Abstract Reasonable and effective ventilation plays an important role in the safety of tunnel construction. Fans are usually designed to be are capable of satisfying the maximum demand of a tunnel. During the tunnel construction, the actual usage of the tunnel could be considerably less than the designed fan capacity. This leads to high energy consumption and low efficiency. Therefore, a system that can analyze in real-time the tunnel environment and calculate the actual demand is required for tunnel construction. In this study, a tunnel ventilation intelligent frequency control (TVIC) system is designed based on the radial basis function neural network (RBF NN). As a type of feedforward neural network, RBF NN is used to obtain the relationship between the fan operating frequency and various pollutant concentrations, the tunnel length, and the temperature. TVIC is composed of a safety-monitoring system, control system, communication system, and variable-frequency drive (VFD) fan. It can self-adjust the frequency of the fan according to the construction environment inside the tunnel, and has been used in the Huayingshan tunnel in southwest China for a year and a half. In addition, it displays good reliability and a satisfactory capacity for tunnel environmental improvement and energy conservation. Compared with the current manual control method, ventilation system was observed to reduce electricity consumption by 42% after using TVIC.

中文翻译：

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基于径向基函数神经网络的隧道施工通风频率控制

摘要 合理有效的通风对隧道施工安全具有重要作用。风机通常设计为能够满足隧道的最大需求。在隧道施工期间，隧道的实际使用量可能会大大低于设计风机容量。这导致高能耗和低效率。因此，隧道施工需要一个能够实时分析隧道环境并计算出实际需求的系统。本研究基于径向基函数神经网络（RBF NN）设计了隧道通风智能频率控制（TVIC）系统。作为一种前馈神经网络，RBF NN用于获得风机运行频率与各种污染物浓度、隧道长度、和温度。TVIC由安全监控系统、控制系统、通讯系统和变频驱动（VFD）风扇组成。可根据隧道内施工环境自行调节风机频率，已在西南华蓥山隧道使用一年半。此外，它还表现出良好的可靠性和令人满意的隧道环境改善和节能能力。与目前的手动控制方法相比，使用 TVIC 后通风系统可减少 42% 的电力消耗。并已在西南华蓥山隧道使用一年半。此外，它还表现出良好的可靠性和令人满意的隧道环境改善和节能能力。与目前的手动控制方法相比，使用 TVIC 后通风系统可减少 42% 的电力消耗。并已在西南华蓥山隧道使用一年半。此外，它还表现出良好的可靠性和令人满意的隧道环境改善和节能能力。与目前的手动控制方法相比，使用 TVIC 后通风系统可减少 42% 的电力消耗。