## 5 Conclusions  
The standard deviation and distribution functions of different sensors are needed to develop structural system identification methods further. Many low-cost sensors have such low resolutions that their use has been exempt in civil engineering applications with low budgets. As a result, justifying the use of low-cost sensors in civil engineering requires improvements in both deep noise canceling and accuracy. This paper's main contribution and novelty are to combine the results of similar sensors for mitigating the adverse effects of environmental or individual inherent noises of low-cost sensors for estimating static environmental variations. The study finds that by coupling a number of similar sensors for measuring the same structural response or variation, the overall accuracy is higher than the estimation of an individual sensor. This paper also contributes to the civil engineering literature by providing the standard deviation and distribution functions of these types of sensors for different environmental circumstances. It is noted that the developed noncontact distance measurement device of this paper is intended to be used for water level control of dams, bridges, and open canal applications. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Sensor Name** & **Sensor Type** & \begin{tabular}{c} **Number of** \\ **Needed Sensors** \\ \end{tabular} & \
\begin{tabular}{c} **Error** \\ **(\%)** \\ \end{tabular} &
\begin{tabular}{c} **Price** \\ **(EUR)** \\ \end{tabular} \\ \hline HC-SR04 & Ultrasonic & 2 & 1.85\% & 5.0 \\ VI53l0x & ToF & 8 & 8.55\% & 43.2 \\ VI53 lx & ToF & 2 & 3.70\% & 25.0 \\ HC-SR04 & Ultrasonic & 1 & 2.40\% & 2.5 \\ VI53l0x & ToF & 1 & 10.17\% & 5.4 \\ VI53l1x & ToF & 1 & 4.10\% & 12.5 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Price comparison of the devices made from coupled sensors. To investigate the proposed methodology, this paper uses 75 various distance sensors mounted on a stiff foundation with 3D-printed bases. These sensors (25 HC-SR04, 25 VL53L0X, and 25 VL53L1X) were connected to two microcontrollers. The microcontrollers were connected through transmission and receiving ports (RX and TX ports) of the microcontroller serial communication gates. Data acquisition was carried out using a Raspberry Pi connected to one of the microcontrollers. The acquired data were then uploaded to the cloud. The analysis of the acquired data shows that sensor combination is beneficial for both analog and digital sensors, regardless of the ambient light situation. The combination of 25 similar sensors showed a considerable improvement in estimation accuracy and data fluctuation for all sensor types. It is also shown that even the worst combination of sensors has a lower error than the estimation of the worst selected sensor. Further analysis of the data shows that HC-SR04 is a cheap analog distance sensor and has higher estimation accuracy than the expensive time-of-flight (ToF) sensors (VL53L0X and VL53L1X) in laboratory experiments carried out in a civil engineering laboratory with and without excessive ambient light. Moreover, the lower price of HC-SR04 compared with the other ToF sensors in this study can justify the sensor combination methodology for this type of sensor. The significant endowments and limitations of the proposed measuring distance device through combining estimations of several similar sensors can be summarized as follows. Combining the results of multiple similar ranging sensors improves the estimation accuracy by averaging the responses of uncalibrated sensors. By comparing the measurement of individual sensors with the combined results of a few sensors, the sensitivity and accuracy of individual sensors can be monitored for the duration of the data acquisition process. In the case of a single malfunctioning sensor, the acquisition process can continue due to several combined sensors instead of using the outputs of a single measuring tool.