass-fiber-reinforced polymer stirrups were subjected to fatigue tension and shear, and failed owing to shear.

This paper provides insight into the seismic behavior of a full-scale precast reinforced concrete wall under in-plane cyclic loading combined with out-of-plane loading replicated by sand backfill to simulate the actual condition of basement walls. The tested wall exhibited flexural cracks, owing to the high aspect ratio and considerable out-of-plane movement due to lateral pressure from the backfill. The wall performed satisfactorily by exhibiting competent seismic parameters and deformation characteristics governed by its ductile response in the nonlinear phase during the test with smaller residual drift. Numerical analysis was conducted to validate experimental findings, which complied with each other. The numerical model was used to conduct parametric studies to study the effect of backfill density and aspect ratio on seismic response of the proposed precast wall system. The in-plane capacity of walls reduced, while deformation characteristics were unaffected by the increase in backfill density. An increase in aspect ratio leads to a reduction in in-plane capacity and an increase in drift. Curves between the ratio of in-plane yield capacity and design shear load of walls are proposed for the backfill density, which may be adopted to determine the in-plane yield capacity of the basement walls based on their design shear.

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.

Normally, the edge effects of surficial landslides are not considered in the infinite slope method for surficial stability analysis of soil slopes. In this study, the limit stress state and discrimination equation of an infinite slope under saturated seepage flow were analyzed based on the Mohr-Coulomb strength criterion. Therefore, a novel failure mode involving three sliding zones (upper tension zone, middle shear sliding zone, and lower compression zone) was proposed. Accordingly, based on the limit equilibrium analysis, a semi-analytical framework considering the edge effect for the surficial stability of a soil slope under downslope seepage was established. Subsequently, the new failure mode was verified via a numerical finite element analysis based on the reduced strength theory with ABAQUS and some simplified methods using SLIDE software. The results obtained by the new failure mode agree well with those obtained by the numerical analysis and traditional simplified methods, and can be efficiently used to assess the surficial stability of soil slopes under rainwater seepage. Finally, an evaluation of the infinite slope method was performed using the semi-analytical method proposed in this study. The results show that the infinite slope tends to be conservative because the edge effect is neglected, particularly when the ratio of surficial slope length to depth is relatively small.

The wave of “digital age” featuring digital information is coming. Digital technology is profoundly changing the societal development direction and evolution paths. It also has significant bearing on production modes, social interactions and lifestyles. With regard to urban design, a system of knowledge about the creation and adaptation of material space forms that integrate humanities, art, technology and materials, digital technology has provided it with a brand-new and revolutionary scientific impetus for its evolution. The result of this evolution is “digital urban design paradigm based on human-computer interaction”, i.e., the urban development is moving toward “pan-dimensionality” and “individual ubiquity”. The future of urban design will construct a new approach to urban research and engineering, which is more complex, capable of accommodating and compatible with multiple goals of “instrumental rationality” and “value rationality”. Such a new approach shall be led by the probabilistic theory of “gray scale thinking”, reflecting quaternary synergetic view of “scientific rationality, ecological rationality, cultural rationality and technical rationality” to realize the cognitive progress of “engineering for the benefit of mankind”.

The inspection of water conveyance tunnels plays an important role in water diversion projects. Siltation is an essential factor threatening the safety of water conveyance tunnels. Accurate and efficient identification of such siltation can reduce risks and enhance safety and reliability of these projects. The remotely operated vehicle (ROV) can detect such siltation. However, it needs to improve its intelligent recognition of image data it obtains. This paper introduces the idea of ensemble deep learning. Based on the VGG16 network, a compact convolutional neural network (CNN) is designed as a primary learner, called Silt-net, which is used to identify the siltation images. At the same time, the fully-connected network is applied as the meta-learner, and stacking ensemble learning is combined with the outputs of the primary classifiers to obtain satisfactory classification results. Finally, several evaluation metrics are used to measure the performance of the proposed method. The experimental results on the siltation dataset show that the classification accuracy of the proposed method reaches 97.2%, which is far better than the accuracy of other classifiers. Furthermore, the proposed method can weigh the accuracy and model complexity on a platform with limited computing resources.

This study investigated the use of recycled tire-derived aggregate (TDA) mixed with kaolin as a method of increasing the ultimate bearing capacity ( UBC) of a strip footing. Thirteen 1g physical modeling tests were prepared in a rigid box of 0.6 m × 0.9 m in plan and 0.6 m in height. During sample preparation, 0%, 20%, 40%, or 60% (by weight) of powdery, shredded, small-sized granular (G 1–4 mm) or large-sized granular (G 5–8 mm) TDA was mixed with the kaolin. A strip footing was then placed on the stabilized kaolin and was caused to fail under stress-controlled conditions to determine the UBC. A rigorous 3D finite element analysis was developed in Optum G-3 to determine the UBC values based on the experimental test results. The experimental results showed that, except for the 20% powdery TDA, the TDA showed an increase in the UBC of the strip footing. When kaolin mixed with 20% G (5–8 mm), the UBC showed a threefold increase over that for the unreinforced case. The test with 20% G (1–4 mm) recorded the highest subgrade modulus. It was observed that the UBC calculated using finite element modeling overestimated the experimental UBC by an average of 9%.

