modeling traffic-induced, in-dwelling vibration using ...

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MODELING TRAFFIC-INDUCED, IN-DWELLING VIBRATION USING URBAN DESIGN AND PLANNING VARIABLES Irem Ayhan Selcuk K. Mert Cubukcu

Traffic-induced vibration is a serious urban problem and environmental disturbance that has long been tolerated. However, it has become a common source of public complaint. Although this annoyance is controllable through design and planning decisions, the level of traffic-induced, in-dwelling vibration has received little attention in the urban design and planning literature. This study expands on past research on traffic-induced vibration by modeling the ground-borne, in-dwelling vibration levels in a historic town center using variables that can be controlled through urban design and planning decisions. The aim is to construct a simple model that can be used by designers and decision makers to control in-dwelling vibration levels. In-dwelling vertical vibration velocities induced by different types of vehicles were recorded at 10 different historic masonry houses in Birgi, Turkey. The results showed that the simple linear regression model explained a large portion of the variance in peak traffic-induced, ground-borne, indwelling vibration levels (R2 = .867). This simple model can be used by practitioners in the fields of urban design and planning to generate alternative solutions to keep traffic-induced vibration at acceptable levels prior to the architectural design and construction stages.

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INTRODUCTION Undoubtedly, one of the major goals of both urban design and planning is livability. Through the practice of planning and design, cities are ideally expected to be free from any kind of danger, nuisance, and pollution (Steinø, 2004). Nevertheless, the vibration of environments is an environmental pollution problem (Xia, et al., 2005), and traffic-induced vibration is a serious environmental disturbance (Watts, 1990) that has long been tolerated (Xia, et al., 2005). However, it is now considered a significant nuisance and has become a common source of public complaint (ibid.). In a study from the United Kingdom, Watts (1990) reported that the percentage of dwellers surveyed who were greatly bothered by traffic-induced vibration was as high as the percentage of dwellers who were bothered by traffic noise. Vehicles create two types of vibration in buildings. The first is airborne vibration, which is caused by low-frequency sound waves produced by vehicle engines and exhaust, which are then transmitted through the air (Hendriks, 2002; Watts, 1990). High levels of airborne vibration generally occur on windows and doors that front the street and rarely become perceptible on the floor. The second type is ground-borne vibration (Watts, 1990). Traffic-induced, ground-borne vibration is generated through the contact of vehicle tires with irregularities in the road, such as rough pavement surfaces, cracks, and road humps. The stress waves created by these contacts travel through the soil and are transmitted to the surrounding built structures, including buildings and walls, causing in-dwelling vibration (Hunaidi, 2000; Lombaert, et al., 2000; Pau and Vestroni, 2008). Rapid urbanization and increasing density have led to more buildings being constructed closer to roads, which has made traffic-induced vibration an increasingly common problem in cities (Hao, et al., 2001; Xu and Hong, 2008). An increase in traffic flow and axle loads has also added to the magnitude of the problem (Bata, 1971). The negative effects of traffic-induced vibration on living and work environments in urban areas have thus become design and policy issues for local and national authorities (Xia, et al., 2005). The American National Standards Institute and the International Organization for Standardization (ISO) adopted criteria for evaluating vibration in buildings in 1983 and 1989 respectively (Hanson, et al., 2006), followed by the Swiss Association for Standardization (SNV) and the British Standards Institution in 1992 (DEFRA, 2007; SNV, 1992). The ISO criteria were revised in 2003 (Hanson, et al., 2006). Vibration is covered as an environmental planning and management subject in the American Planning Association’s (2006) Planning and Urban Design Standards. The Federal Transit Administration in the United States Department of Transportation requires ground-borne vibration impact assessment for transit projects including busses (ibid.). To follow these myriad regulations on controlling the level of traffic-induced vibration, a number of different mitigation methods have been developed and implemented worldwide at varying costs (Nelson, 1996). Traffic-induced, in-dwelling vibration is a major concern for dwellers for three main reasons. First, an in-dwelling vibration level slightly above the perception threshold is sufficient to become an annoyance to dwellers and lead to complaints (Hanson, et al., 2006; Hunaidi, 2000). Second, indwelling vibration causes anxiety regarding possible building damage (Watts and Krylov, 2000). Third, it may cause disturbances in the functioning of sensitive equipment (Hao, et al., 2001). Traffic-induced vibration is an even greater concern in historic parts of cities. Crispino and D’Apuzzo (2001) summarized a number of reasons for that concern. First, the roads in historic sections of cities are often paved in stone, which is usually very rough and poorly maintained. Second, the tendency to limit private cars in these areas leads to an increase in the number of highcapacity and heavyweight public transport vehicles in these areas. Finally, the presence of buildings under preservation or legal protection creates additional concerns regarding vibratory motion (ibid.). Older buildings also have higher inherent damping and, thus, higher vibration frequencies because they are usually built with stone or timber (Hao, et al., 2001).

