预测压气法隧道施工中的漏气量的模型研究

在阐述压气法隧道施工原理的基础上,建立了水–气二相流模型来模拟压气法隧道施工过程中超压空气驱替隧道周围土体的孔隙水的水–气二相流过程。该模型同时考虑了土体中气相和水相的流动以及水相和气相相互作用的影响,比较符合水–气二相渗流过程的物理意义。通过工程算例,计算出不同施工进度下开挖面和隧道混凝土衬砌上的空气损失速率以及空气入流对隧道周围土体渗流场的影响,结果表明:在均质的地质条件下,开挖面上的漏气速率一般为常数,而衬砌上的漏气速率将随着隧道长度的增加而线性增大。最后,提出了利用水–气二相流模型进一步提高压气法隧道施工的漏气量的数值模拟精度,需要深入研究的两点意见。     

Investigations on air pressure and air losses in compressed air tunneling through saturated clayey soil

Determination of air pressure and assessment of air losses in clayey soil are of great importance to implementation of compressed air tunneling. In the present work, a series of air flow tests were performed to provide a more reasonable method based on flow characteristics of snap-off pressure and the dissolution/diffusion. Results showed that, the nonlinear air flow behavior and gas breakthrough were presented with the increase in air pressure. After that, the excessive pressure decreased continuously to reach an equilibrium termed as the snap-off. For the tested clayey soil, snap-off pressures around 250 kPa could be adopted as the air pressure, which was significantly lower than the gas breakthrough pressures. Diffusion coefficient of 1.5 × 10^-11 m²/s could be determined in the followed dissolution/diffusion stage, which bring 3 orders of decreasing magnitude in air losses compared to the capillary flow occurred after gas breakthrough. As a conclusion, the adoption of snap-off pressure in compressed air tunneling could effectively prevent the continuous air/water flow in clayey soil and create a more human-friendly environment. Additionally, less air losses could be presented compared to that using gas breakthrough pressure, indicating tremendous energy savings in field implementation.     

Geological compressed air energy storage as an enabling technology for renewable energy in Ontario,Canada

As renewable resources are increasingly used to provide power to the world’s demand centres, dealing with the intermittent nature of these resources and their affect on the power grid is becoming a significant issue. Compressed air energy storage (CAES) is one technology that is proposed to increase flexibility when integrating renewable energy sources such as wind, solar and tidal generation with the power grid. By creating a storage medium where the energy produced from these sources can be stored and dispatched to the grid as required, a higher penetration of renewable energy generation can be achieved.     

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。     

基于循环神经网络的盾构隧道#br# 引发地面最大沉降预测

伴随着计算机技术的快速发展,机器学习等新兴算法正在被越来越多地运用于预测隧道掘进引发的地面最大沉降。在隧道施工过程中,由盾构机和地面监测点位采集的数据具有很强的序列化特征,而传统的机器学习算法对序列数据的处理存在一定的局限性。循环神经网络(RNN)具有极强的对时序型数据的处理能力,在视频识别、语音翻译等领域有着广泛的应用。采用两种RNN模型(LSTM、GRU)和传统的BP神经网络模型,以地质参数、几何参数和盾构机参数作为输入,对隧道施工过程中引发的地面最大沉降进行预测分析。结果显示,RNN对隧道沉降的预测结果优于传统的BP神经网络模型,并且RNN在连续未知区段的预测结果比BPNN更加稳定。     

基于BP神经网络的水下岩溶地层#br# 盾构掘进参数预测与分析

随着我国城市地铁网的建设,越来越多的隧道将不可避免的穿越水下岩溶区,受制于岩溶地层的复杂性、注浆加固后地层的诸多不确定性,盾构穿越该类地层施工风险极大,而选取合理的盾构掘进参数是确保盾构安全与高效掘进的关键。以长沙地铁三号线盾构穿越水下岩溶段为工程依托,首先通过统计与分析钻探数据,明确了岩溶分布特征;其次,通过输入地层特征参数和隧道特征参数,建立了可输出盾构掘进速度、推力、刀盘扭矩、开挖仓压力、气垫仓压力和同步注浆量等掘进参数的BP神经网络水下岩溶盾构掘进参数预测模型;最后,对样本数据进行了训练,并成功应用于工程实践。研究结果表明：训练的输出值与期望值吻合度较高,构建的BP神经网络模型具有较好的适应性;输出的预测结果能有效反映实际盾构掘进参数的变化趋势,预测值与实际期望值的平均误差均低于13%,在误差可接受范围内。现场应用结果表明,地表沉降在安全范围内,盾构掘进过程中未发生工程事故,盾构掘进参数选取合理,姿态控制较好。研究成果可用于指导水下岩溶盾构隧道工程施工,且该方法的提出也为其他复杂地层盾构掘进参数合理选取提供了新思路。     

Evaluating subcontractor performance using evolutionary fuzzy hybrid neural network

