ARID ZONE JOURNAL OF ENGINEERING, TECHNOLOGY & ENVIRONMENT AZOJETE September 2024. Vol. 20(3):581-600 Published by the Faculty of Engineering, University of Maiduguri, Maiduguri, Nigeria. Print ISSN: 1596-2490, Electronic ISSN: 2545-5818 www.azojete.com.ng Corresponding author’s e-mail address: awosanyaolugbenga2015@gmail.com 581 DEVELOPMENT OF AXLE LOAD SPECTRA FOR INTEGRATION IN TRAFFIC CHARACTERIAZTION FOR NIGERIAN EMPIRICAL MECHANISTIC PAVEMENT ANALYSIS AND DESIGN SYSTEMS D. O. Awosanya*, A. A. Murana, and A. A. Olowosulu Department of Civil Engineering. Ahmadu Bello University, Zaria, Nigeria *Corresponding author's email address: awosanyaolugbenga2015@gmail.com ARTICLE INFORMATION Submitted 29 March, 2023 Revised 19 December, 2023 Accepted 23 December, 2023 Keywords: Traffic Analysis Axle load spectra Traffic Classification WIM Axle load distribution (Axle load spectra) Axle Load Quantification factors ABSTRACT Design traffic is employed in this research to estimate the expected load repetitions for a given axle load spectrum during the pavement life. In changing traffic characterization in mechanistic-empirical (M-E) design procedure, the process does away with the equivalent single axle load (ESAL) concept and traffic analysis implementation was done by directly using axle load. Comparison of predicted Number of Axle load range repetitions determined by equivalent axle load factor (EALF) and general axle load equivalency factors (GAF), is the main objective. In this study, axle load spectrum by Weigh-In-Motion (WIM) data and parametric standard axle loads which cause equal damage were collected and utilized. Determination of the axle load distribution factor (ALDF) from the WIM data. It is a critical input in the method based on different axle load groups for the estimation of the predicted number of axle load repetitions during the pavement design period. The results that the marked difference on the estimated predicted axle load predictions with the determination of EALF and GAF 63.95% of GAF value for SAST. Similarly, 10.14% for SADT; 30.24% for TADT and 21.43% for TRDT. This indicates the inherited error in the empiricism content in the empirical approach of handling traffic, for the determination of EALF. The estimated traffic prediction value increases as the captured axle load magnitude increased, as a resultant of the type of commodities and goods carried on the roadway pavement. 1.0 Introduction The characterization of traffic is one of the significant elements in the analysis and design of pavements (flexible, rigid, and composite), (Buch et al., (2009), Timm and Newcomb (2002) observed that the primary change in traffic analysis when using mechanistic -empirical (M-E) design is the elimination of the load equivalency factors and calculation of equivalent single axle load (ESALs), and determines the expected number of repetitions of each load during the pavement life directly using axle load spectra. In this approach, the anticipated truck traffic is classified by axle type (steering, single, tandem, and tridem), and within each axle type, the distribution of axle weights is calculated. This is a more precise characterization of truck traffic loads, and it allows for more detailed and nuanced damaged investigations (Salama and Chatti, 2011). Pavement engineering calculations using axle load spectra are generally more complex than those using ESALs or Traffic Index (TI) because loading cannot be reduced to one equivalent number. However, the load spectra approach of quantifying traffic loads offers a more practical and realistic representation of traffic loading than using TI or ESALs (Highway Design Manual, 2012). The load input to (M-E) methods in changing of traffic characterization is in terms of axle load distributions (i.e., load spectra) by axle configuration. This approach is a significant improvement over the aggregate ESAL-based method described previously, because it allows a http://www.azojete.com.ng/ mailto:%20efegabs@gmail.com mailto:%20salami.lukman@adelekeuniversity.edu.ng mailto:%20salami.lukman@adelekeuniversity.edu.ng Arid Zone Journal of Engineering, Technology and Environment, September 2024; Vol. 20(3)581-600. ISSN 1596-2490; e-ISSN 2545- 5818; www.azojete.com.ng Corresponding author’s e-mail address: awosanyaolugbenga2015@gmail.com 582 mechanistic pavement design approach. (Papagianakis and Massad, 2008). Axle load distribution factors (ALDF) represent the percentage of the total number of truck axle repetitions within each load interval (which varies with axle type) for each axle type. ALDF constitute a major change from the current 1993 AASHTO pavement design guide that requires only the total number of 18-kip ESALs as input (Kim et al., 2011) In characterizing effects of applied loads of mixed-traffic stream, the combined loading effects of different axle types in the design traffic must be considered when designing a pavement. There are two common approaches by which the combined effects are evaluated: one based on the hypothesis of cumulative damage, and the other by the method of equivalent load. Under the hypothesis of cumulative damage, where the design repetitions of load for each axle type is a critical input whereby the total amount of damage caused by the mixed traffic is computed as the sum of damage ratios of all load types. (Oguara, 2004, Fwa et al., 2006; Huang, 2007; and Timm and Newcomb, 2002) Method based on different axle load groups in traffic analysis is when the design is based on a number of axle load groups, as characterized by the ALDF, whereby, the predicted number of repetitions for each axle load group during the design period is determined. The procedure for predicting the predicted number of repetitions for each axle load group during the design period is similar to the method based on ESAL, except that each load group is considered separately. The initial repetitions of each axle type, growth factor, directed distribution factor (DDF), and lane distribution factor (LDF) are determined. These are critical variables required for the computation of repetitions of each axle type for a period of 20 years for a flexible pavement. Nigerian Empirical Mechanistic Pavement Analysis and Design System is a framework for Mechanistic- Empirical Pavement Design for tropical climate developed by Olowosulu, (2005). Two major components in NEMPADS that required to be refine, that were (1) traffic data are required in the M-E pavement design procedure. It is expressed in terms of 8, 200 kg (80 kN) equivalent single axle loads (ESALs)and secondly, simplified procedure for prediction of pavement response in the prediction pavement performance. The focus on changes in traffic characterization is the main interest in this research