Impact of NOx reduction on long-term ozone trends in an urban atmosphere

We investigated the relationships between ambient O(3) concentrations and the concentrations of its precursors, NO(x) and NMHC, in Osaka, Japan. The levels of O(x)' (where [O(x)']=[O(3)]+[NO(2)]-0.1x[NO(x)] where the last term accounts for primary emissions of NO(2)) were uniform within the city even in the photochemically active season. We suggested that NO oxidation by peroxy radicals was a minor contributor, and that oxidation of locally emitted NO by background O(3) in the city was the primary control on NO(2) concentrations. Ozone concentrations increased linearly from 1985 to 2002 at a rate of 0.6 ppbv/yr, even though O(x)' concentrations remained constant after the mid 1990s. The trend for O(x)' concentrations could not be explained in terms of an increase in local O(3) production, and the trend was found to reflect background O(3) concentrations in Japan. There was a clear relationship between the NO(2)/O(x)' ratio and NO(x) concentration: the ratio decreased with decreasing NO(x) concentration. As a consequence, O(3) increased with decreasing NO(x) concentration. The reduction of NO(x) emissions was deemed to be an important factor for the recent trend of increasing O(3) concentrations in Osaka City.     

A constrained neural network model for soil liquefaction assessment with global applicability

A constrained back propagation neural network (C-BPNN) model for standard penetration test based soil liquefaction assessment with global applicability is developed, incorporating existing knowledge for liquefaction triggering mechanism and empirical relationships. For its development and validation, a comprehensive liquefaction data set is compiled, covering more than 600 liquefaction sites from 36 earthquakes in 10 countries over 50 years with 13 complete information entries. The C-BPNN model design procedure for liquefaction assessment is established by considering appropriate constraints, input data selection, and computation and calibration procedures. Existing empirical relationships for overburden correction and fines content adjustment are shown to be able to improve the prediction success rate of the neural network model, and are thus adopted as constraints for the C-BPNN model. The effectiveness of the C-BPNN method is validated using the liquefaction data set and compared with that of several liquefaction assessment methods currently adopted in engineering practice. The C-BPNN liquefaction model is shown to have improved prediction accuracy and high global adaptability.     

基于RBF神经网络方法的混凝土强度预测

在分析普通混凝土强度各影响因素的基础上,选取6个影响因素组成输入层,以混凝土28 d强度作为输出,建立径向基函数网络,经网络训练和仿真结果对比,表明所建网络结构合理、收敛速度快、精度高,可以满足普通混凝土强度预测要求,具有广泛的应用前景。     

造价估测方法的研究和BP神经网络模型的应用

比较分析了现行的造价估测模型的特点及其存在的问题,突出BP神经网络模型进行造价估测的理论优势,引入工程分类思想,以学校类建筑为例,建立了BP神经网络估测模型并进行了造价估测.     

民用建筑室内空气污染及检测方法评价

室内空气质量好坏直接影响着人们的健康,文章深入剖析了造成室内污染的原因。污染物的类型以及室内污染物的特征。根据我国现状,造成我国民用建筑室内空气污染主要是人为污染,并以化学污染为     

High resolution modeling of the effects of alternative fuels use on urban air quality: Introduction of natural gas vehicles in Barcelona and Madrid Greater Areas (Spain)

The mitigation of the effects of on-road traffic emissions on urban air pollution is currently an environmental challenge. Air quality modeling has become a powerful tool to design environment-related strategies. A wide range of options is being proposed; such as the introduction of natural gas vehicles (NGV), biofuels or hydrogen vehicles. The impacts on air quality of introducing specific NGV fleets in Barcelona and Madrid (Spain) are assessed by means of the WRF-ARW/HERMES/CMAQ modeling system with high spatial-temporal resolution (1 km², 1 h). Seven emissions scenarios are defined taking into account the year 2004 vehicle fleet composition of the study areas and groups of vehicles susceptible of change under a realistic perspective. O₃ average concentration rises up to 1.3% in Barcelona and up to 2.5% in Madrid when introducing the emissions scenarios, due to the NO_x reduction in VOC-controlled areas. Nevertheless, NO₂, PM10 and SO₂ average concentrations decrease, up to 6.1%, 1.5% and 6.6% in Barcelona and up to 20.6%, 8.7% and 14.9% in Madrid, respectively. Concerning SO₂ and PM10 reductions the most effective single scenario is the introduction of 50% of NGV instead of the oldest commercial vehicles; it also reduces NO₂ concentrations in Barcelona, however in Madrid lower levels are attained when substituting 10% of the private cars. This work introduces the WRF-ARW/HERMES/CMAQ modeling system as a useful management tool and proves that the air quality improvement plans must be designed considering the local characteristics.     