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According to Hajek, et al. (2006), there are three main factors that affect the level of traffic-induced, ground-borne, in-dwelling vibration: (1) source, (2) transmission medium, and (3) receiver. Source includes the parameters pertaining to the vehicle and surface conditions; transmission medium includes the distance between the source and the receiver, soil characteristics, and ground topology; and receiver includes the parameters pertaining to the building and the location of the receiver (ibid.). Although almost all of these factors can be directly or indirectly controlled through design and planning decisions, the level of traffic-induced, in-dwelling vibration has received little attention in the urban design and planning literature. This study expands on past research on traffic-induced vibration by modeling the ground-borne, in-dwelling vibration levels in a historic town center using variables that can be controlled through urban design and planning decisions. The aim is to construct a simple model that can be used by designers and decision makers to control in-dwelling vibration levels prior to the architectural design and construction stages. This approach will provide new insights into the fields of urban design and planning through a greater awareness of traffic-induced vibration. The remainder of the paper is organized as follows: the next section consists of a literature review, the third section presents information on the data collection and processing used in this study, the fourth section describes the analysis and results, and the final section concludes the discussion.

LITERATURE REVIEW Vibratory motion at a point is defined by three rotational components, or variables, and three directional components. Any one of these components or variables can be characterized by maximum displacement (amplitude), the instantaneous speed of the movement (velocity), or the rate at which the speed changes (acceleration) (Rudder, 1978). In studies on traffic-induced vibration, it is important to note the direction in which vibrations are measured or analyzed (Hendriks, 2002). According to Rudder (1978), using the vertical component as the predominant component of the vibratory motion is sufficient for assessing the magnitude of traffic-induced, ground-borne vibration. Hendriks (2002) suggested that measuring vibrations in the vertical direction is generally preferred because vibrations along the ground surface are usually greatest in the vertical direction. Moreover, Li, et al. (2009) reported that traffic-induced vibrations are predominantly in the vertical direction, and vibrations in the horizontal direction are negligible. Vertical vibrations are typically measured in terms of velocity (Rudder, 1978), and the vibration amplitude (maximum displacement) is often expressed either as the “peak” or the root mean square. There is a constant relation between these two measures when the motion is sinusoidal (varying according to a sine curve). The root mean square value is derived by dividing the peak value by the square root of two (Griffin, 1990; Hendriks, 2002). Vertical peak particle velocity (PPV), expressed in millimeters per second (mm/s), has become the standard measure in studies dealing with trafficinduced, ground-borne vibration levels (Morbia, et al., 2013) because it is related to the magnitude of stress to which buildings are exposed (Hanson, et al., 2006). Although traffic-induced, in-dwelling vibration has been a topic of interest since the early 1960s (Bata, 1971), a universal threshold level at which ground-borne vibration can cause structural damage is not available. This is likely because the threshold level varies for each building, depending on the building’s inherent structural strength, material properties, and dynamic characteristics (Hao, et al., 2001). The lowest PPV threshold level for architectural damage suggested in the literature is 1 mm/s (Garg and Sharma, 2010). A general review of the literature reveals that architectural damage may occur when the PPV exceeds 5 mm/s, and structural damage may occur when the PPV exceeds 10 mm/s for modern buildings (Whiffin and Leonard, 1971).