study, for transition from using equivalency factors to determine the number of equivalent single axle loads (ESALs) to a push toward using load spectra in NEMPADS. Report 1-26 recommended that traffic should be divided into a number of load groups, each with different load magnitudes and configurations and different numbers of load repetitions (Huang, 2007). The main objective of this study is to compare the estimated number of repetitions of each axle load range for each axle type during the pavement life using EALF determined from the empirically based approach and determined GAF from the (M-E) methods, respectively, and then to recommend the most accurate and reliable estimated predicted number of load repetitions This is achieving with following objectives:( 1) Determination of axle load damage factor with the same traffic composition with either of these pavement design approaches: Computation of EALF based on empirical based method and GAF based on (M-E) method. (2) Estimation of the expected number of repetitions of each axle load range during the pavement life by either utilizing EALF or GAF. The scope of this study, includes the (1) the collection of traffic information that includes the axle load spectrum by portable WIM data and loading on the axle configurations considered in this study, that cause the same amount of damage at the standard axle. The determination of ALDF is from the analysis of the traffic data. Traffic analysis using the method based on different axle load groups technique has been used to estimate the predicted number of load repetitions. file:///C:/user/Downloads/azojete143/www.azojete.com.ng mailto:%20kunleoluyori@gmail.com Awosanya et al: Development of Axle Load Spectra for Integration in Traffic Characteriaztion for Nigerian Empirical Mechanistic Pavement Analysis and Design Systems. AZOJETE, 20(3):581-600. ISSN 1596-2490; e-ISSN 2545-5818, www.azojete.com.ng Corresponding author’s e-mail address: awosanyaolugbenga2015@gmail.com 583 Axle load damage factors were computed on the basis of the pavement design approach. EALF was computed on the basis of handling traffic on empirically based approach and GAF was computed on the basis of handling traffic in (M-E) methods. Both axle load damage factors were the critical tools with the ALDF were the determined traffic inputs utilized to estimate the predicted number of load repetitions within the two considered approaches respectively, and then compared with each other. The scope of this study, includes the (1) the collection of traffic information that includes the axle load spectrum by portable WIM data and loading on the axle configurations considered in this study, that cause the same amount of damage at the standard axle. The determination of ALDF is from the analysis of the traffic data. Traffic analysis using the method based on different axle load groups technique has been used to estimate the predicted number of load repetitions. Axle load damage factors were computed on the basis of the pavement design approach. EALF was computed on the basis of handling traffic on empirically based approach and GAF was computed on the basis of handling traffic in (M-E) methods. Both axle load damage factors were the critical tools with the ALDF were the determined traffic inputs utilized to estimate the predicted number of load repetitions within the two considered approaches respectively, and then compared with each other. 2. Materials and Methods. To determine the number of load repetitions of each axle type for a period of 20 years on a flexible pavement, the required transport information and the analysis of the traffic data are utilized in the following considerations: 2.1 Input Data Characterization and Axle Load Spectra This includes truck traffic load data; axle configuration and equivalencies; ALS records; analysis of ALS data of the independent axle load surveys; and truck traffic information. 2.1.1 Truck Traffic Load Data Truck traffic information is used for estimating pavement loading known as Axle Load Spectra, incorporated into the (ME) design procedure. This information involves the independent axle load surveys and loads on the axle configurations that cause the same amount of damage as the standard axle 2.1.2 Axle Configuration and Equivalencies The damage due to different axle groups is dependent on the axle spacing, the number of types per axle, the load on the group and the suspension. For design purposes, it is generally appropriate to consider axle groups in terms of the following four types: • Single axle with single wheels; (SAST); • Single wheels single axle with dual wheels; (SADT); • Tandem axles both with dual wheels; (TADT); • Tridem axles all with dual wheels; (TRDT). Table 1: Axle Loads which cause Equal Damage Axle Configuration SAST SADT TADT TRDT Load (kN) 53 80 135 181 Source: Road Design Part 2- Pavement, CAMPWO3-102-99; Austroads (2004b) and 2012 The standard axle is defined as a defined as a single axle with dual wheels that carries a load of 8.2 tonnes (80kN). Loads on the axle configurations given above that cause the same amount of damage as the standard axle are given in Table1. http://www.azojete.com.ng/ mailto:%20edetjoseph1991@gmail.com%09 Arid Zone Journal of Engineering, Technology and Environment, September 2024; Vol. 20(3)581-600. ISSN 1596-2490; e-ISSN 2545- 5818; www.azojete.com.ng Corresponding author’s e-mail address: awosanyaolugbenga2015@gmail.com 584 2.1.3 ALS records According to Ahmed and Erlingsson, (2015), ALS is one of the key inputs for M-E design of pavement structures (Lu and Harvey 2006). Swan et al. (2008) have also indicated that the ALS was one of the most significant factors in the M-E pavement design. In this paper, the axle loads and configurations from WIM instruments are usually presented in the form of the ALS or frequency distribution of the axle load groups of a given axle configuration. Therefore, raw WIM data must be analyzed to produce the ALS. This traffic data was captured by a portable weigh-in-motion (WIM) System on Kaduna - Zaria Roadway with the following details: (i) Number of axles; (ii) Gross Vehicle Mass (GVM); and (iii) Individual axle weights, recorded in” tons”. The data are grouped into different axle loads for each axle configuration (steering, single, tandem, tridem and quad). Usually, the axle load interval or bin width of 10 kN is used to group the axle loads for SAST and SADT; bin width of 20kN for TADT and bin width of 30kN for TRDT. The frequency for each load group is then calculated to derive the ALS. 2.1.4 Analysis of ALS data of the independent axle load surveys The independent axle load surveys have become the main source of up-to-date vehicle weight data, as there have no recent data on weighbridge operations is available. The WIM Data contains Vehicle Classification and axle group load data. Traffic survey conducted as secondary data by (Stewart Scott International, 2007) on Kaduna - Zaria Northbound and Zaria - Kaduna Southbound. ALS data captured on the Kaduna –Zaria Roadway was used for the study. Measurements were made on the Northbound Axis (Kaduna-Zaria) on 5th March, during the 9.am and 3.00PM (About 6hrs) and similar exercise was conducted on the Southbound Axis (Zaria-Kaduna) on 6th of March, 2007. The ALS for the Roadway have been developed which provide the load distribution of steering axles, other single axles, tandem axles and tridem representative ALS for the roadway. The ALS for the sites has been developed which provide the load distribution of steering axles, other single axles, tandem axles and tridem axles (Awosanya et al., 2021). ALS data from the independent axle load surveys was used for the study. 