This paper developed an evolutionary fuzzy hybrid neural network (EFHNN) to enhance the effectiveness of assessing subcontractor performance in the construction industry. The developed EFHNN combines neural networks (NN) and high order neural networks (HONN) into a hybrid neural network (HNN), which acts as the major inference engine and operates with alternating linear and non-linear NN layer connections. Fuzzy logic is employed to sandwich the HNN between a fuzzification and defuzzification layer. The authors developed and applied the EFHNN to assess subcontractors performance by fusing HNN, FL and GA. Enhancing subcontractor performance assessments are crucial in terms of providing to general contractors information on historical contractor performance essential to guiding a selection of appropriate subcontractors for a specific current or future subcontracting need. Results show that the proposed EFHNN may be deployed effectively to achieve optimal mapping of input factors and subcontractor performance output. Moreover, the performance of linear and non-linear (high order) neuron layer connectors in the EFHNN was significantly better than performances achieved by previous models that used singular linear NN.     

基于人工神经网络的变风量空调控制系统

研究了变风量空调系统神经网络预测优化控制方法,优化指标考虑了舒适性和耗能量,舒适性指标取PMV指标,耗能量包括风机和冷水泵能耗。系统的控制量为送风风速和冷水流量,被控参数为空调区域的温湿度,采用预测滚动优化控制算法训练多层前向神经网络,然后将其作为优化反馈控制器来求解变风量暖通空调系统的优化解,并在运行中实时预测空调区域的负荷。仿真结果表明,采用此方法,在模型环境、负荷参数变化的情况下,既可以达到节能的要求,又可以使空调区域的温湿度保持在舒适范围内。     

Automobile gross emitter screening with remote sensing data using objective-oriented neural network

One of the costs of Taiwan's massive economic development has been severe air pollution problems in many parts of the island. Since vehicle emissions are the major source of air pollution in most of Taiwan's urban areas, Taiwan's government has implemented policies to rectify the degrading air quality, especially in areas with high population density. To reduce vehicle pollution emissions an on-road remote sensing and monitoring system is used to check the exhaust emissions from gasoline engine automobiles. By identifying individual vehicles with excessive emissions for follow-up inspection and testing, air quality in the urban environment is expected to improve greatly. Because remote sensing is capable of measuring a large number of moving vehicles in a short period, it has been considered as an assessment technique in place of the stationary emission-sampling techniques. However, inherent measurement uncertainty of remote sensing instrumentation, compounded by the indeterminacy of monitoring site selection, plus the vagaries of weather, causes large errors in pollution discrimination and limits the application of the remote sensing. Many governments are still waiting for a novel data analysis methodology to clamp down on heavily emitting vehicles by using remote sensing data. This paper proposes an artificial neural network (ANN), with vehicle attributes embedded, that can be trained by genetic algorithm (GA) based on different strategies to predict vehicle emission violation. Results show that the accuracy of predicting emission violation is as high as 92%. False determinations tend to occur for vehicles aged 7-13 years, peaking at 10 years of age.     

固定式压缩空气泡沫灭火系统设计仿真研究

运用AMESim仿真软件建立仿真模型,验证了压缩空气泡沫固定式系统应用的可行性,并对影响固定式压缩空气泡沫灭火系统工程化设计的主导因素进行了分析研究.分析结果表明:模拟结果与试验结论具有一定的相似性;系统工程化设计中可忽略泡沫原液粘度的影响;泡沫混合比、气液比、管网管径等相关因素对系统工作性能的影响呈现一定的规律性,整个系统管网压力损失随泡沫混合比提高、气液比提高、管网管径变小而增大;模拟方式对系统工程化设计具有指导意义.     

基于改进PSO-BP神经网络的冰蓄冷空调冷负荷动态预测模型

当前多数冰蓄冷空调冷负荷动态预测方法中,由于模型输入变量与输出结果相关性差、信息冗余度高等原因,导致多数预测模型在预测精度和收敛速度方面都未达到理想的预测效果,因此,提出一种改进的PSO-BP神经网络算法预测大型公共建筑的冷负荷。对于输入变量与输出结果采用灰色关联度分析,消除样本输入变量对数的耦合性,确定影响冰蓄冷空调系统冷负荷的关键性因素,将其作为输入变量,预测冰蓄冷空调系统动态冷负荷。结果表明：T时刻室外空气温度、T-1 h时刻室外空气温度、T时刻室外空气湿度、T时刻太阳辐射强度、T-1 h时刻太阳辐射强度、T-1 h时刻空调冷负荷是影响T时刻冰蓄冷空调系统冷负荷的关键因素,并以此作为预测模型的输入变量。相对于传统PSO-BP神经网络全输入变量预测算法,该模型预测结果精确度更高、收敛速度更快。     

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。     

常用洗涤剂用于压缩空气泡沫系统可行性探讨

使用背负式空气压缩泡沫灭火系统,对无磷洗衣粉、家用洗洁精、十二烷基苯磺酸钠与K12、十二烷基苯磺酸与氢氧化钠等4种常用洗涤剂溶液进行发泡和灭火试验,试验表明溶液具有一定的灭火效果,在A类压缩空气泡沫灭火剂不足时可以临时充当应急替代品或仅作为训练使用。     

