Determining Average Available Workdays for Roadway Construction Projects Using Long-Term Weather Data—A Case Study for Alabama

Abstract

Construction project durations specified on contracts are influenced by adverse weather conditions such as rainfall and low temperatures. This study aimed to develop an efficient method for determining monthly Average Available Workdays (AAWDs) for roadway construction projects using historical long-term (ten years or more) local weather data. A survey was conducted to understand the status of current practices using weather information for contract time determination by transportation agencies. Excel spreadsheet tools with Visual Basic for Applications (VBA) programs were developed to process the downloaded long-term weather data with two different formats. Instead of manually processing the short-term (e.g., one–three years) weather data, VBA programs efficiently count for weekends, legal holidays, and adverse weather days as non-workdays each month over the years with weather data (>10 years) and then determine the monthly available workdays (AWDs) and AAWDs. This method was verified using daily records from five completed roadway construction projects. Many contractor-claimed non-workdays due to other factors, not weather-related, that contributed to substantially longer project duration affect the comparison of AWDs determined from nearby weather stations using the developed VBA tools. The method and VBA tools developed were applied to 88 weather stations (10–122 years, average 42 years of data) to determine AAWDs in Alabama, USA, as a case study. Monthly AAWDs in Alabama were grouped into three climate zones: North Region, Central Regions, and South Regions with 185, 193, and 200 AAWDs per year, respectively, with more workdays (17–19 days) in warmer months and fewer (9–11 days) in colder months. The determined AAWDs help both DOTs and construction contractors determine/propose reasonable construction project durations and resolve the construction delay issues. The method and VBA tools can be revised/updated by other DOTs and construction companies for different definitions and thresholds on non-workdays and then efficiently determine AWDs and AAWDs using long-term local weather data.

1. Introduction

Roadway construction project contract documents explicitly define the timeline for the completion of all work [1,2]. According to the Federal Highway Administration (FHWA) policy in the USA, each state must establish the necessary documented processes for determining project contract durations [3]. It is important for transportation agencies to establish adequate procedures for determining contract time for roadway projects. The USA FHWA [3] suggests that these procedures should account for geography and climate differences throughout the state and that some types of work may not be undertaken during certain times of the year or may experience a reduction in labor productivity. Contracts should be specific about how delays of all magnitudes will be handled and include threshold values for predictable and unpredictable severe weather impacts, such as heavy rainfalls, hurricanes, and temperature-related conditions like cold weather, snowstorms, and heat waves [4].

As a result, state Departments of Transportation (DOTs) have developed various contract time determination systems (CTDSs) or procedures, such as Texas DOT CTDS, Louisiana CTDS, Kentucky CTDS [2], and Oklahoma CTDS [1,5]. Approximately 15 DOTs have developed and used CTDSs, while 17 DOTs currently rely on engineering experience to determine contract time, and other DOTs use various similar methods [5]. The effect of weather on production rates was considered in the CTDSs developed by the Indiana, Oklahoma, and Wisconsin DOTs [5]. Before reporting production rates, these CTDSs either provide an adjustment factor or solicit user input on adjustment factors applicable to different work items. Recently, there has been an increase in the number of DOTs that recognize weather as a major factor affecting construction project productivity rates [5].

The construction activity itself, the workers performing the activity, and the environment in which the activity is performed are three sources of variability in a task completion time [6]. The accurate forecast of roadway project contract time, including the effect of adverse weather, is crucial for contractors as it allows for the prediction of more realistic durations and costs and helps to minimize conflicts and litigation between transportation agencies and contractors by clearly stating and defining time extensions due to weather delays beyond normal and expected conditions [1,7]. Weather parameters (rainfall, temperature, wind, etc.) and the magnitude of their effect on the project’s duration depend on the geophysical conditions of the project and the type of construction [8]. Rainfall is one of the major uncertainty factors that have adverse impacts on the productivity and duration of roadway construction activities [9]. By determining the impact of weather, complications of assessing time extension disputes and unpredictable costs can be reduced [10]. Contract managers should define how time extensions due to adverse weather are granted in the contracts and differentiate them from other delay-causer factors [4]. However, adverse weather and normal weather delays must be defined by the project scheduler as they may have a different impact on the project duration [10].