2.1.5 Truck Traffic Information Truck traffic information adopted from the secondary data of the work of S.S.I (2007) includes: Results of Independent Axle Load Surveys for Kaduna-Zaria Roadway Traffic Volume: ADT (2006) = 11,000; Percent Truck =8%: ADT (1983) =7402 Defaults = DDF = (1.0); LDF (0.5); Design Period=20yrs 2.2 Methods 2.2.1 Axle Load Information from the truck traffic information. Axle load information includes axle configurations, average number of axles per truck, and axle load distribution factors. 2.2.1.1 Axle Configuration Axle configuration is defined by the number of axles sharing the same suspension system and the number of tires in each axle. Multiple axles involve two, three, or four axles spaced 1.2 to 2.0 meters apart, and are referred to as tandem, triple, or quad, respectively. They are treated differently from single axles because they impose pavement stresses/strains that overlap (Papagiannakis and Massad, 2008). 2.2.1.2 Average number of axles per truck This input represents the average number of axles for each truck class (classes 5 to 13) for each axle type (steering, single, tandem, and tridem).M-E procedure allows for dividing the single axle category into steering single axle (Single tire) and other single axle (dual tires). A is the average number of axles per truck, is the ratio between the cumulative number of all the axles from the total weighed trucks and the cumulative vehicle weighed on the roadway, which is computed for each of the axis on the roadway. This variable is as presented in Equation 1. file:///C:/user/Downloads/azojete143/www.azojete.com.ng mailto:%20kunleoluyori@gmail.com Awosanya et al: Development of Axle Load Spectra for Integration in Traffic Characteriaztion for Nigerian Empirical Mechanistic Pavement Analysis and Design Systems. AZOJETE, 20(3):581-600. ISSN 1596-2490; e-ISSN 2545-5818, www.azojete.com.ng Corresponding author’s e-mail address: awosanyaolugbenga2015@gmail.com 585 A= 𝑇𝑜𝑡𝑎𝑙 𝑛𝑢𝑚𝑏𝑒𝑟 𝑜𝑓 𝑎𝑙𝑙 𝑡ℎ𝑒 𝑛𝑜𝑟𝑚𝑎𝑙𝑖𝑧𝑒𝑑 𝑎𝑥𝑙𝑒 𝑛𝑢𝑚𝑏𝑒𝑟𝑓𝑟𝑜𝑚 𝑡ℎ𝑒 𝑚𝑒𝑎𝑠𝑢𝑟𝑒𝑑 𝑑𝑎𝑡𝑎 𝑇𝑜𝑡𝑎𝑙 𝑛𝑢𝑚𝑏𝑒𝑟 𝑜𝑓 𝑎𝑙𝑙 𝑡ℎ𝑒 𝑡𝑟𝑢𝑐𝑘𝑠 𝑤𝑒𝑖𝑔ℎ𝑒𝑑 𝑏𝑦 𝑡ℎ𝑒 𝑊.𝐼.𝑀 (1) 2.2.1.3 Axle Load Distribution Factors The axle load distribution factors (ALDF) simply represent the percentage of the total axle applications within each load interval for a specific axle type (steering, single, tandem, and tridem) and vehicle class (Classes 5 through 13). A definition of load intervals for each axle type is provided below: Single (steering Single or other single) – 0-250KN, at 10KN intervals; Tandem – 0 to 500KN at 20KN intervals; Tridem -0 to 750 KN at 30KN intervals. The percentage (decimal) of axles for each axle load group (The frequency of axle load ranges determined in the ALDF), is defined by: pi = Number of axles in load range Total no.of axles in load range of the bound axis of each roadway. (2) ALDF constitute a major change from the current 1993 AASHTO pavement design guide that requires only the total number of 18-Kip ESALs as input. 2.2.1.4 Traffic Growth Factors ADT growth rate was predicted by using the statistically based growth models built on historical trends, whereby, conversion to Linear or Exponential Annual Growth Rates was employed in which the current process uses one growth rate (ADT growth rate) for all truck classes, was determined using Equation 3. r = ( Tf T0 ) ( 1 Yf−Yo ) – 1 (3) where: Yo is the base year, Yf is the future year, To and Tf are the corresponding estimates of ADT, and r is the estimated exponential growth rate (NCHRP 538, 2005) Traffic growth factors are the yearly growth rates for each truck class. The growth function can be either linear growth or compound growth. G is the growth factor: One simple way to project the growth factor is to assume a yearly rate of traffic growth and use the average traffic at the start and end of the design period as the design traffic: G= 1 2 [ 1+( 1 + 𝑟) 𝑌] (4) In which r is the growth rate (decimal) per year (Huang, 2007) 2.3 Axle Load Damage Quantification Factors The key parameter needed in the comparison of the predicted of repetitions for each axle load group depends mainly on how the traffic is being handled. In handling the traffic by the empirically based methods, the equivalent axle load factor (EALF) for each axle load group is being determined by the AASHTO ESAL equation. However, handling the traffic by the M-E methods, it is useful to define a companion summary measure-a general axle load equivalency factor (GAF) is being employed. The key link in this comparison is that the same developed Axle load distribution factor (ALDF) is being utilized in both computations, between the empirical methods and M-E methods. The two detail descriptions are presented below: 2.3.1 Determination of Equivalent Axle Load Factor (EALF) In handling of traffic in empirically based methods, the method of analysis was based on the AASHTO Equivalency Factors in which the AASHTO equations for computing Equivalent Axle Load Factor (EALF) for each of the generated axle load ranges for each axle type as contained Log ( 𝑊𝑡𝑥 𝑊𝑡18 ) = 4.79log (18+1) - 4.79+ - http://www.azojete.com.ng/ mailto:%20edetjoseph1991@gmail.com%09 Arid Zone Journal of Engineering, Technology and Environment, September 2024; Vol. 20(3)581-600. ISSN 1596-2490; e-ISSN 2545- 5818; www.azojete.com.ng Corresponding author’s e-mail address: awosanyaolugbenga2015@gmail.com 586 in the developed axle load distribution factor required in M-E design procedure. The following regression equations based on the results of road tests can be used for determining EALF: = log ( 4.2−𝑃𝑡 4.2−1.5 ) ; 0.4 + 0.082(𝐿𝑥 +𝐿2)3.23 ( 𝑆𝑁+1)5.19 (𝐿2)3.23 (5) Where, Wtx= the number of x-axle load application at the end of time t,; Wt18 = the number of 18kip (80KN) single axle load application to time t, ; Lx = the load in kip on one single axle, one set of tandem axles, or one set of tridem axles,; L2 is the axle code = 0 for steering axle; 1 for single axles, 2 for tandem axles, and 3 for tridem axles (Huang, 2007).; SN = structural number - a function of thickness, modulus of each layer, and drainage condition of base and sub-base.; Pt = terminal serviceability – which indicates the pavement conditions to be considered as failures, = the value of when 𝐿𝑋 = 18 and 𝐿2=1, Hence, Practically, EALF is not very sensitive to pavement thickness and SN equal to 5 may be used for most cases and a Pt value of 2.5 can be used (AASHTO, 1986, 1993 and Huang 2007). 