Estimation of air losses in compressed air tunneling using neural network

This paper explores the capabilities of neural networks to predict the air losses in compressed air tunneling. Field data from the Feldmoching tunnel in Munich were used in this study. In this project, compressed air was used to retain the groundwater and shotcrete was used as temporary support. The final permanent lining was installed in free air. The tunnel passed through variable ground conditions ranging from coarse gravel to sand and clay. Grouting, an additional layer of shotcrete and a layer of mortar were occasionally used to control the air losses. A back-propagation feed forward neural network was trained and used to predict the air losses from the Feldmoching tunnel. The results of the prediction of the air losses from the tunnel using a neural network were compared with the field measurements. Data from different tunnel lengths were used for training. In each case, the trained network was used to predict the air losses during the excavation of the rest of the tunnel. It is shown that, not only can a neural network learn the relationship between appropriate soil and tunnel parameters and air losses, it can also generalize the learning to predict air losses for very different geological and geometric conditions. It is also shown that data from a very short length (50 m in one case) of the tunnel (five data point only, in this case) may contain enough information for the neural network to learn and predict the air losses in the remaining (585 m) length of the tunnel with a good degree of accuracy. This can be of considerable value to tunnel engineers in control of tunneling operations and help them in preparation for possible changes in air losses with tunnel advance, with changes in ground conditions and tunnel geometry and with time.     

强夯神经网络预测模型

针对强夯加固地基中的有效加固深度，传统方法建立的相关模型存在着一些局限性及缺点，而人工神经网络技术（ANN）是一种解决模型精确度和使用方便这两个方面矛盾的新途径，具有较高的实用性。     

大气二氧化碳浓度升高对混凝土碳化的影响

基于Fick第一定律建立的混凝土碳化理论模型在实际预测中广泛应用.分析了该模型的可靠性,并从理论的角度探讨了大气污染引起的大气CO2浓度升高对混凝土碳化深度的影响.分析发现,随着大气中CO2浓度的升高,混凝土碳化速度系数不再是一个恒定的量,而是随时间延长不断增大,表明大气污染会加剧混凝土的碳化.该结果应当引起混凝土长期耐久性设计方面的关注.     

遗传算法BP神经网络在隧洞围岩分类中的应用

针对目前神经网络训练易陷入局部极小点问题，用遗传算法优化神经网络的连接权，并在遗传进化过程中采取保留最优个体的策略，建立了基于遗传算法的BP神经网络的模型，并应用于解决水工隧洞围岩分类这一非线性和不确定性较大的实际问题，证明了这种方法是科学可行的。     

用人工神经网络方法预测分析直接蒸发冷却空调机的性能参数

针对填料表面不规则造成的数学模型无法求解的情况,利用人工神经网络优良的非线性映射能力和GLASdek填料直接蒸发冷却空调机实验测试数据,建立了三层前馈式神经网络来预测直接蒸发冷却空调机在不同工况下处理空气的能力,预测结果与实测结果吻合较好。     

室内装修材料中甲醛污染物的检测与防治方法

室内空气污染问题已成为近年来城市居民关注的热点问题之一。装修材料中挥发性有机污染物进入空气,对人体健康造成很大威胁,本文介绍了室内装修中空气污染物甲醛的来源、特点及检测方法,提出了降解室内甲醛污染物的防治措施。     