The impact of adverse weather conditions, such as rain, is a common cause of construction project delays, legal claims, and economic losses [11]. Engineers managing roadway construction projects should consider the amount, frequency, intensity, and duration of precipitation on various construction operation tasks [12]. However, there is little or no guidance on how to quantify the impact of rain and other adverse weather conditions. Several studies have been conducted to develop progress schedules and the critical path method for calculating contract time [3,13]. An automated decision support system (dubbed WEATHER) was developed by Moselhi et al. [7] to calculate the combined effect of reduced labor productivity and work stoppage caused by adverse weather conditions on building construction sites. Their system is portable and can be used in any city in Canada where weather data are available.

Nguyen et al. [4] list seven factors that need to be considered when accounting for the effect of a weather-related delay on highway projects: (1) definition of normal weather, (2) weather thresholds, (3) type of construction activity, (4) lingering days, (5) criteria for the lost day, (6) lost days equivalent due to loss of productivity, and (7) workdays lost versus calendar days lost. Nagata and Haydt [14] suggested the following approaches to account for lost days due to weather when developing the contract schedule: (a) include non-workdays in the schedule calendars to represent the workdays that might be lost due to adverse weather; (b) increase the durations of weather-sensitive work activities to represent the workdays that might be lost to adverse weather; and (c) add an “adverse weather” activity at the end of a project with a duration that equals the number of workdays that might be lost to adverse weather.

Working days are the most commonly used method of defining contract time [15]. In a 1989 survey, 34% of DOTs allocated construction time using working days, 12% using calendar days, and 14% using completion dates [15]. Weather conditions can impact construction productivity; 60% of state DOTs surveyed included expected weather delays in contract time estimates [15]. “How should a project schedule incorporate workdays that might be lost due to adverse weather?” is a frequently debated question [14]. The critical path method is frequently used to calculate project completion time and is supported by various scheduling software packages such as Microsoft Project, CPM Scheduling Primavera, and others.

Engineers should consider several factors, such as weather, location, soils, traffic, and equipment technology when determining construction contract time and productivity rates [5]. However, there is no general guidance on considering adverse weather conditions in construction operations. Creating project-specific contract time or production rates frequently relies on “rules of thumb” or engineering judgment. The contract time could be over- or underestimated if weather data are not properly analyzed. Overestimation may cause the project to be completed later than expected. Underestimation might result in contractors bidding higher unit prices to accelerate the work.

The WEATHER program [7] performs a statistical analysis of 10 years of historical hourly weather data from the city where the construction project is located to determine productivity factors for construction activities in Canada. Another method for considering weather impacts on construction planning is to analyze historical weather data to determine the Average Available Workdays (AAWDs) each month that construction operations can continue. The Alabama Department of Transportation’s (ALDOT’s) Construction Bureau developed AAWDs using 3–5 years of rainfall data from major cities or airports in Alabama (Huntsville, Birmingham, Tuscaloosa, Montgomery, and Mobile). The number of available workdays (AWDs) in each month was manually counted, excluding weekends, legal holidays, rainy days, and days with cold air temperatures. ALDOT engineers conducted two analyses, one in 1989 and one in 2003. In the 1989 analysis, the state was divided into three zones, each with two to four ALDOT Divisions, whereas the state was divided into four zones in the 2003 study. Each month, AAWDs ranged from 8 to 19 days. Because of the effects of colder weather on paving operations during the winter and spring months, two Divisions (Divisions 1 and 2) in northern Alabama had fewer AAWDs. Despite having more rainfall events, four Divisions (Divisions 6–9) in southern Alabama have slightly higher AAWDs due to warmer temperatures. Between the two studies, monthly AAWDs differ by no more than two days [16].

For the Texas Department of Transportation (TxDOT), Woods et al. [17] proposed a simple regression equation that contractors can use to estimate the number of non-workdays that will occur during any month of a construction project. Monthly precipitation, monthly temperature, number of weekend days, and number of holidays are all input variables in the equation. The necessary input data for calculating non-workdays are easily accessible on the web. Ford et al. [18] suggest that project managers can use daily rainfall datasets during the proposal stage to estimate potential delays and assess the feasibility of the customer’s provided timeline in North Carolina. A step-by-step methodology for predicting rain delays and an analysis of rainfall event probabilities in Asheville, North Carolina, was developed to demonstrate how construction project managers can calculate statistical probabilities of significant rainfall events to forecast delays [18].

