一种新的模拟潮差区混凝土受氯盐侵蚀试验方法

针对目前实验室内模拟潮差区混凝土受氯盐侵蚀干湿循环试验方法中存在的难点,提出了一种新的海洋潮汐环境的室内模拟试验方法。以实际码头工程为参照,设计了相应的室内模拟试验,通过对现场混凝土墙及室内混凝土试件的取样分析从浸润时间比的角度讨论了两者之间的相关性。试验结果证明该模拟试验方法能有效模拟混凝土结构在实际海洋潮汐环境下的氯盐侵蚀情况。     

基于人工神经网络的混凝土疲劳寿命估算

把决定混凝土疲劳寿命的应力水平，应力比，加载频率和描述随机性的破坏概率作为神经网络的输入，以疲劳寿命作为网络的输出，用网络结构描述它们之间的非线性关系，利用实验数据样本完成了网络的训练，并对模拟样本进行了估算，实例计算了结果表明，利用已有疲劳试验数据训练好的人工神经网络进行混凝土疲劳寿命估算，可部分代替冗长，昂贵的疲劳实验。     

人工鱼礁在自然海水条件下的腐蚀寿命研究

有氯盐侵蚀的钢筋腐蚀,是钢筋混凝土结构性能劣化的主要原因之一,混凝土构件使用寿命的关键是氯离子的传输速率和使钢筋产生锈蚀的时间。本文以人工鱼礁混凝土构件为研究对象,分析海水氯离子在C30、C35和钢渣复合材料人工鱼礁混凝土中的扩散系数,以混凝土中钢筋锈蚀的临界氯离子浓度为寿命预测的边界条件,利用氯离子扩散模型预测C30、C35和钢渣复合材料人工鱼礁在海水中的腐蚀寿命。研究结果表明,氯离子在混凝土人工鱼礁中的扩散规律基本满足Fick第二定律。通过模型计算得出C30、C35和钢渣复合材料混凝土人工鱼礁的腐蚀寿命分别为51.6,54.8和56.8年。     

人工鱼礁在自然海水条件下的腐蚀寿命研究

有氯盐侵蚀的钢筋腐蚀,是钢筋混凝土结构性能劣化的主要原因之一,混凝土构件使用寿命的关键是氯离子的传输速率和使钢筋产生锈蚀的时间。本文以人工鱼礁混凝土构件为研究对象,分析海水氯离子在C30、C35和钢渣复合材料人工鱼礁混凝土中的扩散系数,以混凝土中钢筋锈蚀的临界氯离子浓度为寿命预测的边界条件,利用氯离子扩散模型预测C30、C35和钢渣复合材料人工鱼礁在海水中的腐蚀寿命。研究结果表明,氯离子在混凝土人工鱼礁中的扩散规律基本满足Fick第二定律。通过模型计算得出C30、C35和钢渣复合材料混凝土人工鱼礁的腐蚀寿命分别为51.6,54.8和56.8年。     

平潭大屿岛上台湾相思长势的时空差异性

为了探讨台湾相思群落生长发育与气候特征的关系,选用11个位于大屿靠海一侧不同地点的台湾相思群落研究对象。通过2016年4—12月对台湾相思标记枝条的生长状况进行长期监测。观测结果表明:11个样点受盐害叶片占比为1.16%~51.48%,受虫害叶片占比为30.27%~59.78%。植株大多受到盐害与虫害的干扰,花数量和花蕾数量处于一个较低的水平。由植物生长状况与气候特征的相关性结果可知,风向和风力随月份变化而变化,12月风力最高达5.93m/s。月份和风向与受盐害叶片占比、受虫害叶片占比均呈现极显著相关性。在开展海岛植被生态修复时,需要注重冬季养护、病虫害防治和筛选抗风、耐盐雾的海岛适生物种。     