2.3.2 Determination of General Axle Equivalency Factor (GAF) In handling of traffic in M-E methods, the method of analysis was based on the practical comparison of axle load spectra, through which statistical measures are needed that are related to the concept of pavement damage and are independent of pavement –related variables. General axle load equivalency factors (Dahlin1994; AASHTO1993) can fulfil this need. The (GAF) are defined as: W =𝐿𝑖 =Axle load of any type or spacing in pounds (or converted to kN) =Load Li =carried by axle group type i (KN), from the ALDF.: 𝑆𝐿𝑖 = Standard load for axle group type,that has a similar unity damage for each axle type:-Single axle with single tires: SL=53KN; -Single axle with dual tires: SL=80KN=18,000Pounds; -Tandem group with dual tires: SL=135KN; -Tridem group with dual tires: SL=181KN. Any changes in GAFs can be attributed directly to changes in traffic loads that is the focus of this study for the purpose of improving the pavement performance to be determined by NEMPADS (Hajek et al., 2000, Austroads 2004b and 2012; Moffatt et al., 2014). 2.4 Determination of the predicted number of load repetitions. The considered method in this thesis for estimating pavement loading known as Axle Load Spectra for use with the Mechanistic-Empirical (ME) design procedure, whereby method based on different axle load groups is being employed. The procedure for predicting the predicted number of load repetitions for each axle load (for each axle load range) during the design life, is similar to the method based on ESAL, except that each load group is considered separately. Timm and Newcomb, (2002) studied the primary difference between how traffic is handled in empirically based methods versus Mechanistic-Empirical (M-E) Methods. In estimating anticipated traffic loading, whereby, the design is based on the axle load spectra, then the initial number of repetitions per day for the ith load group can be computed from ( 𝑛𝑜)𝑖 = ( 𝑃𝑖𝐹𝑖) (𝐴𝐷𝑇)𝑜 (T) (A) (8) EALF = 𝑊𝑡18 𝑊𝑡𝑥 (6) GAF = ( W 18,000 ) 3.8 = ( 𝐿𝑖 𝑆𝐿𝐼 ) 3.8 (7) file:///C:/user/Downloads/azojete143/www.azojete.com.ng mailto:%20kunleoluyori@gmail.com Awosanya et al: Development of Axle Load Spectra for Integration in Traffic Characteriaztion for Nigerian Empirical Mechanistic Pavement Analysis and Design Systems. AZOJETE, 20(3):581-600. ISSN 1596-2490; e-ISSN 2545-5818, www.azojete.com.ng Corresponding author’s e-mail address: awosanyaolugbenga2015@gmail.com 587 in which Pi is the percentage (decimal) of axles in the ith load group (The frequency of axle load ranges determined in the ALDF), determined according to equation 2 above. The predicted number of load repetitions 𝑛𝑖 for load group i during the design period is 𝑛𝑖 = 365 (𝑛𝑜)𝑖 (G) (D) (L) (Y) (9) in which ( 𝑛𝑜)𝑖 is the initial number of repetitions per day for the ith load group, determined above in Equation 8, G is the growth factor, D is the directional distribution factor, which is usually assumed to be 0.5 unless the traffic in two directions is different, L is the lane distribution factor which varies with the volume of traffic and the number of lanes, and Y is the design period in years. 2.4.1 Predicted number of repetitions in empirically based approach. In handling traffic in empirically based approach, EALF was used as a key axle damage factor parameter in calculating the predicted number of repetitions for the empirical approach, using Equations 8 and 9. 2.4.2 Predicted number of repetitions in mechanistically–empirical methods. In handling traffic in M-E methods, GAF is used as a key axle damage factor variable in calculating the predicted number of repetitions for the empirical approach, using Equations 8 and 9. In these methods, empiricism was being eliminated. 2.4.3 Comparison of Predicted Number of Axle Repetitions Determined by EALF and GAF. A comparison between handling traffic in an empirically based methods and the M-E methods can best be illustrated by the same traffic composition and the method based on different axle load groups, with a marked difference of the axle load damage factor computed for each of the same determined axle load ranges in the ALDF. In this study, there is a need to compare the value of the predicted number of repetitions of each axle load group during the design period, using empirical and Mechanistic-Empirical methods. In handling traffic in empirically based approach, EALF was used as a key axle damage factor parameter in calculating the predicted number of repetitions for the empirical approach. In handling traffic in M-E methods, GAF is used as a key axle damage factor variable in calculating the predicted number of repetitions for the (M-E) methods This indicate the impact of empiricism content in the traffic projection in the axle applications and its effect on the elimination of empiricism content in the traffic projection in the axle applications. 3. Results and Discussion Traffic elastic analysis is performed using the method based on different axle load groups. the different variables discussed in the previous section are considered. the investigation of the resulting estimate of predicted number of load repetitions by making comparison its value on the basis of the determination of EALF and GAF. the following sections discuss the outcomes of these results 3.1 Average number of axles per truck Average number of axles on each of the axis of the roadway as calculated according to Equation 1, are presented in Table 2. From the analysis of results, obtained axle configuration from the surveyed truck configurations on the Kaduna-Zaria roadway. The average number of axles on the southbound axis (Zaria to Kaduna) was 2.66, and on the northbound axis (Kaduna to Zaria) it was 2.71. The results showed that the truck magnitude and the captured countered axles on the northbound axis are greater in number than the weighed truck number on the southbound axis. http://www.azojete.com.ng/ mailto:%20edetjoseph1991@gmail.com%09 Arid Zone Journal of Engineering, Technology and Environment, September 2024; Vol. 20(3)581-600. ISSN 1596-2490; e-ISSN 2545- 5818; www.azojete.com.ng Corresponding author’s e-mail address: awosanyaolugbenga2015@gmail.com 588 Table 