Assessing the durability of concrete is of prime importance to provide an adequate service life and reduce the repairing cost of structures. Freeze–thaw is one such test that indicates the ability of concrete to last a long time without a significant loss in its performance. In this study, the freeze–thaw resistance of polymer concrete containing different polymer contents was explored and compared to various conventional cement concretes. Concretes’ fresh and hardened properties were assessed for their workability, air content, and compressive strength. The mass loss, length change, dynamic modulus of elasticity, and residual compressive strength were determined for all types of concretes subjected to freeze–thaw cycles according to ASTM C666-procedure A. Results showed that polymer concrete (PC) specimens prepared with higher dosages of polymer contents possessed better freeze–thaw durability compared to other specimens. This high durability performance of PCs is mainly due to their impermeable microstructures, absence of water in their structure, and the high bond strength between aggregates and a polymer binder. It is also indicated that the performance of high-strength concrete containing air-entraining admixture is comparable with PC having optimum polymer content in terms of residual compressive strength, dynamic modulus of elasticity, mass loss, and length change.

The performance of the wood-frame buildings after tornadoes has shown that the majority of the wind damage resulted from building envelope failure most typically due to the loss of the roof. To assess the performance and the reliability of low-rise wood-frame residential buildings with a focus on the roofs, fragility analysis can be used to estimate the probability of failure of a roof when constructed with specified nails and sheathing sizes. Thus, this paper examines the fragility of specific types of nails, roof-to-wall (RW) connection details, and sheathing sizes based on the damaged roofs that were previously assessed in the Dunrobin area in Ottawa (Ontario) that was hit with an Enhanced Fujita (EF3) tornado on September 21, 2018. The presented fragility analysis considers four scenarios, including different sheathing and nail sizes. Dead loads, wind loads, and resistance on the sheathing panels were compiled and analyzed to determine the failure of the examined roofs. The eight fragility models suggest that the safest roof sheathing (RS) is the 1.22 m × 1.22 m sheathing panel with 8 d nails, and the safest RW connections is achieved by using H2.5 hurricane clips.

To reveal the potential influence of styrene-butadiene-styrene (SBS) polymer modification on the anti-aging performance of asphalt, and its mechanism, we explored the aging characteristics of base asphalt and SBS-modified asphalt by reaction force field (ReaxFF) and classical molecular dynamics simulations. The results illustrate that the SBS asphalt is more susceptible to oxidative aging than the base asphalt under oxygen-deficient conditions due to the presence of unsaturated C=C bonds in the SBS polymer. In the case of sufficient oxygen, the SBS polymer inhibits the oxidation of asphalt by restraining the diffusion of asphalt molecules. Compared with the base asphalt, the SBS asphalt exhibits a higher degree of oxidation at the early stage of pavement service and a lower degree of oxidation in the long run. In addition, SBS polymer degrades into small blocks during aging, thus counteracting the hardening of aged asphalt and partially restoring its low-temperature cracking resistance.

This paper utilizes three popular semantic segmentation networks, specifically DeepLab v3+, fully convolutional network (FCN), and U-Net to qualitively analyze and identify the key components of cutting slope images in complex scenes and achieve rapid image-based slope detection. The elements of cutting slope images are divided into 7 categories. In order to determine the best algorithm for pixel level classification of cutting slope images, the networks are compared from three aspects: a) different neural networks, b) different feature extractors, and c) 2 different optimization algorithms. It is found that DeepLab v3+ with Resnet18 and Sgdm performs best, FCN 32s with Sgdm takes the second, and U-Net with Adam ranks third. This paper also analyzes the segmentation strategies of the three networks in terms of feature map visualization. Results show that the contour generated by DeepLab v3+ (combined with Resnet18 and Sgdm) is closest to the ground truth, while the resulting contour of U-Net (combined with Adam) is closest to the input images.

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.

Typical effects of coarse and fine aggregates on the long-term properties of sea sand recycled aggregate concrete (SSRAC) are analyzed by a series of axial compression tests. Two different types of fine (coarse) aggregates are considered: sea sand and river sand (natural and recycled coarse aggregates). Variations in SSRAC properties at different ages are investigated. A novel test system is developed via axial compression experiments and the digital image correlation method to obtain the deformation field and crack development of concrete. Supportive results show that the compressive strength of SSRAC increase with decreasing recycled coarse aggregate replacement percentage and increasing sea sand chloride ion content. The elastic modulus of SSRAC increases with age. However, the Poisson’s ratio reduces after 2 years. Typical axial stress–strain curves of SSRAC vary with age. Generally, the effect of coarse aggregates on the axial deformation of SSRAC is clear; however, the deformation differences between coarse aggregate and cement mortar reduce by adopting sea sand. The aggregate type changes the crack characteristics and propagation of SSRAC. Finally, an analytical expression is suggested to construct the long-term stress–strain curve of SSRAC.