海水环境下钢筋混凝土人工鱼礁的耐久性寿命预测

根据人工模拟的流动海水环境(流速约为0.15m/s)下的混凝土侵蚀试验测得的混凝土中氯离子质量分数,分别测定了腐蚀350,380,400d后混凝土的氯离子扩散系数,并计算和预测了混凝土人工鱼礁的耐久性寿命。研究结果表明:氯离子在混凝土人工鱼礁中的扩散规律基本上遵循Fick第二定律;钢筋混凝土人工鱼礁的耐久性寿命与保护层厚度的平方成正比;海水盐度对混凝土人工鱼礁的耐久性寿命影响较大。     

海洋潮汐环境下混凝土硫酸盐侵蚀机理研究

研究海洋潮汐区侵蚀环境下混凝土中硫酸盐侵蚀机理,对不同配合比混凝土试件在海洋潮汐环境下进行现场暴露试验。同时利用分光光度计法测定不同侵蚀龄期及不同配合比混凝土中硫酸根离子质量分数。试验结果表明：早期海洋暴露环境下,混凝土中总硫酸根离子质量分数随着腐蚀龄期的增加而显著升高,但随着时间增加幅度逐渐降低;混凝土中总硫酸根离子质量分数因水胶比、水泥用量不同而不同;一定比例的矿物掺合料可以有效提高混凝土抗硫酸盐侵蚀性能。     

河北省自然状态沙质海岸的侵蚀及预测

在系统分析河北省自然状态沙质海岸长期演变的基础上,运用"一线理论"及我国《海港水文规范》中的规范公式,建立海岸侵蚀数学模型,经2000、2006年实测数据验证,表明所建立模型的各参数确定合理,较真实地复演岸线侵蚀特征,可用于自然状态沙质海岸在较长时期内侵蚀预测。根据岸线的预测数据得出如下结论:2006—2026年,岸线整体后退速率不断减小,各岸段侵蚀速率有所不同。至2006年,戴河—洋河段侵蚀状态已基本停止,并逐渐趋于平衡;洋河—大蒲河段仍为侵蚀最严重地区,且侵蚀中心不断向南移动;大蒲河—塔子沟段受蚀速率明显减小,但随着侵蚀进一步进行以及沙源的减少,冲刷范围逐渐南移,将成为新的侵蚀中心地区。     

感潮水闸流量神经网络计算模型研究

感潮水闸流量的准确计算对于河网地区水闸引排水效益的分析和水闸综合管理体系的建立具有非常重要的意义。对感潮水闸的水力特性进行了详细分析,认为感潮水闸具有瞬时性和非线性等水力特点,提出采用人工神经网络理论建立其过流量的计算模型。建立了三种计算模式,应用浦东新区东沟水闸资料对不同模式进行了训练、测试和比较,推荐以水闸内外河水位、闸门开启度和上一时刻流量作为神经网络输入的计算模型为最终感潮水闸流量计算模型。研究表明,人工神经网络可以较好地隐含识别感潮水闸的多种出流类型,并具有较强的泛化和容错能力,从而为感潮河网地区水闸流量的计算提供了一种新的解决途径。     

基于广义扩散方程海工混凝土结构耐久寿命概率评估方法

针对国内现有的氯离子侵蚀寿命预测模型存在的问题,提出了基于广义扩散方程(GDF)的海洋环境下混凝土结构耐久寿命预测的概率方法.以某海上混凝土工程原位取粉测试及室内快速试验为手段,获得不同环境分区的时变模型参数统计值.采用随机抽样方法,实现结构各部位混凝土使用寿命的概率预测.通过对模型参数的敏感性分析讨论了该模型的优点.最后,提出了周期性检测,不断更新模型参数的动态寿命评估思路.     

基于人工神经网络的海底细粒土工程分类研究

海洋沉积物工程定名对于开展海洋工程建设具有重要作用,然而海底粉土和黏性土的定名受人为因素影响容易产生误差.使用人工神经网络的方法对黄河口埕岛海域284组细粒土数据进行了训练和学习,得到了只利用沉积物粒径质量分数进行定名的方法.结果表明,使用人工神经网络的方法能够有效地对沉积物进行工程定名.当网络含有5个输入层节点、9个...     