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The PPV threshold tends to be lower for more sensitive historic buildings. In the German and Italian standards, the PPV threshold level for historic buildings is 3 mm/s (Hao, et al., 2001); in the Swiss standards, it is 1.5 mm/s (SNV, 1992). Nevertheless, Watts (1990) noted that traffic-induced vibration levels below these thresholds can still cause damage. Traffic-induced vibration may work as a trigger mechanism and produce additional static stress on the structural elements of buildings, which may already be weakened by other causes. Long and repeated periods of exposure to trafficinduced vibration are also known to be a threat to such buildings (ibid.). However, the threshold vibration level at which vibrations can be perceived is lower than the threshold associated with building damage, and traffic-induced vibration becomes intolerable to residents long before the vibration causes structural or architectural damage to the building (Hanson, et al., 2006; Hume, 1995). An in-dwelling vibration level slightly above the perception threshold is sufficient to cause annoyance to dwellers and lead to complaints (Hanson, et al., 2006; Hunaidi, 2000). A number of studies have focused on the annoyance levels caused by road and railroad traffic, including Klæboe, et al. (2003) and Peris, et al. (2012). A continuous, traffic-induced, sinusoidal, vertical vibration is perceptible when the PPV exceeds approximately .3 mm/s (Steffens, 1974), and it may be considered unacceptable to dwellers if it rises above 1 mm/s (Watts, 1990). Hanson, et al. (2006) suggested that complaints associated with traffic-induced vibration are also made when the vibration level is lower than the perception threshold. The earliest recorded studies on traffic-induced vibration date back to the early 1900s. In 1901, researchers examined the vibration levels induced by the Central London Railway, but the study could not be completed due to a lack of proper equipment (Hyde and Lintern, 1929). Hyde and Lintern, members of the UK’s National Physical Laboratory, were pioneers in reporting the indwelling vibration levels caused by passing motor vehicles. They concluded that road surface and bad tire conditions were the main causes of vibration (ibid.). Another early study was conducted by the National Research Council of Canada in the 1940s to assess vibration induced by busses in Winnipeg, Manitoba (Sutherland, 1951). A vast number of experiments have also been carried out by the UK’s Transport and Road Research Laboratory since the early 1960s (Crispino and D’Apuzzo, 2001). Based on the physical nature of the problem, a number of studies have developed numerical prediction models for traffic-induced vibration (François, et al., 2005; Grundmann, et al., 1999; Hao and Ang, 1998; Lombaert and Degrande, 2001; Lombaert, et al., 2000; Mhanna, et al., 2012). Road unevenness, vehicle dynamics, and soil characteristics have been used to explain traffic-induced vibration levels. Some studies have focused on traffic-induced vibration in single structures. Clemente and Rinaldis (1998) examined the Villa Farnesina, a masonry structure in Rome; Crispino and D’Apuzzo (2001) looked at the Palazzo San Teodoro, an old mansion in Naples; Kliukas, et al. (2008) researched the Arch-Cathedral Belfry in Vilnius, Lithuania; Korkmaz, et al. (2011) studied a masonry residential building located near a highway in Turkey; Li, et al. (2009) looked at the Kate Edger Information Commons building located on the University of Auckland City Campus in New Zealand; Morbia, et al. (2013) examined the Sidi Saiyyed Mosque and Astodiya Gate in Ahmedabad, India; and Pau and Vestroni (2008) examined the Colosseum in Rome. These studies aimed to measure the possible effects of road and vehicle characteristics and vehicle speed on traffic-induced, in-dwelling vibration levels. Hunaidi and Tremblay (1997) measured the vibrations induced by three different vehicles (a bus, a truck, and a drop-weight device) in nine different residential buildings in Montreal, Canada. They concluded that vibration is dependent on vehicle type, vehicle speed, road surface, and the measurement location. Other studies, including Watts and Krylov (2000) and Watts, et al. (1997), have examined vibration caused by road humps and speed cushions.

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A review of the literature shows that studies on traffic-induced vibration in buildings have been based heavily on engineering approaches and have focused on the physical nature of the problem. However, traffic-induced vibration is also a spatial issue and constitutes a real urban design and planning concern. None of the studies reviewed have approached this issue from a design and planning perspective. Thus, most of these studies are of little help in understanding the role of urban design and planning decisions in traffic-induced, ground-borne vibration levels. This study aimed to construct a simple model that could be used by designers and decision makers to control in-dwelling vibration levels prior to the architectural design and construction stages. Thus, the authors chose variables that can be controlled through urban design and planning decisions to explain the levels of traffic-induced, in-dwelling vibrations. The historic parts of cities are more sensitive to vibratory motion, and traffic-induced vibration is the most frequently blamed cause of deterioration in historic areas (Crispino and D’Apuzzo, 2001; Morbia, et al., 2013), so the study was conducted in a historic setting.