The South Dakota Department of Transportation (SDDOT) conducted two comprehensive studies (1997 and 2022) to provide the number of workdays that will be available for each of the South Dakota climate zones and for each type of construction project, i.e., grading, surfacing, and structural projects [3,8]. A total of 103 climate stations with at least 30 years of record were collected and used for analysis. To determine adverse weather days for all construction types across the state, a uniform precipitation threshold of 0.76 cm (0.3 in.) was used. Two temperature thresholds, 0 °C and 4.4 °C (32 °F and 40 °F), were uniformly applied across the state, with the results comparing them. For grading projects only, a precipitation threshold of 1.9 cm (0.75 in.) was used to identify additional adverse weather days. The 80th percentile (not average or median) was a statistical approach used to determine the adverse weather days for all the scenarios. Three types of guiding charts were created for SDDOT to determine days available for construction in a month [8].

In this study, a state-of-practice survey was conducted for all state DOTs in the USA to understand the current practices for using weather data/information to determine available workdays for construction contracts. This study aimed to develop an efficient method for determining monthly AAWDs for roadway construction projects using historical long-term (ten years or more) local climate data (daily rainfall and air temperature). Spreadsheet-based tools with Visual Basic for Applications (VBA) programs were developed to determine monthly AAWDs from long-term weather data in many weather stations (cities). Adverse weather conditions were determined from 15 combinations of daily rainfall and daily mean air temperature thresholds. The method and VBA tools developed were applied to 88 weather stations (10–122 years, average 42 years of data) to determine AAWDs in Alabama, USA, as a case study. It was found that AAWDs can be grouped into three climate zones in Alabama: ALDOT North Region, Central Regions (ALDOT East Central and West Central Regions), and South Regions (ALDOT Southeast and Southwest Regions). The aim of this study is to provide transportation agencies, project managers, construction contractors, and project stakeholders with a more consistent and data-driven tool to accurately/efficiently estimate project durations that can aid in setting more realistic contract times. By utilizing and analyzing long-term historical weather data, this VBA program tool can reduce schedule disputes and support more realistic project scheduling, resource allocation, and risk management in roadway construction projects.

2. Methods and Materials

2.1. DOT’s State-of-Practice Survey

A state-of-practice survey was conducted to assess current practices employed by the US state DOTs in considering adverse weather impacts on the planning and duration determination of roadway projects. The survey was distributed to Directors of the construction division or similar positions at 51 state DOTs in the USA, including the District of Columbia DOT. The respondents provided valuable information regarding current practices and guidelines utilized to evaluate the effects of weather-related parameters. Additionally, the survey explored the methods employed to determine adverse weather conditions or non-workdays for roadway construction projects. One of the purposes of this national survey was to identify the thresholds for adverse weather parameters for non-working days for roadway construction projects. The original survey questionnaires and responses to each question are given elsewhere by Mejia Reyes [16], and useful results of the survey are given in Section 3.1.

2.2. Climate Data for Determining AAWDs

Daily climate data from weather stations across Alabama with at least 10 years of records were downloaded for this study. These data were obtained from two databases in the National Centers for Environmental Information (NCEI) under the National Oceanic and Atmospheric Administration (NOAA): (1) Global Summary of the Day (GSOD) [19] and (2) Global Historical Climatology Network (GHCN) stations [20]. The GSOD [19] was the primary database, and the GHCN [20] was used as a complementary data source to increase the number of representative weather stations. The weather parameters analyzed from the climate stations were daily air temperature (maximum, minimum, and mean at 0.06 °C [0.1 °F] measurement units) and daily precipitation (at 0.025 cm [0.01 in.]). The two databases had different data formats and were processed using the user-developed VBA code in Microsoft Excel. Quality control procedures include checking for missing or inconsistent values. In instances where daily mean air temperature (TEMP) entries were missing, they were calculated as the average of the daily minimum (TMIN) and maximum (TMAX) air temperatures; when TMIN or TMAX was missing, it was counted for one day with missing air temperature data in the GSOD database. In cases when TMIN or TMAX was missing, the observed temperature (TOBS) in the GHCN database was used as a substitute for the mean air temperature. To demonstrate and validate the methodology and spreadsheet tools, the case study site selected was for five ALDOT Regions in Alabama, USA (Figure 1).

The spatial distribution of the initial 105 Alabama weather stations obtained from the GSOD and GHCN databases was reviewed for data completeness and location overlap. Some stations are in the same city/location but a short distance apart, raising the possibility of data duplication. In such cases, the stations were merged into a single station for analysis that combined complete and non-overlapping records from each one. For example, Andalusia station 72227599999 had daily weather data from 1993 to 2005, and Andalusia Municipal Airport Station 72227553843 covered data from 2006 to 2019. These two stations were merged as one station and analyzed to determine monthly AAWDs. In cases of overlap, the weather station with the longest and complete data record after each overlapping/combining location was kept for further data analysis. After data cleaning and station merging, a total of 83 Alabama weather stations were ultimately used to determine AAWDs for five ALDOT Regions (

Table 1. Spatial distribution of 88 weather stations used for the case study to determine AAWDs for five ALDOT Regions.