BP神经网络在水厂预测混凝投药量中的应用研究

建立了一种基于三层结构BP神经网络的混凝投药量前馈控制模型,采用烧杯试验数据进行了仿真验证,同时建立了传统的线性回归混凝投药量前馈控制模型,并采用两种模型基于同一样本数据进行仿真。从投药预测值一实际值的对比图和均方根误差等可以看出,BP模型优于回归模型,它通过学习可以根据原水水质进行投药量的有效预测,有一定的自适应性,实用性较强,但也存在一定的局限性,对某些水质的投药预测值还存在一定误差。     

岩石本构关系的径向基函数神经网络快速逼近模型

建立了径向基函数神经网络快速逼近模型 ,基于具体条件下岩石力学实验数据 ,对相应岩石的本构关系进行逼近 ,实例分析表明 ,该模型不仅对具体应力 -应变关系能够很好逼近和预测 ,而且逼近速度快、稳定性好 ,对岩石力学快速、高效数值方法的进一步发展具有重要参考价值。     

VAV系统送风管道静压的线性神经元网络补偿控制

利用神经元网络具有自学习以及超强非线性逼近的能力，提出了基于线性神经元网络的补偿控制方法。这种控制方法能够根据送风管道静压耦合因素的变化自适应地调节控制量，实现对管道静压的补偿控制。给出了神经元权系数的在线学习方法，并通过实验验证了控制算法的有效性。     

Prediction of roadheader performance by artificial neural network

Performance prediction of the roadheaders is one of the main subjects in determining the economics of the underground excavation projects. During the last decades, researchers have focused on developing performance prediction models for roadheaders. In the first stage of this study, the performance of a roadheader used in Kucuksu sewage tunnel (Istanbul) was recorded in detail and the instantaneous cutting rate (ICR) of the machine was determined. The uniaxial compressive strength (UCS) and rock quality designation (RQD) are used as input parameters in previously developed empirical models in order to point out the efficiency of these models, and the relationships between measured and predicted ICR for different encountered formations. In the second stage of the study, Artificial Neural Network (ANN) technique is used for predicting of the ICR of the roadheader. A data set including UCS, RQD, and measured ICR are established. It is traced that a neural network with two inputs (RQD and UCS) and one hidden layer can be sufficient for the estimation of ICR. In addition, it is determined that increase in number of neurons in hidden layer has positive optimizing on the performance of the ANN and a hidden layer larger than 10 neurons does not have a significant effect on optimizing the performance of the neural network. Furthermore, probability of memorizing is being recognized in this situation. Based on this study, it is concluded that the prediction capacity of ANN is better than the empirical models developed previously.     

智能桥梁结构的智能计算方案及其初步实现

提出了一种智能桥梁的智能计算方案，并利用人工神经网络法，建立一种识别作用在桥梁结构上荷载的力学反分析法，以此初步实现该方案，该方法利用传感器检测信息进行荷载识别，从而为智能桥梁结构的智能化计算奠定基础，算例表明，该方法有较好的应用前景。     

基于BP神经网络的隧道岩爆预测模型研究

在分析岩爆主要影响因素的基础上,建立了基于BP神经网络岩爆预测模型,采用已有岩爆发生数据作为训练样本对网络进行训练,利用收敛的网络进行岩爆烈度预测,预测结果与实际吻合,说明利用人工神经网络预测岩爆发生烈度是一种可行的方法。     

基于遗传优化神经网络的市政管网水质模型研究

将遗传算法的全局搜索能力和BP神经网络的局部学习能力有机结合,得到一种快速高效的建立供水管网余氯的水质模型的新方法。验证结果表明,遗传算法优化后的神经网络模型所需要考虑的参数较少,应用方便,预测精度和效率较高,在城市给水系统水质模拟预测研究中有一定的参考应用价值。     

神经网络模型在边坡坍塌预测中的应用

尝试建立了在区域降水条件下,选取蒸发量和降雨量作为评价参数,预测边坡坍塌强度的神经网络模型,实例的计算和验证表明,用人工神经网络的方法来预测在降水条件下边坡的坍塌强度是可行的。