DATA COLLECTION AND PROCESSING The authors recorded in-dwelling vibration velocities induced by different types of vehicles at 10 different historic masonry houses in Birgi, Turkey. Birgi is located in the Aegean region of Turkey in the province of Izmir, 68 miles (110 km) from the city of Izmir. This small town was once a cultural and political center and served as the capital of the Aydinoglu Beylik (Sultanate). The architecture of Birgi benefited from its status as a capital, and it has become one of the keystones of Turkish civilian architecture (UNESCO, 2012). Birgi is currently on the United Nations Educational, Scientific and Cultural Organization’s (UNESCO) tentative list of World Heritage sites, considered a cultural heritage site “of outstanding universal value” (ibid.). There are 163 registered historic buildings under legal protection in Birgi, 116 of which are used as residences. In 96 of these buildings, the width of the fronting street does not permit wide vehicles, including trucks and busses, to pass. Three of the remaining 20 buildings lack a fronting street, two are located on dead-end streets, and two front on only a pedestrian path. Thus, there were 13 residential buildings that were suitable for vibration measurements. However, the owners of three of these buildings were unavailable at the time of the study, so measurements were only taken in the 10 buildings shown in Figure 1. All 10 of the buildings considered in the study were one- or two-story masonry structures used only for residential purposes (Figure 1). Masonry buildings are common in Turkey, especially in rural areas, on the outskirts of urban areas, and in historic town centers. There are nearly four million masonry buildings in Turkey, generally constructed using traditional methods (Turer, et al., 2007) in which mortar joints connect the stone blocks (Korkmaz, et al., 2011). The measurements were conducted on April 11-19, 2012. The daily maximum temperature was between 64°F and 75°F (18°C and 24°C), and the weather was often partly cloudy. The authors used Brüel & Kjær RT Pro Photon software and a four-channel, portable, real-time, dynamic signal analyzer with three Brüel & Kjær (Type 8340) high sensitivity (10 V/g) piezoelectric annular shear design accelerometers for the measurements. The accelerometers were calibrated in the frequency range of 5-1,500 Hz. The sensitivity was measured at 159.2 Hz with a 95% confidence level, using a coverage factor of k = 2. The measurement parameters of the signal analyzer were set to a fast Fourier transform (FFT) resolution of 400 lines, a frequency range of 125 Hz, and a sampling rate of 320 Hz. For the measurements, the accelerometers were placed in the entrance room of each building on the ground floor. One of the accelerometers was placed in the geometric center of the room, and the

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FIGURE 1. The 10 buildings where the traffic-induced vibration measurements were conducted.

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remaining two accelerometers were placed in the corners of the room closest to the street, facing the street. The accelerometers were attached directly to the floor using duct tape. Measurements were taken for six types of vehicles: a truck, a midsize bus, an ambulance, a fire truck, a tractor, and a car. For each measurement, only a single vehicle was allowed to pass on the street. The street was cleared of other moving objects, including other vehicles, people, and animals, and all people left the building. Mechanical devices that could produce vibrations, such as washing machines and dishwashers, were turned off. We obtained a total of 612 measurements from the three accelerometers. The PPV ranged from .01 mm/s to .34 mm/s. The highest measured PPV was slightly over .3 mm/s, the minimum perceivable level suggested by Steffens (1974). It is worth mentioning that, according to Athanasopoulos and Pelekis (2000) and Hao, et al. (2001), the smallest displacement and velocity responses occur on the ground-floor level, and the authors of this study expect that the PPV levels would be higher on the upper floors of the buildings in the sample. Because the vibration levels recorded by the three accelerometers were not independent from one another, only the measurements from the accelerometer placed in the geometric center of the rooms were used in statistical modeling. The center accelerometer data were chosen for analysis because in-dwelling vibration levels are considerably higher at the center of the floor (Hyde and Lintern, 1929). Of the 204 traffic-induced vibration measurements taken in the center of the rooms, five had measurement errors. Thus, the data used in the analysis consisted of 199 traffic-induced, indwelling vibration measurements. With the exception of the soil characteristics data, all other data were collected through field surveys taken at the time of the vibration measurements. The soil characteristics data were obtained from a geological survey completed in 1993 by the Birgi Municipality (1993). Following Hajek, et al. (2006), the explanatory variables were divided into three categories — source, transmission medium, and receiver — and data were collected on each. The source variables included measures pertaining to the vehicles and the road, the transmission-medium variables included measures pertaining to the soil and other structures between the source and the receiver, and the receiver variables included measures pertaining to the building. The function of trafficinduced, ground-borne, in-dwelling vibration (PPV) at the center of the ground floor is as follows: PPV = f (S, T, R) where S is the vector of source characteristics, T is the vector of transmission-medium characteristics, and R is the vector of receiver characteristics. Data were collected for over 80 explanatory variables under these three vectors. A closer look at the soil data revealed that the soil characteristics for the 10 buildings were very similar, and there was little variation in the soil variables. Thus, the variables pertaining to the soil characteristics turned out to be insignificant in the estimation process, and they were dropped from the equation. The variables pertaining to road pavement were also dropped from the equation for the same reason. The selected statistical model included 12 explanatory variables. The S vector included four variables: (1) total weight of the vehicle (Weight), (2) untransformed vehicle speed (Speed), (3) square root of vehicle speed (SpeedSqRt), and (4) tire combination of the vehicle (whether all of the tires were the same size or not) (Tire). The T vector included five variables: (1) slope of the street or road (Slope); (2) presence of a wall between the building and the road (Wall); (3) presence of an empty space, garden, or park between the building and the road (Space); (4) presence of a water canal between the building and the road (Canal); and (5) number of road intersections around the building (Intersect). Finally, the R vector included three variables: (1) footprint area of the building