ALDOT’s Region	Weather Station Locations (States)			Total Stations by Region	Mini/Maxi/Average Years of Data
	Alabama	Georgia	Mississippi
North Region	23 (11, 12) 1	-	-	23	11/122/60
West Central Region	16 (7, 9)	-	3	19	12/116/34
East Central Region	14 (4, 10)	-	-	14	10/92/33
Southeast Region	22 (17, 5)	2	-	24	11/106/39
Southwest Region	8 (6, 2)	-	-	8	10/82/46
Total Stations by State	83 (45, 38)	2	3	88	10/122/42

Note: ¹—The first number is the total number of Alabama stations in each region. Inside brackets, the first and second numbers are the number of GSOD and GHCN stations in each region, respectively. All stations in Georgia and Mississippi are GSOD stations.

), and these are plotted in Figure 1. The spatial distribution of these weather stations from both databases was not evenly distributed amongst the five ALDOT Regions, ranging from 8 to 24 stations (cities) per region, with the Southwest Region being the most deprived. To improve spatial representation, three additional stations from Mississippi and two from Georgia, all with more than ten years of data, were downloaded from the GSOD database and used for further analysis. Therefore, a total of 88 stations were used for the case study (Table 1). The longest weather dataset used was 122 years, from 1901 to 2021, and the average number of years of available data was from 33 to 60 years in five Regions (Table 1). There were 45, 28, and 17 stations with more than 30, 50, and 70 years of weather data to determine AAWDs, respectively.

2.3. Classify Daily Climate Data for Each Station

Excel spreadsheets with VBA programs were developed by the research team and used to automatically/thoroughly/systematically process and categorize weekends, holidays, and adverse weather days due to unfavorable climate conditions based on the long-term daily weather data in each month of each year. For example, the Excel VBA function “Weekday(date, [firstdayofweek])” can classify any date as a weekday (Monday to Sunday). Specifically, the “Weekday (date, 7)” function was used to output any Saturdays as 1, Sundays as 2, Mondays as 3, etc., (e.g., for date 5 January 1965, the weekday function outputs “4”, indicating that it is Tuesday). This process was used to differentiate between workdays and non-workdays for construction projects based on the DOT’s established criteria. In this study, construction workdays were defined as days other than weekends (Saturdays and Sundays), state holidays, and days with adverse weather conditions that are defined using weather threshold conditions (

Table 2. Parameters (rainfall P and air temperature T thresholds) for adverse weather conditions for roadway construction projects.

Parameter No.	Daily Rainfall	Mean Air Temperature
1 or P1	P > 0.0 cm [0.0 in.]	T < −1.1 °C (30 °F)
2 or P2	P > 0.25 cm [0.1 in.]	T < −1.1 °C (30 °F)
3 or P3	P > 0.51 cm [0.2 in.]	T < −1.1 °C (30 °F)
4 or P4	P > 0.76 cm [0.3 in.]	T < −1.1 °C (30 °F)
5 or P5	P > 0.64 cm [0.25 in.]	T < −1.1 °C (30 °F)
6 or P6	P > 0.0 cm [0.0 in.]	T < 1.7 °C (35 °F)
7 or P7	P > 0.25 cm [0.1 in.]	T < 1.7 °C (35 °F)
8 or P8	P > 0.51 cm [0.2 in.]	T < 1.7 °C (35 °F)
9 or P9	P > 0.76 cm [0.3 in.]	T < 1.7 °C (35 °F)
10 or P10	P > 0.64 cm [0.25 in.]	T < 1.7 °C (35 °F)
11 or P11	P > 0.0 cm [0.0 in.]	T < 4.4 °C (40 °F)
12 or P12	P > 0.25 cm [0.1 in.]	T < 4.4 °C (40 °F)
13 or P13	P > 0.51 cm [0.2 in.]	T < 4.4 °C (40 °F)
14 or P14	P > 0.76 cm [0.3 in.]	T < 4.4 °C (40 °F)
15 or P15	P > 0.64 cm [0.25 in.]	T < 4.4 °C (40 °F)

Note: Highlighted P13 was used to derive specific results of the case study in Alabama; however, the spreadsheet tools generate results for all 15 threshold parameters.