2: Average number of axles per truck for Kaduna-Zaria Roadway Kaduna-Zaria Roadway Southbound Axis (SB) Northbound Axis (NB) Axle Configurations No of Axles counted for each BIN No of Axles counted for each BIN SAST 86 99 SADT 68 76 TADT 75 90 TRDT 0 3 Total number of axles counted for each BIN 229 268 Total number of trucks counted 86 99 Average number of axles per truck 229/86= 2.66 268/99= 2.71 3.2 Axle Load Distribution Factor (ALDF) Axle Load Distribution Factor (ALDF) as calculated according to Equation 2, are presented in Table 3. From the analysis of the results, the cumulative for each of the axle type, of the calculated frequencies is 100%. The frequencies of axle ranges is presented in Table 3, with the corresponding axle load ranges for each of the axle configurations. The captured axle load data on the Kaduna-Zaria roadway. It indicates generally that the captured surveyed and weight of the axles on the northbound (NB) axis are greater in magnitude than the weighted axle in the southbound (SB) axis. The reason behind the differentials was that construction and industrial commodities were carried on the NB axis, whereby, in the SBaxis, agricultural products were carried. The number of axles counted for each BIN has the greatest magnitude of axle load magnitude in the NBaxis are higher for SAST, TADT, and SADT respectively, than in the SB axis. For TRDT, not captured on the SBaxis, but very scanty on the NB axis, and the sample space is very minimum. 3.3 Growth Factor and Annual growth rate. The ADT growth rate was calculated according to Equation 3, and the growth factor was calculated according to Equation 4, and are both presented in Table 4. From the analysis of results, obtained traffic volumes ADT values for the year 1983 and 2006, for the Kaduna-Zaria roadway. In percentage terms, the resulting exponential growth rate is 1.74 percent. The use of the obtained growth rate resulted in the growth factor that is 1.21%. The determined growth factor and growth rate were not both affected by the content of empiricism and therefore, had the same impact of the predicted number of axle load repetitions and pavement performance as implemented by Timm and Newcomb (2002). 3.4 Computation of Equivalent Axle Load Factor (EALF) The regression equations based on the results of road tests is used for the determination of EALF for the four different axle types observed on the Kaduna-Zaria roadway. The axle load intervals are determined for each axle types from the result of the secondary data captured on the Kaduna-Zaria Roadway from the work of S.S.I (2007). The determined EALF from the same traffic composition for the four different axle types is presented in Table 5. 3.5 Computation of GAF. General axle load equivalency factors (GAF) is computed from the same traffic composition using the statistical measures that are needed to the concept of pavement damage and are independent of pavement -related variables. The determined GAF is presented for the Kaduna- Zaria roadways in Table 6, for the four different axle types on the considered roadway. file:///C:/user/Downloads/azojete143/www.azojete.com.ng mailto:%20kunleoluyori@gmail.com Awosanya et al: Development of Axle Load Spectra for Integration in Traffic Characteriaztion for Nigerian Empirical Mechanistic Pavement Analysis and Design Systems. AZOJETE, 20(3):581-600. ISSN 1596-2490; e-ISSN 2545-5818, www.azojete.com.ng Corresponding author’s e-mail address: awosanyaolugbenga2015@gmail.com 589 Table 3: Results of the calculated axle load distribution factor (ALDF) for the four different axle configurations Single Axle with Single Tire (SAST) Single Axle with Dual Tires (SADT) Southbound Axis (Sb) Axis Northbound Axis (Nb) Axis Southbound Axis (Sb) Axis Northbound Axis (Nb) Axis S/N BIN Count (No of Axles Counted for Each Bin) for Southbound Axis Percentage of Occurrence (Frequency) %for Sb-Axis Count (No of Axles Counted for Each Bin) for Northbound Axis Percentage of Occurrence (Frequency) % for Nb-Axis S/N Bin Count (No of Axles Counted for Each Bin) for Southbound Axis Percentage of Occurrence (Frequency) % for Sb-Axis Count (No of Axles Counted for Each Bin) for Northbound Axis Percentage of Occurrence (Frequency) %for Nb-Axis 1 0 0 0 0 0 1 0 0 0 0 0 2 10 2 2.33 4 4.04 2 10 1 1.47 1 1.32 3 20 3 3.49 3 3.03 3 20 1 1.47 2 2.63 4 30 9 10.47 8 8.08 4 30 8 11.76 6 7.89 5 40 16 18.60 9 9.09 5 40 31 45.59 6 7.89 6 50 39 45.35 18 18.18 6 50 10 14.71 5 6.58 7 60 3 3.49 32 32.32 7 60 2 2.94 0 0 8 70 8 9.30 15 15.15 8 70 1 1.47 2 2.63 9 80 1 1.16 5 5.05 9 80 5 7.35 1 1.32 10 90 0 0 2 2.02 10 90 1 1.47 4 5.26 11 100 2 2.33 0 0 11 100 2 2.94 6 7.89 12 110 2 2.33 2 2.02 12 110 1 1.47 6 7.89 13 120 1 1.16 1 1.01 13 120 1 1.47 11 14.47 14 130 0 0 0 0 14 130 2 2.94 9 11.84 15 140 0 0 0 0 15 140 1 1.47 2 2.63 16 150 0 0 0 0 16 150 0 0 3 3.95 17 160 0 0 0 0 17 160 0 0 5 6.58 18 170 0 0 0 0 18 170 1 1.47 1 1.32 19 180 0 0 0 0 19 180 0 0 1 1.32 20 190 0 0 0 0 20 190 0 0 1 1.32 21 200 0 0 0 0 21 200 0 0 3 3.95 22 210 0 0 0 0 22 210 0 0 1 1.32 23 220 0 0 0 0 23 220 0 0 0 0 24 230 0 0 0 0 24 230 0 0 0 0 25 240 0 0 0 0 25 240 0 0 0 0 26 250 0 0 0 0 26 250 0 0 0 0 Total Number of Axles Counted 86 100.00 99 100.00 Total Number of Axles Count 68 100.00 76 100.00 http://www.azojete.com.ng/ mailto:%20edetjoseph1991@gmail.com%09 Arid Zone Journal of Engineering, Technology and Environment, September 2024; Vol. 20(3)581-600. ISSN 1596-2490; e-ISSN 2545-5818; www.azojete.com.ng Corresponding author’s e-mail address: awosanyaolugbenga2015@gmail.com 590 TABLE 3 CONT’D: Results of the calculated axle load distribution factor (ALDF) for the four different axle configurations Tandem Axle with Dual Tire (TADT) Tridem Axle with Dual Tires (TDT) Southbound Axis (Sb) Axis Northbound Axis (Nb) Axis Southbound Axis (Sb) Axis Northbound Axis (Nb) Axis S/N BIN Count (No of Axles Counted for Each Bin) for Southbound Axis Percentage of Occurrence (Frequency) % for Sb-Axis Count (No of Axles Counted for Each Bin) for Northbound Axis Percentage of Occurrence (Frequency) % for Nb-Axis S/N Bin Count (No of Axles Counted for Each Bin) for Southbound Axis Percentage of Occurrence (Frequency) % for Sb-Axis Count (No of Axles Counted for Each Bin) For Northbound Axis Percentage of Occurrence (Frequency) % for Nb-Axis 1 0 0 0 1 1.11 1 0 0 0 0 0 2 20 4 5.33 1 1.11 2 30 0 0 0 0 3 40 11 14.67 3 3.33 3 60 0 0 0 0 4 60 31 41.33 6 6.67 4 90 0 0 0 0 5 80 13 17.33 4 4.44 5 120 0 0 0 0 6 100 2 2.67 6 6.67 6 150 0 0 0 0 7 120 2 2.67 4 4.44 7 180 0 0 0 0 8 140 1 1.33 2 2.22 8 210 0 0 0 0 9 160 0 0 7 7.78 9 240 0 0 0 0 10 180 0 0 8 8.89 10 270 0 0 2 66.67 11 200 3 4.00 8 8.89 11 300 0 0 0 0 12 220 2 2.67 15 16.67 12 330 0 0 0 0 13 240 2 2.67 11 12.22 13 360 0 0 0 0 14 260 0 0 1 1.11 14 390 0 0 1 33.33 