基于BP神经网络的施工扬尘量化建模研究
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摘要
施工扬尘是大气颗粒污染物的重要来源,量化扬尘排放量是评价施工扬尘控制措施效果的重要手段。通过监测建筑工地边界附近同一平面坐标3.9-18.9m范围内不同天气条件下、不同高度处的降尘浓度变化,总结出施工扬尘产生的主要影响因素。影响施工扬尘的主要因素为：粉尘含水量、空气温度、空气湿度和施工强度。研究结果表明,建筑施工扬尘污染在地基开挖、地基建设和地基回填阶段最为严重,主体工程施工阶段次之,地面装饰施工阶段产生的扬尘污染可忽略不计,建筑设备安装阶段几乎没有扬尘污染产生。
利用BP神经网络,对施工扬尘过程进行模拟,得出一定约束条件下的扬尘量化模型。BP神经网络经训练、优化后,采取140个隐含层神经元、最大训练周期为1000周,隐含层激励函数选择Levenberg-Marquardt最优化算法,最终确定一个形为4-140-1的三层网络结构的模型。其输入输出数据的相关度达到0.93786,网络经训练后最终输出性能为5.94×10-28,效果较为理想。结果表明,所建模型能够较好地预测施工扬尘在不同天气条件下的浓度变化趋势,可为工程施工场地扬尘运移、扩散及污染控制提供科学依据,该方法具有较广阔的开发应用前景。
Construction fugitive dust is the main source of atmospheric particulate pollutants, so it deserves to research and control it. Quantization dust emissions are an important means to assess effect of fugitive dust control measures. By monitoring the construction site near the boundary coordinates of the same plane within 3.9～18.9m under different weather conditions, different height of the dust concentration, summed up the construction dust generated by the main factors. The main factors affecting the construction fugitive dust are: dust moisture, air temperature, air humidity and construction strength. The results show that construction dust pollution in the foundation excavation, foundation construction, and backfilling the foundation stage are the most serious, followed by the main construction phase, the dust pollution produced by construction phase surface decoration is negligible, construction equipment Installation phase produces almost no dust pollution.
Using BP neural network to simulate the process of construction fugitive dust, and get quantitative model under certain constraints. After BP neural network being trained, optimized, take 140 hidden layer neurons and the maximum training period of 1000 weeks; select the Levenberg-Marquardt optimization algorithm as hidden layer activation function to determine a three layer shape network model for 4-140-1 finally. The degree of input and output data related to 0.93786, the network performance of the final output after training is 5.94 x 10'28, results are satisfactory. The results show that the model can predict the dust pollution concentration trend in different weather conditions well, and provide the scientific basis for construction site dust migration, proliferation and pollution control. The method has wider application prospect.
利用BP神经网络,对施工扬尘过程进行模拟,得出一定约束条件下的扬尘量化模型。BP神经网络经训练、优化后,采取140个隐含层神经元、最大训练周期为1000周,隐含层激励函数选择Levenberg-Marquardt最优化算法,最终确定一个形为4-140-1的三层网络结构的模型。其输入输出数据的相关度达到0.93786,网络经训练后最终输出性能为5.94×10-28,效果较为理想。结果表明,所建模型能够较好地预测施工扬尘在不同天气条件下的浓度变化趋势,可为工程施工场地扬尘运移、扩散及污染控制提供科学依据,该方法具有较广阔的开发应用前景。
Construction fugitive dust is the main source of atmospheric particulate pollutants, so it deserves to research and control it. Quantization dust emissions are an important means to assess effect of fugitive dust control measures. By monitoring the construction site near the boundary coordinates of the same plane within 3.9～18.9m under different weather conditions, different height of the dust concentration, summed up the construction dust generated by the main factors. The main factors affecting the construction fugitive dust are: dust moisture, air temperature, air humidity and construction strength. The results show that construction dust pollution in the foundation excavation, foundation construction, and backfilling the foundation stage are the most serious, followed by the main construction phase, the dust pollution produced by construction phase surface decoration is negligible, construction equipment Installation phase produces almost no dust pollution.
Using BP neural network to simulate the process of construction fugitive dust, and get quantitative model under certain constraints. After BP neural network being trained, optimized, take 140 hidden layer neurons and the maximum training period of 1000 weeks; select the Levenberg-Marquardt optimization algorithm as hidden layer activation function to determine a three layer shape network model for 4-140-1 finally. The degree of input and output data related to 0.93786, the network performance of the final output after training is 5.94 x 10'28, results are satisfactory. The results show that the model can predict the dust pollution concentration trend in different weather conditions well, and provide the scientific basis for construction site dust migration, proliferation and pollution control. The method has wider application prospect.
引文
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