Journal of Architectural and Planning Research 32:4 (Winter, 2015) 314 TABLE 1. Descriptive statistics for the sample (n = 199). ______________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________ Variable Minimum Maximum Mean Standard deviation _______________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________ PPV (mm/s) .006 .342 .118 .117 Weight (tons) 1.37 37.50 8.245 9.575 Speed (km/h) 5 75 34.804 19.511 Tire* 0 1 .789 .409 Slope (%) 5.119 9.044 7.700 .954 Wall* 0 1 .563 .497 Space* 0 1 .673 .470 Canal* 0 1 .206 .405 Intersect (#) 1 4 1.754 1.257 Area (m2) 42.020 154.288 95.225 31.662 Terrace* 0 1 .111 .314 Concrete* 0 1 .251 .435 Soil* 0 1 .095 .295 Wood* 0 1 .005 .071 Stone* 0 1 .342 .475 Slate* 0 1 .101 .301 Mosaic* 0 1 .106 .308 _______________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________ Note. Asterisk indicates the variable is a dummy variable. ______________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________

(Area), (2) presence of other buildings sharing at least two side walls with the building (Terrace), and (3) the floor-covering material (Floor). Floor was a categorical variable that included seven different materials: concrete, bare soil, wood, stone, slate, mosaic coating, and tile. Mosaic coating is a traditional type of flooring made of small, white, irregular-shaped natural stones and concrete. It differs from conventional mosaic tiling, which is often made of square pieces of ceramic or glass. Dummy variables were used to indicate six of the flooring materials (Concrete, Soil, Wood, Stone, Slate, and Mosaic); the seventh material, Tile, was indicated by giving all of the floor dummy variables a value of zero. For all of the other dummy variables, presence was indicated by a value of one, and absence was indicated by a value of zero. Descriptive statistics for all of the variables included in the model are presented in Table 1.

ANALYSIS AND RESULTS The generation and transmission of ground-borne, in-dwelling vibration has a very complex nature (Xu and Hong, 2008). This study aimed to construct a simple model that could be used by designers and decision makers to control in-dwelling vibration levels prior to the architectural design and construction stages. The estimated regression model was linear in terms of the parameters of the explanatory variables (Weight, Speed, Tire, Slope, Wall, Space, Canal, Intersect, Area, Terrace, Concrete, Soil, Wood, Stone, Slate, and Mosaic) and the dependent variable (PPV). Although the transformed forms, including logarithmic, square, square root, and Box-Cox forms, of the dependent and independent variables were also considered, the linear model yielded statistically better results. Only the variable for the square root of the vehicle speed (SpeedSqRt) was included in the model (together with the variable for the untransformed vehicle speed, Speed) because the relation between PPV and vehicle speed was found to be nonlinear. A constant was also included in the model, but the interaction variables turned out to be insignificant. Thus, the selected model can be written as follows: PPV = ß0 + ßS S + ßT T + ßR R + ε where ß0 is the parameter for the constant term, ßS is the vector of parameters for the source variables, ßT is the vector of parameters for the transmission-medium variables, ßR is the vector of

Journal of Architectural and Planning Research 32:4 (Winter, 2015) 315 TABLE 2. Parameter estimates for the proposed model (dependent variable = PPV). _______________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________ Variable Coefficient t-statistic p-value ______________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________ Constant -1.257 -4.158 .000 Weight .001 2.880 .004 Speed .003 2.251 .026 SpeedSqRt -.031 -2.219 .028 Tire -.035 -2.997 .003 Slope .072 4.073 .000 Wall -.102 -3.415 .001 Space -.144 -3.609 .000 Canal -.094 -2.533 .012 Intersect .157 4.587 .000 Area .005 7.538 .000 Terrace -.301 -9.477 .000 Concrete .270 5.761 .000 Soil .603 10.362 .000 Wood .178 2.696 .008 Stone .584 7.193 .000 Slate .362 7.550 .000 Mosaic .175 4.824 .000 ______________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________