15 280 0 0 6 6.67 15 420 0 0 0 0 16 300 2 2.67 3 3.33 16 450 0 0 0 0 17 320 1 1.33 1 1.11 17 480 0 0 0 0 18 340 1 1.33 1 1.11 18 510 0 0 0 0 19 360 0 0 0 0 19 540 0 0 0 0 20 380 0 0 2 2.22 20 570 0 0 0 0 21 400 0 0 0 0 21 600 0 0 0 0 22 420 0 0 0 0 22 630 0 0 0 0 23 440 0 0 0 0 23 660 0 0 0 0 24 460 0 0 0 0 24 690 0 0 0 0 25 480 0 0 0 0 25 720 0 0 0 0 26 500 0 0 0 0 26 750 0 0 0 0 Total Number of Axles Counted 75 100.00 90 100.00 Total Number of Axles Count 0 0 3 100.00 file:///C:/user/Downloads/azojete143/www.azojete.com.ng mailto:%20kunleoluyori@gmail.com Awosanya et al: Development of Axle Load Spectra for Integration in Traffic Characteriaztion for Nigerian Empirical Mechanistic Pavement Analysis and Design Systems. AZOJETE, 20(3):581-600. ISSN 1596-2490; e-ISSN 2545-5818, www.azojete.com.ng Corresponding author’s e-mail address: awosanyaolugbenga2015@gmail.com 591 Table 4: Traffic Growth Factors Future ADT Future Year 11,000.00 2006 Base ADT Base Year 7402 1983 Growth rate (r) % Equation 3 Utilized 1.74% Design Period 20Years Growth Factor (G) Equation 4 Utilized 1.21% Table 5: Results of the Determined EALF for each axle load range of the axle load spectrum. S/N Axle Load Range for SAST (kN) EALF For SAST Axle Load Range for SADT (kN) EALF For SADT Axle Load Range for TADT (kN) EALF For TADT Axle Load Range for TRDT (kN) EALF For TRDT 1 0 0.0000 0 0.0000 0 0 0 0.0000 2 10 0.0001 10 0.0003 20 0.0004 30 0.0004 3 20 0.0013 20 0.0033 40 0.0045 60 0.0054 4 30 0.0089 30 0.0169 60 0.0233 90 0.0271 5 40 0.0352 40 0.0562 80 0.0773 120 0.0865 6 50 0.1024 50 0.1441 100 0.1983 150 0.2096 7 60 0.2452 60 0.3095 120 0.4258 180 0.4217 8 70 0.5132 70 0.5843 140 0.8037 210 0.7478 9 80 0.9728 80 1.000 160 1.3755 240 1.2177 10 90 1.7102 90 1.5871 180 2.1832 270 1.8722 11 100 2.8329 100 2.3764 200 3.2689 300 2.7668 12 110 4.4720 110 3.4024 220 4.6801 330 3.9723 13 120 6.7843 120 4.7068 240 6.4744 360 5.5754 14 130 9.9545 130 6.3417 260 8.7232 390 7.6785 15 140 14.1965 140 8.3709 280 11.5145 420 10.4005 16 150 19.7563 150 10.8707 300 14.9531 450 13.8773 17 160 26.9131 160 13.9303 320 19.1618 480 18.2628 18 170 35.9815 170 17.6516 340 24.2905 510 23.7257 19 180 47.3134 180 22.1493 360 30.4673 540 30.4705 20 190 61.2998 190 27.1493 380 37.8983 570 38.6986 21 200 78.3723 200 33.9998 400 46.7682 600 48.6488 22 210 79.0055 210 41.6493 420 57.2904 630 60.5788 23 220 123.7182 220 50.6698 440 69.6985 660 74.7697 24 230 153.0754 230 61.2458 460 84.2462 690 91.5270 25 240 187.6899 240 73.5770 480 101.2082 720 111.1817 26 250 228.2242 250 87.8790 500 120.8813 750 134.0910 http://www.azojete.com.ng/ mailto:%20edetjoseph1991@gmail.com%09 Arid Zone Journal of Engineering, Technology and Environment, September 2024; Vol. 20(3)581-600. ISSN 1596-2490; e-ISSN 2545- 5818; www.azojete.com.ng Corresponding author’s e-mail address: awosanyaolugbenga2015@gmail.com 592 Table 6: Determination of the GAF for each load range of the Axle Load Spectrum 3.6 Determination of the predicted number of load repetitions using EALF Predicted number of axle repetitions utilizing EALF is determined by utilizing the EALF as a primary input using the same traffic composition for the four different axle types in Empirical approach with the empiricism contents not eliminated. This is presented between Figures 1 and Figures 4. Figure 1: Predicted Number of Repetitions using EALF for Single Axle with Single Tire Loads during 20-year Design Period at Kaduna-Zaria Roadway S/ N AXLE LOAD RANGE FOR SAST GAF FOR SAST=(AXLE LOAD RANGE/ 53) ^3.8 AXLE LOAD RANGE FOR SADT GAF FOR SADT=(AXLE LOAD RANGE/ 80)^3. 8 AXLE LOAD RANGE FOR TADT GAF FOR TADT=(AXL E LOAD RANGE/ 80) ^3.8 AXLE LOAD RANGE FOR TRDT GAF FOR TRDT=(AXL E LOAD RANGE/ 80) ^3.8 1 0 - 0 - 0 - 0 - 2 10 0.0018 10 0.0004 20 0.0007 30 0.0011 3 20 0.0246 20 0.0052 40 0.0098 60 0.0151 4 30 0.1150 30 0.0241 60 0.0459 90 0.0703 5 40 0.3432 40 0.0718 80 0.1369 120 0.2098 6 50 0.8014 50 0.1676 100 0.3197 150 0.4897 7 60 1.6022 60 0.3351 120 0.6392 180 0.9792 8 70 2.8782 70 0.6020 140 1.1482 210 1.7590 9 80 4.7807 80 1.0000 160 1.9072 240 2.9216 10 90 7.4795 90 1.5645 180 2.9838 270 4.5709 11 100 11.1622 100 2.3348 200 4.4529 300 6.8215 12 110 16.0341 110 3.3539 220 6.3964 330 9.7988 13 120 22.3172 120 4.6682 240 8.9030 360 13.6386 14 130 30.2508 130 6.3277 260 12.0679 390 18.4870 15 140 40.0902 140 8.3858 280 15.9931 420 24.5001 16 150 52.1073 150 10.8995 300 20.7870 450 31.8441 17 160 66.5898 160 13.9288 320 26.5645 480 40.6947 18 170 83.8412 170 17.5373 340 33.4466 510 51.2375 19 180 104.1807 180 21.7918 360 41.5605 540 63.6675 20 190 127.9427 190 26.7622 380 51.0398 570 78.1890 21 200 155.4768 200 32.5216 400 62.0240 600 95.0159 22 210 187.1479 210 39.1464 420 74.6585 630 114.3709 23 220 223.3356 220 46.7158 440 89.0947 660 136.4861 24 230 264.4341 230 55.3126 460 105.4900 690 161.6025 25 240 310.8525 240 65.0221 480 124.0076 720 189.9699 26 250 363.0143 250 75.9329 500 144.8164 750 221.8473 TABLE 6 : DETERMINATION OF THE GAF FOR EACH LOAD RANGE OF THE AXLE LOAD SPECTRUM. - 1.00 2.00 3.00 4.00 5.00 6.00 7.00 0 1 0 2 0 3 0 4 0 5 0 6 0 7 0 8 0 9 0 1 0 0 1 1 0 1 2 0 1 3 0 1 4 0 1 5 0 1 6 0 1 7 0 1 8 0 1 9 0 2 0 0 2 1 0 2 2 0 2 3 0 2 4 0 2 5 0 LO G A R IT H M O F P R ED IC TE D N U M B ER O F R EP ET IT IO N S INDIVIDUAL AXLE WEIGHTS (kN) SB NB file:///C:/user/Downloads/azojete143/www.azojete.com.ng mailto:%20kunleoluyori@gmail.com Awosanya et al: Development of Axle Load Spectra for Integration in Traffic Characteriaztion for Nigerian Empirical Mechanistic Pavement Analysis and Design Systems. AZOJETE, 20(3):581-600. ISSN 1596-2490; e-ISSN 2545-5818, www.azojete.com.ng Corresponding author’s e-mail address: awosanyaolugbenga2015@gmail.com 593 Figure 2: Predicted Number of Load Repetitions using EALF for Single Axle with Dual Tires Loads during 20year Design Period at Kaduna-Zaria Roadway. Figure 3: Predicted Number of Load Repetitions using EALF for Tandem Axle with Dual Tires Loads during 20-year Design Period at Kaduna -Zaria Roadway Figure 4: Predicted Number of Load Repetitions using EALF for Tridem Axle with Dual Tires Loads during 20-year Design Period at Kaduna-Zaria Roadway. - 1.00 2.00 3.00 4.00 5.00 6.00 7.00 8.00 0 1 0 2 0 3 0 4 0 5 0 6 0 7 0 8 0 9 0 1 0 0 1 1 0 1 2 0 1 3 0 1 4 0 1 5 0 1 6 0 1 7 0 1 8 0 1 9 0 2 0 0 2 1 0 2 2 0 2 3 0 2 4 0 2 5 0 LO G A R IT H M O F P R ED IC TE D N U M B ER O F R EP ET IT IO N S INDIVIDUAL AXLE WEIGTHS (kN) SB - 1.00 2.00 3.00 4.00 5.00 6.00 7.00 8.00 0 2 0 4 0 6 0 8 0 1 0 0 1 2 0 1 4 0 1 6 0 1 8 0 2 0 0 2 2 0 2 4 0 2 6 0 2 8 0 3 0 0 3 2 0 3 4 0 3 6 0 3 8 0 4 0 0 4 2 0 4 4 0 4 6 0 4 8 0 5 0 0 LO G A R IT H M O F P R ED IC TE D N U M B ER O F R EP ET IT IO N S INDIVIDUAL AXLE WEIGTHS ( kN) SB NB 0 1 2 3 4 5 6 7 8 0 3 0 6 0 9 0 1 2 0 1 5 0 1 8 0 2 1 0 2 4 0 