基于BP神经网络的人防工程结构损伤诊断研究

为了有效地对地下人防工程结构进行损伤诊断,提出了以结构前三阶振型变化率绝对值之和作为损伤诊断标识量,结合BP神经网络技术对某浅埋地下人防工程结构进行损伤诊断。实例计算分析表明:通过计算给定人防工程结构的损伤诊断标识量,经过BP神经网络的学习、联想、记忆和分类,能较准确地识别浅埋地下人防工程结构的损伤位置和损伤程度,识别精度较高。     

Estimation of rock modulus: For intact rocks with an artificial neural network and for rock masses with a new empirical equation

The elastic modulus of intact rock is used for many rock engineering projects, such as tunnels, slopes, and foundations, but due to the requirements of high-quality core samples and associated sophisticated test equipment, instead the use of empirical models to obtain this parameter has been an attractive research topic. In the rock mechanics literature, some empirical relations exist between the elastic modulus of intact rock and other rock properties, such as the uniaxial compressive strength (σ_ci), unit weight (γ), Schmidt hammer rebound number, point load index and petrographic composition. However, the past use of specific rock types is the main limitation of the existing empirical equations. In other words, they are not open to the general purpose use. To eliminate this deficiency, a total of 529 datasets, including uniaxial compressive strength, unit weight and elastic modulus of intact rock (E_i), were collected via an extensive literature review. In addition to these datasets, a further total of 80 datasets was obtained from laboratory tests performed on greywacke and agglomerate core samples for this study. To prepare a chart for the prediction of the elastic modulus of intact rock, an artificial neural network was constructed using the large database. In addition, after a brief overview of existing empirical equations, a new empirical equation, which considers RMR and the elastic modulus of intact rock (E_i) as input parameters, is also proposed using worldwide data.     

Estimation of bias with the single‐zone assumption in measurement of residential air exchange using the perfluorocarbon tracer gas method

Residential air exchange rates (AERs) are vital in understanding the temporal and spatial drivers of indoor air quality (IAQ). Several methods to quantify AERs have been used in IAQ research, often with the assumption that the home is a single, well‐mixed air zone. Since 2005, Health Canada has conducted IAQ studies across Canada in which AERs were measured using the perfluorocarbon tracer (PFT) gas method. Emitters and detectors of a single PFT gas were placed on the main floor to estimate a single‐zone AER (AER_1z). In three of these studies, a second set of emitters and detectors were deployed in the basement or second floor in approximately 10% of homes for a two‐zone AER estimate (AER_2z). In total, 287 daily pairs of AER_2z and AER_1z estimates were made from 35 homes across three cities. In 87% of the cases, AER_2z was higher than AER_1z. Overall, the AER_1z estimates underestimated AER_2z by approximately 16% (IQR: 5–32%). This underestimate occurred in all cities and seasons and varied in magnitude seasonally, between homes, and daily, indicating that when measuring residential air exchange using a single PFT gas, the assumption of a single well‐mixed air zone very likely results in an under prediction of the AER.     

Determination of three dimensional hydraulic conductivities using a combined analytical/neural network model

Analysis of groundwater flow through fractured rock masses is an essential step in many engineering and environmental problems, such as in safety assessment of radioactive waste storages, hydrocarbon storage caverns and hydropower projects. The most important hydrological parameter in groundwater flow analysis is the hydraulic conductivity which is anisotropic and heterogeneous in the fractured rock masses. To analyze the groundwater flow correctly, some site investigations through boreholes must be carried out. One of the challenges in seepage analysis for an engineering project is how to determine the anisotropic and heterogeneous hydraulic conductivities of the fractured rock masses using the limited in situ investigation data. In this study, a new practical approach for the determination of three dimensional hydraulic conductivities of fractured rock masses is presented. Starting from rock fracture properties surveyed in six boreholes, the anisotropic hydraulic conductivities are estimated using the in situ injection test results and Oda’s theoretical model. A neural network method is then utilized to generate the three dimensional heterogeneous hydraulic conductivities based on the anisotropic hydraulic conductivities along the six boreholes. In order to evaluate the reliability of this approach, a 3D numerical seepage model using code FLAC3D is performed for a real project. The inflow values in a shaft obtained with the 3D numerical analysis are compared with the in situ measured flow. The result indicates that the derived hydraulic conductivity is acceptable.     

网络图优化在空调工程施工中的应用

从工期-成本优化着手,介绍了网络图优化的方法。结合一空调安装工程实例说明了网络图优化在空调工程施工中的具体应用。研究结果表明网络图优化方法能简便、有效地提高空调安装工程的效率。     

Identifying the cutting tool type used in excavations using neural networks

The paper presents results of preliminary research on utilising neural networks to identify excavating cutting tool’s type used in multi-tool excavating heads of mechanical coal miners. Such research is necessary to identify rock excavating process with a given head, and construct adaptation systems for control of excavating process with such a head.