parameters for the receiver variables, and ε is the error term. The error term is assumed to be a random variable that is independent of any explanatory variable and has an expected mean of zero and a constant variance. The authors performed the regression analysis with ordinary least squares (OLS). Table 2 presents the parameter estimates for the proposed model. All of the explanatory variables were statistically significant at the .03 level. This simple linear regression model was able to explain 86.7% of the variance in traffic-induced, ground-borne, in-dwelling vibration (R2 = .867). The findings of this study regarding the source characteristics parallel the claims found in the literature. As in Clemente and Rinaldis (1998), Hendriks (2002), Hunaidi and Tremblay (1997), Lombaert and Degrande (2001), Watts (1990), and Watts and Krylov (2000), this study found that traffic-induced vibration increased with vehicle weight. The analysis results showed that a 1 ton (1,000 kg) increase in the total weight of the vehicle increased the PPV by .001 mm/s. The lightest vehicle used in the study weighed 1.37 tons, and the heaviest weighed 37.50 tons. Thus, all other variables being equal, there was a .036 mm/s difference between the vibrations induced by these two vehicles. The results indicated that vehicle speed was also a determinant of traffic-induced, ground-borne, in-dwelling vibration, as suggested in a number of studies, including Crispino and D’Apuzzo (2001); Hajek, et al. (2006); Hendriks (2002); Hunaidi and Gallagher (2001); Hunaidi and Tremblay (1997); Klæboe, et al. (2003); Lombaert and Degrande (2001); Pau and Vestroni (2008); Watts (1990); and Watts and Krylov (2000). Unlike other variables in the model, the relation between vehicle speed and traffic-induced, in-dwelling vibration appeared to be nonlinear. The aggregate effect of vehicle speed on PPV was as follows: .003 × SPEED − .031 SPEED

This effect can be illustrated using data for a hypothetical house, where the dummy variables are set to the most frequently observed values, and the values of all other explanatory variables are set at the sample mean (as in Table 1). Figure 2 shows the traffic-induced, in-dwelling vibration levels in mm/s when the vehicle speed varies between 5 km/h and 160 km/h. It is interesting to see that the PPV decreases as the vehicle speed increases between 5 km/h and 37.3 km/h and then starts to

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.50 .45 .40

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FIGURE 2. PPV versus vehicle speed for a hypothetical house with the dummy variables set to the most frequently observed values and the values of all other explanatory variables set at the sample mean.

increase with the vehicle speed when the speed is over 37.3 km/h. Thus, we identified the optimal vehicle speed for keeping traffic-induced, in-dwelling vibration at a minimum for this sample. The estimated parameters showed that the vibration level also decreased by .035 mm/s when the tires of the vehicle were the same size. Although this issue has not previously been considered directly in traffic-induced vibration studies, it has been noted by some researchers. Hyde and Lintern (1929) were the first to argue that tire conditions are the main cause of vibration, while Hajek, et al. (2006) suggested that tires have a direct effect on traffic-induced vibration levels, and Pau, et al. (2005) mentioned that irregularity in tires may increase traffic-induced vibration levels. The findings of this study regarding the characteristics of the transmission medium are also consistent with claims found in the literature. The results of this study showed that the level of traffic-induced vibration increased with an increase in the slope of the road, as suggested in Hao and Ang (1998) and Lombaert and Degrande (2001). A 1% increase in the slope increased the PPV by .072 mm/s. The results indicated that the presence of an empty space, like a park or a garden, between the building and the road had the opposite effect — decreasing traffic-induced, indwelling vibration by .144 mm/s. This finding is consistent with Hajek, et al. (2006); Hao and Ang (1998); Hunaidi (2000); and Hunaidi and Tremblay (1997), who suggested that vibration decreases with distance. In addition, the results showed that the presence of a water canal between the building and the road decreased the level of traffic-induced vibration by .094 mm/s. As empirically shown by Korkmaz, et al. (2011), the effect of traffic-induced vibration can be reduced by trenches between the source and the receiver. They also reported that the filling material does not change the magnitude of this effect. It is therefore plausible to argue that a canal functions like a trench and reduces the level of traffic-induced, ground-borne vibration. A similar argument can be made for the presence of walls. Hunaidi (2000) argued that constructing walls adjacent to the road could be an economical alternative to trenches in residential areas, though he also mentioned that the effectiveness of such walls in reducing vibration levels had not yet been proven. The current study showed that the presence of garden walls reduced traffic-induced vibration by .102 mm/s, supporting Hunaidi’s