Artificial neural networks and their application to assessment of ultimate strength of plates with pitting corrosion

The potential for the structural capability degrading effects of both corrosion and fatigue induced cracks are of profound importance and must be both fully understood and reflected in vessel's inspection and maintenance programme. Corrosion has been studied and quantified by many researchers, however its effect on structural integrity is still subject to uncertainty, particularly with regards to localized corrosion. The present study is focused on assessing the effects of localized pitting corrosion on the ultimate strength of unstiffened plates. Over 265 non-linear finite-element analyses of panels with various locations and sizes of pitting corrosion have been carried out. The results indicate that the length, breadth and depth of pit corrosion have weakening effects on the ultimate strength of the plates while plate slenderness has only marginal effect on strength reduction. Transverse location of pit corrosion is also an important factor determining the amount of strength reduction. When corrosion spreads transversely on both edges, it has the most deteriorating effect on strength. In addition, artificial neural network (ANN) method is applied to derive a formula to predict ultimate strength reduction of locally corroded plates. It is found out that the proposed formulae can accurately predict the ultimate strength of locally corroded plates under uniaxial in-plane compression.     

防波堤的人工神经网络Monte Carlo法可靠性分析

防波堤建设费用巨大,且一旦遭到破坏,后果甚为严重,因此,如何准确地计算防波堤的可靠性意义重大.随着人工神经网络理论的快速发展,人工神经网络方法在结构可靠性分析中的应用逐渐得到重视.基于神经网络的Monte Carlo法计算直立式防波堤的可靠性,概率意义明确.以秦皇岛典型直立堤为算例,采用基于神经网络的Monte Carlo法对直立式防波堤进行可靠性分析时,将直立堤滑动破坏和倾覆破坏的极限状态方程中的所有参数均作为变量处理,并将计算结果与Monte Carlo模拟的直接抽样法、重要抽样法以及独立变量JC法的计算结果进行对比.结果表明:基于神经网络的Monte Carlo法和Monte Carlo模拟的直接抽样法、重要抽样法计算结果相近,而比独立变量JC法的计算结果略低.     

Prediction of Load Carrying Capacity of Corroded Reinforced Concrete Beam

A novel method for prediction of the load carrying capacity of a corroded reinforced concrete beam (CRCB) is presented in the paper. Nine reinforced concrete beams, which had been working in an aggressive environment for more than 10 years, were tested in the laboratory. Comprehensive tests, including flexural test, strength test for corroded concrete and rusty rebar, and pullout test for bond strength between concrete and rebar, were condueted. The flexural test results of CRCBs reveal that the distribution of surface cracks on the beams shows a fractal behavior. The relationship between the fractal dimensions and mechanical properties of CRCBs is then studied. A prediction model based on artificial neural network (ANN) is established by the use of the fractal dimension as the corrosion index, together with the basic intbrmation of the beam. The validity of the prediction model is demonstrated through the experimental data, and satisfactory resuits are achieved.     

Artificial neural network ability in evaluation of random wave-induced inline force on a vertical cylinder

An approach based on artificial neural network (ANN) is used to develop predictive relations between hydrodynamic inline force on a vertical cylinder and some effective parameters. The data used to calibrate and validate the ANN models are obtained from an experiment. Multilayer feed-forward neural networks that are trained with the back-propagation algorithm are constructed by use of three design parameters (i.e. wave surface height, horizontal and vertical velocities) as network inputs and the ultimate inline force as the only output. A sensitivity analysis is conducted on the ANN models to investigate the generalization ability (robustness) of the developed models, and predictions from the ANN models are compared to those obtained from Morison equation which is usually used to determine inline force as a computational method. With the existing data, it is found that least square method (LSM) gives less error in determining drag and inertia coefficients of Morison equation. With regard to the predicted results agreeing with calculations achieved from Morison equation that used LSM method, neural network has high efficiency considering its convenience, simplicity and promptitude. The outcome of this study can contribute to reducing the errors in predicting hydrodynamic inline force by use of ANN and to improve the reliability of that in comparison with the more practical state of Morison equation. Therefore, this method can be applied to relevant engineering projects with satisfactory results.     