2 7 0 3 0 0 3 3 0 3 6 0 3 9 0 4 2 0 4 5 0 4 8 0 5 1 0 5 4 0 5 7 0 6 0 0 6 3 0 6 6 0 6 9 0 7 2 0 7 5 0LO G A R IT H M O F P R ED IC TE D N U M B ER O F R EP ET IT IO N S INDIVIDUAL AXLE WEIGTHS ( kN) SB NB http://www.azojete.com.ng/ mailto:%20edetjoseph1991@gmail.com%09 Arid Zone Journal of Engineering, Technology and Environment, September 2024; Vol. 20(3)581-600. ISSN 1596-2490; e-ISSN 2545- 5818; www.azojete.com.ng Corresponding author’s e-mail address: awosanyaolugbenga2015@gmail.com 594 3.7 Determination of the predicted number of load repetitions using GAF Predicted number of axle repetitions utilizing GAF is determined by utilizing the GAF as a primary input using the same traffic composition for the four different axle types in Mechanistic –Empirical Procedures without the empiricism contents that has been eliminated. This is presented between Figures 5 and Figures 8. Figure 5: Predicted Number of Load Repetitions using GAF for Single Axle with Single Tires Loads during 20year Design Period at Kaduna-Zaria Roadway Figure 6: Predicted Number of Load Repetitions using GAF for Single Axle with Dual Tires Loads during 20-year Design Period at Kaduna -Zaria Roadway Figure 7: Predicted Number of Load Repetitions using GAF for Tandem Axle with Dual Tires Loads during 20-year Design Period at Kaduna-Zaria Roadway - 1.00 2.00 3.00 4.00 5.00 6.00 7.00 8.00 0 1 0 2 0 3 0 4 0 5 0 6 0 7 0 8 0 9 0 1 0 0 1 1 0 1 2 0 1 3 0 1 4 0 1 5 0 1 6 0 1 7 0 1 8 0 1 9 0 2 0 0 2 1 0 2 2 0 2 3 0 2 4 0 2 5 0LO G A R IT H M O F P R ED IC TE D N U M B ER O F R EP ET IT IO N S INDIVIDUAL AXLE WEIGTHS ( kN ) SB NB - 1.00 2.00 3.00 4.00 5.00 6.00 7.00 8.00 0 1 0 2 0 3 0 4 0 5 0 6 0 7 0 8 0 9 0 1 0 0 1 1 0 1 2 0 1 3 0 1 4 0 1 5 0 1 6 0 1 7 0 1 8 0 1 9 0 2 0 0 2 1 0 2 2 0 2 3 0 2 4 0 2 5 0 LO G A R IT H M O F P R ED IC TE D N U M B ER O F R EP ET IT IO N S INDIVIDUAL AXLE WEIGTHS (k N) SB NB - 1.00 2.00 3.00 4.00 5.00 6.00 7.00 8.00 0 2 0 4 0 6 0 8 0 1 0 0 1 2 0 1 4 0 1 6 0 1 8 0 2 0 0 2 2 0 2 4 0 2 6 0 2 8 0 3 0 0 3 2 0 3 4 0 3 6 0 3 8 0 4 0 0 4 2 0 4 4 0 4 6 0 4 8 0 5 0 0 LO G A R IT H M O F P R ED IC TE D N U M B ER O F R EP ET IT IO N S INDIVIDUAL AXLE WEIGTHS ( kN) SB NB file:///C:/user/Downloads/azojete143/www.azojete.com.ng mailto:%20kunleoluyori@gmail.com Awosanya et al: Development of Axle Load Spectra for Integration in Traffic Characteriaztion for Nigerian Empirical Mechanistic Pavement Analysis and Design Systems. AZOJETE, 20(3):581-600. ISSN 1596-2490; e-ISSN 2545-5818, www.azojete.com.ng Corresponding author’s e-mail address: awosanyaolugbenga2015@gmail.com 595 Figure 8: Predicted Number of Load Repetitions using GAF for Tridem Axle with Dual Tires Loads during 20year Design Period at Kaduna -Zaria Roadway 3.8 Comparison of the expected number of repetitions of each axle load group during the pavement life Determined by EALF and GAF. Comparison of the expected number of repetitions of each load group during the pavement life by traffic analysis, whereby, the results of the determined ALDF was utilized and presented in Table 3. The axle load ranges obtained and contained in Table 3, was used in the determination of EALF using Equations 5 and 6, that was presented in Table 5, and the same axle load ranges were used in the calculation of GAF using Equation 7 and presented in Table 6. The differences in the obtained results of EALF and GAF was presented in Table 7 for the axle load ranges for each of the different axle configurations. From the traffic analysis, the method based on different axle load groups was used for the determination of the predicted number of load repetitions by using Equations 8 and 9, and this is presented between Figures 9 and Figures 12. The marked difference is determined by using EALF or GAF individually in Equation 8 respectively, which is an input of Equation 9, and the results were compared. The obtained results indicate the effect of the empiricism content in EALF but that was eliminated in GAF. The projected traffic that was compared was plotted on the four different axle configurations for different axle load ranges was plotted between Figure 9andFigure 12. The compared predicted number of repetitions for load group were presented in Figure 9 to Figure 12. The presentations are presented are as follows: The predicted number of axle load repetitions for the SAST is presented in Figure 9. Similarly, Figure 10 is for SADT; Figure 11 is for Tandem Axles with dual tires and Figure 12 depicts the TRDT. - 1.00 2.00 3.00 4.00 5.00 6.00 7.00 8.00 9.00 0 3 0 6 0 9 0 1 2 0 1 5 0 1 8 0 2 1 0 2 4 0 2 7 0 3 0 0 3 3 0 3 6 0 3 9 0 4 2 0 4 5 0 4 8 0 5 1 0 5 4 0 5 7 0 6 0 0 6 3 0 6 6 0 6 9 0 7 2 0 7 5 0LO G A R IT H M O F P R ED IC TE D N U M B ER O F R EP ET IT IO N S INDIVIDUAL AXLE WEIGTHS ( kN) http://www.azojete.com.ng/ mailto:%20edetjoseph1991@gmail.com%09 Arid Zone Journal of Engineering, Technology and Environment, September 2024; Vol. 20(3)581-600. ISSN 1596-2490; e-ISSN 2545-5818; www.azojete.com.ng Corresponding author’s e-mail address: awosanyaolugbenga2015@gmail.com 596 Table 7: Axle Load Damage Quantification Factors Comparison of EALF and GAF for SAST. Comparison of EALF and GAF FOR SADT. Comparison of EALF and GAF for TADT. Comparison of EALF and GAF FOR TRDT. S/N Axle load range for SAST (kN) EALF for SAST GAF for SAST Axle load range for SADT (kN) EALF for SADT GAF for SADR Axle load range for TADT (kN) EALF for TADT GAF for TADT Axle load range for TRDT (kN) EALF for TRDT GAF for TRDT 1 0 0 0 0 0 0 0 0 0 0 0 0 2 10 0 0 10 0 0 20 0 0 30 0 0 3 20 0.001 0.02 20 0.003 0.01 40 0.005 0.01 60 0.005 0.02 4 30 0.009 0.12 30 0.017 0.02 60 0.023 0.05 90 0.028 0.07 5 40 0.035 0.34 40 0.056 0.07 80 0.077 0.14 120 0.093 0.21 6 50 0.102 0.8 50 0.144 0.17 100 0.198 0.32 150 0.239 0.49 7 60 0.245 1.6 60 0.31 0.34 120 0.426 0.64 180 0.513 0.98 8 70 0.513 2.88 70 0.584 0.6 140 0.804 1.15 210 0.968 1.76 9 80 0.973 4.78 80 1 1 160 1.376 1.91 240 1.658 2.92 10 90 1.71 7.48 90 1.587 1.56 180 2.183 2.98 270 2.631 4.57 11 100 2.833 11.16 100 2.376 2.33 200 3.269 4.45 300 3.939 6.82 12 110 4.472 16.03 110 3.402 3.35 220 4.68 6.4 330 5.64 9.8 13 120 6.784 22.32 120 4.707 4.67 240 6.474 8.9 360 7.807 13.64 14 130 9.954 30.25 130 6.342 6.33 260 8.723 12.07 390 10.512 18.49 15 140 14.197 40.09 140 8.371 8.39 280 11.514 15.99 420 13.875 24.5 16 150 19.76 52.11 150 10.371 10.9 300 14.953 20.79 450 18.019 31.84 17 160 26.913 66.59 160 13.93 13.93 320 19.162 26.56 480 23.091 40.69 18 170 35.982 83.84 170 17.652 17.54 340 24.28 33.45 510 29.259 51.24 19 180 47.313 104.18 180 22.149 21.79 360 30.467 41.56 540 36.714 63.67 20 190 61.3 127.94 190 27.552 26.76 380 37.898 51.04 570 45.669 78.19 21 200 78.372 155.48 200 34 32.52 400 46.768 62.02 600 56.358 95.02 22 210 99.006 187.15 210 41.649 39.15 420 57.29 