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.

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.

Previous studies on concrete have identified silica fume (SF) as the most effective supplementary material, whereas fly ash (FA) and slag have been identified as economical materials with long term strength potential. Development of blended cement mortar referred to as blended mortar (BM) requires similar assessment. The present study explores the application of Alccofine (AL) as supplementary material and compares its performance with conventional materials namely SF, FA and ground granulated blast furnace slag (GGBS). The mortar specimens with binder to fine-aggregates (b/f ) ratio of 1:2 are prepared at water to binder (w/b) ratios of 0.4 and 0.35. The strength values and stress-strain curve for control and BM specimens are obtained at 7, 28, 56, and 90 d curing periods. The assessment based on strength activity index, k-value method and strength estimation model confirms that AL, despite lower pozzolanic activity, contributes to strength gain, due to reduced dilution effect. Assessment of stress-strain curves suggests that the effect of w/b ratio is more dominant on the elastic modulus of BM specimens than on control specimens. The observations from the study identify enhanced strength gain, improved elastic modulus and higher energy absorption as key contributions of AL making it a potential supplementary material.

In recent building practice, rapid construction is one of the principal requisites. Furthermore, in designing concrete structures, compressive strength is the most significant of all parameters. While 3-d and 7-d compressive strength reflects the strengths at early phases, the ultimate strength is paramount. An effort has been made in this study to develop mathematical models for predicting compressive strength of concrete incorporating ethylene vinyl acetate (EVA) at the later phases. Kolmogorov-Smirnov (KS) goodness-of-fit test was used to examine distribution of the data. The compressive strength of EVA-modified concrete was studied by incorporating various concentrations of EVA as an admixture and by testing at ages of 28, 56, 90, 120, 210, and 365 d. An accelerated compressive strength at 3.5 hours was considered as a reference strength on the basis of which all the specified strengths were predicted by means of linear regression fit. Based on the results of KS goodness-of-fit test, it was concluded that KS test statistics value (D) in each case was lower than the critical value 0.521 for a significance level of 0.05, which demonstrated that the data was normally distributed. Based on the results of compressive strength test, it was concluded that the strength of EVA-modified specimens increased at all ages and the optimum dosage of EVA was achieved at 16% concentration. Furthermore, it was concluded that predicted compressive strength values lies within a 6% difference from the actual strength values for all the mixes, which indicates the practicability of the regression equations. This research work may help in understanding the role of EVA as a viable material in polymer-based cement composites.

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.

The aim of this study is to develop concrete composites that are resistant to armor-piercing projectiles for defense structures. Different reinforcement configurations have been tested, such as short steel fibers, long steel fibers, and steel mesh reinforcement. Three different concrete mix designs were prepared as “Ultra High Performance (UHPFRC), High Performance (HPFRC) and Conventional (CFRC) Fiber Reinforced Concrete”. The content of hybrid steel fibers was approximately 5% in the UHPFRC and HPFRC mixtures, while the steel fiber content was approximately 2.5% in the CFRC mixture. In addition, a plain state of each mixture was produced. Mechanical properties of concrete were determined in experimental studies. In addition to the fracture energy and impact strength, two important indicators of ballistic performance of concrete are examined, which are the penetration depth and damage area. The results of the study show that the depth of penetration in UHPFRC was around 35% less than that in HPFRC. It was determined that the mixtures of UHPFRC and HPFRC containing 5% by volume of hybrid steel fibers showed superior performance (smaller crater diameter and the less projectile penetration depth) against armor-piercing projectiles in ballistic tests and could be used in defense structures.

In the past, glass fiber-reinforced polymer (GFRP)-reinforcement has been successfully applied in reinforced concrete (RC) structures where corrosion resistance, electromagnetic neutrality, or cuttability were required. Previous investigations suggest that the application of GFRP in RC structures could be advantageous in areas with seismic activity due to their high deformability and strength. However, especially the low modulus of elasticity of GFRP limited its wide application as GFRP-reinforced members usually exhibit considerably larger deformations under service loads than comparable steel-reinforced elements. To overcome the aforementioned issues, the combination of steel and GFRP reinforcement in hybrid RC sections has been investigated in the past. Based on this idea, this paper presents a novel concept for the predetermination of potential plastic hinges in RC frames using GFRP reinforcement. To analyze the efficiency of the concept, nonlinear finite element simulations were performed. The results underscore the high efficiency of hybrid steel-GFRP RC sections for predetermining potential plastic hinges on RC frames. The results also indicate that the overall seismic behavior of RC structures could be improved by means of GFRP as both the column base shear force during the seismic activity as well as the plastic deformations after the earthquake were considerably less pronounced than in the steel-reinforced reference structure.

This paper presents a reliability-based settlement analysis of T-shaped deep cement mixing (TDM) pile-supported embankments over soft soils. The uncertainties of the mechanical properties of the in-situ soil, pile, and embankment, and the effect of the pile shape are considered simultaneously. The analyses are performed using Monte Carlo Simulations in combination with an adaptive Kriging (using adaptive sampling algorithm). Individual and system failure probabilities, in terms of the differential and maximum settlements (serviceability limit state (SLS) requirements), are considered. The reliability results for the embankments supported by TDM piles, with various shapes, are compared and discussed together with the results for conventional deep cement mixing pile-supported embankments with equivalent pile volumes. The influences of the inherent variabilities in the material properties (mean and coefficient of variation values) on the reliability of the piled embankments, are also investigated. This study shows that large TDM piles, particularly those with a shape factor of greater than 3, can enhance the reliability of the embankment in terms of SLS requirements, and even avoid unacceptable reliability levels caused by variability in the material properties.

The purpose of this research was to develop statistical and intelligent models for predicting the severity of road traffic accidents (RTAs) on rural roads. Multiple Logistic Regression (MLR) was used to predict the likelihood of RTAs. For more accurate prediction, Multi-Layer Perceptron (MLP) and Radius Basis Function (RBF) neural networks were applied. Results indicated that in MLR, the model obtained from the backward method with the correct percent of 84.7% and R² value of 0.893 was the best method for predicting the likelihood of RTAs. Also, MLR showed that the variables of not paying attention to the front not paying attention to the frontroad ahead, followed byand then vehicle-motorcycle/bike accidents were the greatest problems. Among the models, MLP had a better performance, so that the prediction accuracy of MLR, MLP, and RBF were 84.7%, 96.7%, and 92.1%, respectively. MLP model, due to higher accuracy, showed that the variable of reason of accident had the highest effect on the prediction of accidents, and considering MLR results, the variables of not paying attention to the front and then vehicle-motorcycle/bike accidents had the most influence on the occurrence of accidents. Therefore, motorcyclists and cyclists are more prone to accidents, and appropriate solutions should be adopted to enhance their safety.