Durability of Concrete with Different Improvement Measures and Its Service Life Prediction in Island and Reef Environment

To solve the durability of island and reef concrete engineering in the harsh environment of high temperature, high salt, high humidity and windy, the strength grade of concrete and the type of corrosion inhibitor were used as the influence factors, while the relative dynamic elastic modulus was used as the evaluation index. In addition, the law and time variability of the deterioration of concrete, the size effect, environmental similarity and the service life model were studied. The results showed that improving the strength grade of concrete could improve the durability of concrete, and corrosion inhibitor could slightly improve the durability of concrete. Time-varying law of the deterioration of concrete conformed to the univariate quadratic polynomial. Combined with the concrete damage equivalent theory, a size effect model based on the relative dynamic elastic modulus was proposed and verified, and the size effect coefficient was also given. An environmental similarity model between simulated and practical island and reef environment was proposed. Combined with the reliability theory and the first order second moment method, a new service life model of concrete structure was proposed. The authors were convinced that the research will be advantageous to researchers.

     

Experimental Investigation of Unconfined Compression Strength and Stiffness of Cement Treated Salt-Rich Clay

Soft clay with high sodium chloride salt concentration is a problem encountered by geotechnical and highway engineers. Chemical stabilization using cement is an attractive method to improve the engineering properties of soft soil. However, very limited studies have been conducted to reveal the effect of salt concentration on the engineering properties of cement-stabilized soil and the reported results in literature are not consistent. The impact of sodium chloride salt on the strength and stiffness properties of cement-stabilized Lianyungang marine clay is studied in this study. The clay with various sodium chloride salt concentrations was prepared artificially and stabilized by various contents of Ordinary Portland cement. A series of unconfined compressive strength (UCS) tests of cement stabilized clay specimen after 7, 14, and 28 days curing periods were carried out. The results indicate that a high sodium chloride salt concentration has a detrimental effect on the UCS and stiffness of cement-stabilized clay. The detrimental effect of salt concentration on the strength and stiffness of cement-stabilized clay directly relates to cement content. Soils mixed with high cement content are more resistant to the negative effect of salts than soils mixed with low cement content. The ratio of modulus of elasticity to UCS of cement treated soil does not have an obvious relationship with salt concentration. The findings of this study present a rational basis for the understanding of the impact of salt on the engineering properties of cement-treated soil.     

Effects of Curing Conditions on Strength Development of High Strength Concrete

This paper presents the results of a series of studies on the influence of curing conditions onthe strength development of high strength concrete.The 1-,3-,7-,14-and 28-day strengths of four dif-ferent mixes of Grade 75～80 concrete,with or without pulverized fuel ash and/or condensed silica fume,under five different curing regimes were investigated.It is revealed that the curing conditions have signifi-cant influence on both the short term and long term strength development of the concrete and that con-crete mixes of the same grade but containing different mineral admixtures show distinct favour for a cur-ing regime.These results will be helpful for evaluating suitable curing methods for high strength concretewith different mix proportions.     

神经网络在海洋天然气水合物检测中的应用

文章主要讨论了如何利用神经网络预测天然气水合物的合成和分解。利用了声速、幅度、频率来反映天然气水合物的合成。建立了一个3层前向型网络,通过实验,人工神经网络的引用取得了良好的效果。     

Artificial intelligence-based hull structural plate corrosion damage detection and recognition using convolutional neural network

With the support of big data and GPU acceleration training, the artificial intelligence technology with deep learning as its core is developing rapidly and has been widely used in many fields. At the same time, feature extraction operations are required by the current image-based corrosion damage detection method in the field of ships, with little effect but consuming the large amount of manpower and financial resources. Therefore, a new method for hull structural plate corrosion damage detection and recognition based on artificial intelligence using convolutional neural network is proposed. The convolutional neural network (CNN) model is trained through a large number of classified corrosion damage images to obtain a classifier model. Then the classifier model is used with overlap-scanning sliding window algorithm to recognize and position the location of corrosion damage. Finally, the damage detection pattern for hull structural plate corrosion damage as well as other types of superficial structural damage using convolutional neural network is proposed, which can accelerate the application of artificial intelligence technology into the field of naval architecture & ocean engineering.