).

Adverse weather days were defined as days that had unfavorable weather conditions for contractors to work on or complete certain construction tasks. Legal state’s holidays were categorized into “fixed” and “movable” dates. For “fixed” dates of legal holidays, if the holiday falls on a Saturday, it is observed on the preceding Friday; if it falls on a Sunday, it is observed on the following Monday. For “movable” dates (e.g., the third Monday in January), the holiday is observed on the exact day it falls, with no adjustments. In the case study, 12 or 13 holiday days before or after 2021, including Juneteenth (June 19th), were used in Alabama. The spreadsheet VBA codes developed for this study can be slightly revised to add or remove holidays or use other weather thresholds for automatically determining workdays and non-workdays when applying to different regions and DOTs.

Daily precipitation (P) and mean daily air temperature (T) were the weather parameters evaluated to classify workdays and non-workdays from the daily weather data collected. A total of 15 adverse weather conditions or parameters (P1–P15) evaluated in this study (Table 2) were based on discussions with ALDOT state construction engineers and managers after a literature review and the national survey (see results in Section 3.1). The SDOT 1997 study [8], which is the most relevant and useful study, determined the criteria for classifying non-workdays based on values analyzed from literature reviews, contacting other state DOTs via phone, fax, email, and mail, interviews with construction project contractors and SDOT field engineers, and SDOT field notes from 54 construction projects.

The daily rainfall (P) thresholds used for the study were 0 cm (0 in.), 0.25 cm (0.1 in.), 0.51 cm (0.2 in.), 0.64 cm (0.25 in.), and 0.76 cm (0.3 in.). The daily mean air temperature (T) thresholds were −1.1 °C (30 °F), 1.67 °C (35 °F), and 4.4 °C (40 °F). Therefore, there are a total of 15 combinations of daily rainfall and mean air temperature thresholds for adverse weather conditions, as given in Table 2. These combinations are threshold parameters No. 1 to 15 or P1 to P15. For example, the threshold parameter P1 has P > 0 cm (0 in.) (any amount of rainfall) and T < −1.1 °C (30 °F) as adverse weather conditions. These thresholds can be revised in the VBA codes when applicable to other regions.

The VBA-powered Excel spreadsheets determine non-workdays for construction projects based on all 15 adverse weather conditions in Table 2, and the adverse weather condition or Parameter 13 or P13 was selected to derive the case study results for ALDOT. P13 has daily rainfall P > 0.51 cm (0.2 in.) and daily mean air temperature T < 4.4 °C (40 °F) for adverse conditions for construction projects in Alabama. North Dakota DOT used daily rainfall P > 0.76 cm (0.3 in.) and daily mean air temperature T < 0 °C (32 °F) for adverse conditions [8].

The user-developed VBA code was created to automate the classification of each day in the historical weather dataset as either an available workday (AWD) or non-workday (NWD, with adverse weather conditions, weekends, holidays) for construction projects, and the information is summarized by month, year by year for each processed weather station. Once monthly AWDs are determined based on the available years, a statistical summary of monthly AWDs is generated by VBA code for each weather station processed, and then the average available workday or AAWD is determined using all monthly AWDs over the years with available weather data for all twelve months. To maintain data quality and reliability, the number of days with missing weather data (TotAMiss) and missing rainfall data (TotRainMiss) are also determined each month through the VBA program. If the sum of the two missing data days (TotAMiss + TotRainMiss) for a month is greater than one, the calculated AWD for that month is not used for computing AAWDs. Therefore, the determined AAWDs are unaffected by any missing weather or rainfall data. The VBA program also checked whether the daily mean air temperature data were missing and concluded that there were no missing temperature data for the study. After monthly AAWDs from January to December are determined for each station in a region, the average of the monthly AAWDs for each month for all stations in the region is determined as part of the project results.

3. Results and Discussion

3.1. Survey Results

For the state-of-practice survey, out of the 51 DOTs contacted, a total of 30 DOTs responded, resulting in a survey response rate of 58.8%. A summary of the key findings of the survey is given here. Question Q1 asked about the contract time administration used by DOTs for roadway construction projects. Out of 30 responding DOTs, 25 (83%, all percentages are based on the number of DOTs who responded to the specific question) indicated that they use completion date contracts, which specify a fixed date by which the project must be completed, regardless of the start date. In comparison, 20 (67%) DOTs use calendar-day contracts, which are based on a specified date or notice to proceed and include provisions for adjusting the completion date based on the agreed calendar number of days after notice to start is received. Question Q2 sought to identify construction factors that contribute most to construction project delays. Material shortage, poor project management, and adverse weather conditions were ranked as the top three factors for delays.