74.66 630 69.037 114.4 23 220 123.718 223.34 220 50.67 46.72 440 69.698 89.09 660 83.99 136.5 24 230 153.075 264.43 230 61.246 55.31 460 84.246 105.49 690 101.52 161.6 25 240 187.69 310.85 240 73.577 65.02 480 101.208 124.01 720 121.96 190 26 250 228.224 363.01 250 87.879 75.93 500 120.881 144.82 750 145.667 221.9 file:///C:/user/Downloads/azojete143/www.azojete.com.ng mailto:%20kunleoluyori@gmail.com Awosanya et al: Development of Axle Load Spectra for Integration in Traffic Characteriaztion for Nigerian Empirical Mechanistic Pavement Analysis and Design Systems. AZOJETE, 20(3):581-600. ISSN 1596-2490; e-ISSN 2545-5818, www.azojete.com.ng Corresponding author’s e-mail address: awosanyaolugbenga2015@gmail.com 597 Figure 9: Comparison of the Expected Number of Load Repetitions for Single Axle with Single Tires Loads determined by EALF and GAF during 20year Design Period at Kaduna -Zaria Roadway. Figure 10: Comparison of the Expected Number of Load Repetitions for Single Axle with Dual Tires Loads determined by EALF and GAF during 20year Design Period at Kaduna -Zaria Roadway. Figure 11: Comparison of the Expected Number of Load Repetitions for Tandem Axle with Dual Tires Loads determined by EALF and GAF during the 20year Design Period at Kaduna - Zaria Roadway. - 1.00 2.00 3.00 4.00 5.00 6.00 7.00 8.00 0 1 0 3 0 4 0 5 0 6 0 7 0 8 0 9 0 1 0 0 1 1 0 1 2 0 1 3 0 1 4 0 1 5 0 1 6 0 1 7 0 1 8 0 1 9 0 2 0 0 2 1 0 2 2 0 2 3 0 2 4 0 2 5 0 LO G A R IT H M O F P R ED IC TE D N U M B ER O F R EP ET IT IO N S INDIVIDUAL AXLE WEIGTHS ( kN) SB (E ) SB( M-E) NB ( E) NB (M-E) - 1.00 2.00 3.00 4.00 5.00 6.00 7.00 8.00 0 2 0 4 0 6 0 8 0 1 0 0 1 2 0 1 4 0 1 6 0 1 8 0 2 0 0 2 2 0 2 4 0 2 6 0 2 8 0 3 0 0 3 2 0 3 4 0 3 6 0 3 8 0 4 0 0 4 2 0 4 4 0 4 6 0 4 8 0 5 0 0 LO G A R IT H M O F P R ED IC TE D N U M B ER O F R EP ET IT IO N S INDIVIDUAL AXLE WEIGTHS (kN)\ SB ( E ) SB (M-E) N ( E) NB (M-E) - 1.00 2.00 3.00 4.00 5.00 6.00 7.00 8.00 0 1 0 2 0 3 0 4 0 5 0 6 0 7 0 8 0 9 0 1 0 0 1 1 0 1 2 0 1 3 0 1 4 0 1 5 0 1 6 0 1 7 0 1 8 0 1 9 0 2 0 0 2 1 0 2 2 0 2 3 0 2 4 0 2 5 0L O G A R IT H M O F P R E D IC T E D N U M B E R O F R E P E T IT IO N S INDIVIDUAL AXLE WEIGTHS (kN) SB ( E ) SB(M- E) NB ( E) NB(M-E) http://www.azojete.com.ng/ mailto:%20edetjoseph1991@gmail.com%09 Arid Zone Journal of Engineering, Technology and Environment, September 2024; Vol. 20(3)581-600. ISSN 1596-2490; e-ISSN 2545- 5818; www.azojete.com.ng Corresponding author’s e-mail address: awosanyaolugbenga2015@gmail.com 598 Figure 12: Comparison of the Expected Number of Load Repetitions for Tridem Axle with Dual Tires Loads determined by EALF and GAF during the 20year Design Period at Kaduna - Zaria Roadway. 4. Conclusion The following conclusions were drawn from this study: (i) The required traffic loading was characterized for the method based on different axle load groups, whereby, traffic loading was handled in a disaggregate fashion. The load input was in terms of axle load distribution by axle configuration. (ii) Traffic loads were handled through a more complicated process called axle load spectra, in which all traffic loads were analyzed based on vehicle class, axle type, and axle load, and therefore, represented, the true distribution of traffic weights carried by the pavement under consideration. The axle load spectrum from the WIM data contains no empiricism content, therefore, it relied not on the equivalency factors. (iii) The percentage of the total number of truck axle repetitions within each load interval (which varies with axle type) for each axle type was determined from the axle load spectrum of the WIM data, that shows the axle load distribution factors (ALDF). ALDF constitute a major change from the current 1993 AASHTO pavement design guide that requires only the total number of 18-kip ESALs as input. (iv) A unique and rational way of handling volume changes and traffic growth was incorporated in this forecasting process in traffic analysis instead of the traditional approach of growth rate assumption. Careful consideration of current and historical traffic trends and the application of engineering was utilized to obtain an accurate prediction of traffic growth. The growth factor calculated was accurate based on the determined growth rate input and its significance influence on the estimated traffic. The growth factors have the same influence both on the load spectra and the same as using ESALs, because the empiricism content has no significant impact on the estimated traffic. (v) The EALF was determined based on the empirical equations developed from the AASHO Road Test, whereby, the regression equations based on the results of road tests was utilized. However, GAF was calculated for each load range of the axle load spectrum, whereby, the needed statistical measures that are related to the concept of pavement damage and are independent of pavement -related variables. The obtained values from the same traffic composition shows that the biggest different between the AASHTO EALF and the GAF, for the practical range for each load range of the axle load spectrum. (vi) This finding confirms the comparison of the axle load number of repetitions determined by AASHTO EALF and the GAF, because its practical range of difference with the same traffic 0 2 4 6 8 10 0 3 0 6 0 9 0 1 2 0 1 5 0 1 8 0 2 1 0 2 4 0 2 7 0 3 0 0 3 3 0 3 6 0 3 9 0 4 2 0 4 5 0 4 8 0 5 1 0 5 4 0 5 7 0 6 0 0 6 3 0 6 6 0 6 9 0 7 2 0 7 5 0LO G A R IT H M O F P R ED IC TE D N U M B ER O F R EP ET IT IO N S INDIVIDUAL AXLE WEIGTHS ( kN) SB ( E) SB(M-E) NB ( E ) NB (M-E) file:///C:/user/Downloads/azojete143/www.azojete.com.ng mailto:%20kunleoluyori@gmail.com Awosanya et al: Development of Axle Load Spectra for Integration in Traffic Characteriaztion for Nigerian Empirical Mechanistic Pavement Analysis and Design Systems. AZOJETE, 20(3):581-600. ISSN 1596-2490; e-ISSN 2545-5818, www.azojete.com.ng Corresponding author’s e-mail address: awosanyaolugbenga2015@gmail.com 599 composition was the same difference in output based on the primary inputs of the different axle load damage factors determined individually. References AASHTO, 1986. Guide for Design of Pavement Structures; American Association of State Highway and Transportation Officials, Washington, D.C AASHTO, 1993. Guide for Design of Pavement Structures; American Association of State Highway and Transportation Officials, Washington, DC. Priest, AL. and Timm, DH. 2006. Methodology and Calibration of Fatigue Transfer Functions for Mechanistic-Empirical Flexible Pavement Design. Report No. NCAT Report 06-03, National Center for Asphalt Technology, Auburn University, Alabama Olowosulu AT. 2005. A Framework for Mechanistic Empirical Pavement Design for Tropical Climate. 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