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claim. Thus, we found the presence of a wall to be slightly more effective than the presence of a water canal in decreasing traffic-induced, ground-borne, in-dwelling vibration. Furthermore, this study found that the level of traffic-induced vibration increased with the number of intersecting streets adjacent to the building. If there were no intersecting streets, the number of intersecting streets was equal to 1. If there were two intersecting streets on any side of the building, the value for the variable was two, and so on. The findings showed that the PPV increased by .157 mm/s with each additional street intersection. This finding is consistent with Watts (1990), who claimed that vibration levels are higher at the intersections of roads. This is a reasonable argument, as a building becomes subject to higher amounts of traffic-induced vibratory motion when there are more intersections around it. In terms of the receiver characteristics, the findings showed that vibration levels increased with an increase in the footprint area of the building. The PPV increased by .005 mm/s for every 1 m2 increase in the footprint area of the building. Although Hajek, et al. (2006); Hunaidi (2000); and Hunaidi and Tremblay (1997) suggested that the characteristics of a building are determinants of traffic-induced vibration levels, footprint area has not been considered directly in the literature. Nevertheless, this finding is plausible, as one would expect that the traffic-induced vibration a building receives would increase as the size of the building increases. The estimated model indicated that terrace or row housing, where the buildings share at least two side walls, decreased the PPV by .301 mm/s. This result is also reasonable, as a portion of the stress waves traveling through the soil are captured by the adjacent buildings in the case of terrace or row housing. Finally, this study found that traffic-induced vibration is dependent on building materials, consistent with Hao, et al. (2001). The results showed that the floor material was a significant determinant of traffic-induced, in-dwelling vibration levels. Bare soil yielded the highest level of vibration, followed by stone, slate, concrete, wood, mosaic coating, and tile. Their relative effects on trafficinduced vibration were .603 mm/s, .584 mm/s, .362 mm/s, .270 mm/s, .178 mm/s, and .175 mm/s respectively; tile was the benchmark floor material, with a PPV of 0 mm/s.

CONCLUSION Traffic-induced vibration is a serious urban problem. However, this frequently cited source of annoyance had not been assessed from an urban design and planning perspective. Although ground-borne, in-dwelling vibration is complex in nature (Xu and Hong, 2008), a simple linear regression model may explain a great portion of the variance in vibration levels. The results from the estimated model used in this study indicated that traffic-induced vibration could largely be controlled through urban design and planning decisions. Simple design policies, such as encouraging the use of garden walls, water canals, and open spaces between roads and dwellings, may help decrease traffic-induced vibration significantly. Aside from being decorative landscape elements and structures to separate different uses and properties, both garden walls and water canals appear to be effective in keeping traffic-induced vibration at low levels. Planning open spaces or increasing the minimum-allowed distance between buildings and roads would also help decrease traffic-induced vibration levels. This study found that the presence of all three design elements together could decrease the PPV by .340 mm/s. Another effective way of controlling traffic-induced vibration levels appears to be encouraging the construction of terrace or row housing, rather than detached housing. The construction of row houses, as well as connecting buildings with small arcs, has been popular throughout the history of

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cities. This simple design strategy has long benefited cities, mainly considering vibration caused by earthquakes, and it may well be adopted to help decrease traffic-induced vibration levels in dwellings. Figure 3 shows the use of one of these arcs in the old medieval city of Mesta, Greece. Furthermore, this study showed that the design of the street network is also an effective means of controlling vibration. Traffic-induced vibration levels increase with the number of intersections adjacent to buildings. Thus, additional precaution should be taken when designing and planning buildings that are adjacent to multiple road intersections. Avoiding adjacent multi-road intersections and selecting less steep routes in the design of street networks may also be helpful. This study supports the idea that restrictions on vehicle speeds and weights, which are almost standard policies in historic town centers, are effective strategies against traffic-inFIGURE 3. Example of an arc used to connect adjacent buildings in Mesta, Greece, to decrease vibration. duced vibration. However, the results showed that the relation between vehicle speed and traffic-induced vibration is not linear, and upper speed limits may not be the best solution. In some cases, lower speeds may increase the level of traffic-induced vibration. Thus, optimal speed ranges should be adopted, accounting for the characteristics of the urban environment in question. Floor material and building size also appear to be strong determinants of traffic-induced vibration levels. Urban design and planning decisions may encourage developers to use wood, mosaic coating, and tile as floor-covering materials on the ground floor. Optimal building sizes for lower vibration levels can be derived using the estimated model and data from the building site. The estimated model shows that, if all of these urban design and planning decisions were considered simultaneously, traffic-induced vibration levels could be kept below perceivable levels. This argument can be illustrated by comparing the expected vibration levels of two hypothetical cases. In both cases, the vibrations are induced by a vehicle with tires of asymmetric sizes (like a tractor), and the buildings have the same footprint area of 154.3 m2, the maximum area in the sample. In both cases, the floor-covering material is bare soil. However, in Case 1, all other variables are set to the sample values to induce the maximum amount of vibration, considering the model results. Thus, there is no garden wall, no water canal, and no park or garden between the road and the building. The house is detached, and the slope of the road and the number of intersections are set to their maximum observed levels, 9.044% and four respectively. In Case 2, all other variables are set to the sample values to induce the minimum amount of vibration. Thus, there is a garden wall, a water canal, and a park between the road and the building. The house is a terrace house, and the slope

