## 1 Introduction
[8] concluded that compared with a linear regression curve, a quadratic regression curve provides a better visual fit to the investigated data sets. They suggested using the quadratic one-half standard deviation curves as shown in Equation (2) for assessing ground vibrations from pile driving with impact hammers and vibrodrivers:  
\[\log\text{PPV}=p+m\log(E^{0.5}/D)+n\log^{2}(E^{0.5}/D) \tag{2}\]  
where PPV (mm/s) also refers to the peak resultant particle velocity, and \(p\), \(m\), and \(n\) are constants of proportionality that are determined empirically. [PERSON] [3] suggested substituting the empirical vibration intensity factor (\(k\), \(K\), and \(p\)) in Equations (1) and (2) with the vibration intensity in a reference case, and proposed the following formula for predicting pile driver-generated vibrations:  
PPV_Vilbro_ =PPV_ref_ (D_ref)^-n  
PPV_Impact_ =PPV_ref_ (D_ref)^-n  
PPV_Vilbro_ (mm/s) and PPV_Impact_ (mm/s) represent the peak vertical velocity component, which is different from the definition of PPV in Equations (1) and (2) using the peak resultant particle velocity, produced by one vibratory pile driver and one impact pile driver, respectively; \(D\) (m) refers to the distance from a pile driver to a receiver; \(E_{\textit{enipip}}\) (J) represents the pile driver's rated energy; and \(\text{PPV}_{\textit{ref}}\) = 16.5 mm/s represents the reference peak vertical particle velocity at a distance of \(D_{\textit{ref}}\) = 7.6 m by a pile driver with rated energy of \(E_{\textit{ref}}\) = 48,806 J. The suggested value for the exponent \(n\) is 1.1, but it can vary from 1.0 to 1.4 to reflect different soil types. Construction-induced vibrations must be inspected and controlled by following the criteria of safe vibration [17; 18]. Many construction projects implement the practice of real-time construction vibration monitoring in a compulsive manner. After reviewing the worldwide standards and practices of noise and vibration monitoring on different construction sites, [PERSON] [19] concluded that the design of monitoring systems is affected by many factors such as criteria, assessment methodology, noise and vibration index, and means of display. To decide appropriate ground vibration monitoring distances in the process of pile driving, [PERSON] et al. [20] designed a statistical procedure to prevent possible construction-related damage. [PERSON] [21] developed a remote vibration monitoring system by using wireless internet data transmission to collect, process, transmit, and analyze vibration measurement data obtained on construction sites. [PERSON] [22] designed a wireless noise and vibration management system to meet the standards set by the local regulations. [PERSON] and [PERSON] [23] built a wireless Internet of Things sensing system to conduct the real-time monitoring and assessment of construction-induced vibrations. However, several limitations in the current construction-induced vibration impact assessment practice are worth further attention. First, by assuming construction scenarios (e.g., construction locations and methods) prior to real construction work, the aforementioned empirical models are typically applied to predict vibration intensities in a large area. Such prediction measures lead to great limitations in construction operations and also are often inaccurate given the great variability in construction-related vibrations. Second, the real-time global vibration impact assessment based on the empirical models is often prevented by moving vibration sources with their exact locations unknown in construction processes. Considering its unavoidable delay and inefficiency, the manual input of locations by construction workers is undesirable in an automatic process of vibration monitoring and assessment. Third, real-time vibration monitoring on construction sites can only guarantee a local vibration impact assessment because the selected monitoring points in reality usually cannot cover the whole surrounding area due to budget limitations.