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FIGURE 4. PPV values for different combinations of vehicle speed and vehicle weight in Cases 1 and 2.

of the road and the number of intersections are set to their minimum observed levels, 5.119% and one respectively. Figure 4 shows the traffic-induced vibration levels for both cases when the vehicle weight varies between 1.5 tons and 37.5 tons, and the vehicle speed varies between 5 km/h and 200 km/h. According to the findings, the maximum estimated level of traffic-induced vibration in Case 2 is .114 mm/s. In comparison, in Case 1, the PPV estimates vary between 1.299 mm/s and 1.511 mm/s. All of the estimated traffic-induced vibration levels in Case 1 are over the threshold for perception suggested by Steffens (1974) (.3 mm/s) and the threshold for annoyance and public complaints suggested by Watts (1990) (1.0 mm/s). It is also important to note that the estimated PPV levels in Case 1 exceed 1.0 mm/s, the lowest PPV threshold level for architectural damage suggested in the literature (Garg and Sharma, 2010), and 1.5 mm/s, the PPV threshold level for historic buildings in the Swiss standards (SNV, 1992). It is clear from Figure 4 that traffic-induced vibration can be reduced significantly and kept below the threshold levels for perception, annoyance, and architectural damage using variables that can be controlled through urban design and planning decisions. However, it may not be possible to apply all of these urban design and planning decisions at the same time due to spatial, economic, and social constraints. In that case, the estimated model can be used to find alternative solutions to keep traffic-induced vibration at acceptable levels.

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This research has some drawbacks. First, although soil characteristics are known to be important determinants of traffic-induced vibration levels, the variables pertaining to the soil characteristics turned out to be insignificant in the current model. A plausible explanation for this finding is that the variation in the soil characteristics for the buildings in the sample was very low. Second, the current model does not account for weather conditions. Third, the model presented here is constructed on a single-vehicle case, whereas the multi-modal traffic in real-life city scenarios has a much more complex structure. A more comprehensive study should cover observations from different soil structures in different weather conditions in a more complex urban setting. Finally, the statistical results derived from the observed sample are limited to the range of the observed values of the variables. A more comprehensive sample of observations with wider ranges of vibration levels and explanatory variables is required to generalize the results obtained from the current study. Nonetheless, this study shows that a simple model can explain a great portion of the variance in traffic-induced, ground-borne, in-dwelling vibration levels in an urban setting. The estimated model can be used by practitioners in the fields of urban design and planning to find alternative solutions to keep traffic-induced vibration at acceptable levels prior to the architectural design and construction stages.

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Watts GR, Krylov VV (2000) Ground-borne vibration generated by vehicles crossing road humps and speed control cushions. Applied Acoustics 59(3):221-236. Whiffin AC, Leonard DR (1971) A survey of traffic-induced vibrations (Report no. Lr 418). Crowthorne, UK: Transport and Road Research Laboratory. Xia H, Zhang N, Cao YM (2005) Experimental study of train-induced vibrations of environments and buildings. Journal of Sound and Vibration 280(3):1017-1029. Xu YL, Hong XJ (2008) Stochastic modelling of traffic-induced building vibration. Journal of Sound and Vibration 313(1):149-170. Additional information may be obtained by writing directly to Dr. Cubukcu at Dokuz Eylul Universitesi, Mimarlik Fakultesi, Buca, Izmir 35160, Turkey; email: [email protected].

ACKNOWLEDGMENTS The authors would like to thank the Municipality of Birgi and Mayor Cumhur Sener for their continuous support and Dokuz Eylul University for its financial support of this research (Project no. 2011.KB.FEN.018). The authors would also like to thank Yildirim Oral, Serhan Tanyel, A. Emel Goksu, T. Kerem Koramaz, and the anonymous reviewers for their valuable comments. Any mistakes that remain are our own. AUTOBIOGRAPHICAL SKETCHES Irem Ayhan Selcuk is an assistant professor of city and regional planning at Dokuz Eylul University, Turkey. She received her BCP, MCP, and PhD degrees in city and regional planning from Dokuz Eylul University. Her research interests include urban transportation, quantitative planning techniques, and preservation of cultural heritage. K. Mert Cubukcu is a professor of city and regional planning at Dokuz Eylul University, Turkey. He holds a bachelor of city planning degree from Middle East Technical University, Turkey, and a PhD in city and regional planning from The Ohio State University, U.S. Dr. Cubukcu is also the head of the Izmir (Region II) Council for Cultural Heritage Preservation. His research interests include quantitative planning techniques, spatial statistics, transport modeling, and geographic information systems. Manuscript revisions completed 9 September 2015.