Among the 18 DOTs that currently have guidance to determine non-workdays due to inclement weather, 12 (67%) DOTs indicated that state workday weather charts/tools are the guidance used in their agencies, and 7 (39%) DOTs used project manager knowledge/experience to account for non-workdays due to adverse weather when developing the roadway project contract. When asked if the agency has plans to develop any guidance to estimate non-workdays due to adverse weather for roadway project contracts, 11 of the 12 respondents said “No”, while only one state said “Yes”, corresponding to 92% vs. 8%. Most DOT representatives from states without guidance in determining the impact of adverse weather on the duration of roadway project contracts believe that the existing methodologies for estimating contract durations are satisfactory.

To achieve more precise estimations of contract durations while considering inclement weather, six DOTs (33%) relied on guidance that considers climate characteristics of specific geographic areas. Similarly, six respondents (33%) based their guidance on the administrative district/region/area offices of the DOTs. Fourteen DOTs had some general criteria to determine non-workdays: (1) minimum precipitation of 0.25 cm (0.1 in.), (2) daily precipitation of 0.25 cm (0.1 in.) or 0.64 cm (0.25 in.) or 1.3 cm (0.5 in.), and (3) minimum temperature of 0 °C (32 °F) or 0–7.2 °C (32–45 °F). Even though the above information is meaningful/useful, our national survey also did not reveal any directly usable thresholds, which is similar to the SDOT study; their contacts with other state DOTs (equivalent to a survey) did not identify specific thresholds of weather parameters to define the non-working days [8]. The survey participants mentioned using various tools and documentation to verify the non-workdays claimed by contractors. The field engineer diary is the most relied-upon resource, with 16 DOTs (94% out of 17 responses) indicating its usage. Additionally, 12 DOTs (71%) employ weekly progress reports to verify the reported non-workdays due to adverse weather. A total of 15 DOTs, or 88% of the respondents, conduct proactive meetings with contractors to preemptively address delays caused by adverse weather, thereby reducing potential conflicts and legal disputes.

3.2. AAWDs in Alabama

The method presented above and the VBA tools developed during the study were first applied to 88 weather stations (10–122 years, average 42 years of data) in Alabama, USA, to determine AAWDs.

Table 3. Monthly attributes (days or cm) of the daily climate data at Talladega, AL (USC00018024) in 2018.

Month	NWDR2T40	WDayR2T40	TotAMiss	TotRainMiss	RainDay	AvgRain (cm)	AMonRain (cm)	MinRain (cm)	MaxRain (cm)	LRGradR1	LRGradR2	LRGradR3	StInspect
January	23	8	0	0	6	0.53	0.10	0.10	0.89	0	0	0	0
February	15	13	0	0	12	2.16	0.92	0.25	7.87	2	2	2	4
March	16	15	0	0	10	1.00	0.32	0.05	2.49	0	1	1	3
April	12	18	0	0	5	2.62	0.44	1.27	5.99	1	1	1	2
May	13	18	0	0	12	1.15	0.45	0.25	3.43	0	0	0	3
June	15	15	0	0	8	1.20	0.32	0.28	3.43	1	1	1	1
July	13	18	0	0	10	1.28	0.41	0.15	5.33	0	0	0	2
August	13	18	0	0	10	1.38	0.44	0.18	3.94	1	1	1	3
September	14	16	0	0	9	1.25	0.38	0.05	3.81	1	1	1	3
October	11	20	0	0	4	1.21	0.15	0.15	2.54	0	0	0	1
November	22	8	0	1	12	1.77	0.71	0.15	8.51	2	2	2	4
December	20	11	0	0	13	1.77	0.74	0.08	7.37	1	1	2	4
Annual	187	178	0 1	1 2	111 3	N/A	N/A	N/A	N/A	9 4	10 5	11 6	30 7

Note: ¹—TAMiss for total number of days with missing weather data over a year (Annual), ²—TRainMiss for total number of days with missing rainfall data over a year, ³—TRainDay for total number of rainy days over a year, ^4–6—ToLRGradR1, ToLRGradeR2, and ToLRGradR3 for total number of days with large rainfall events (P > 1.9 cm [0.75 in.]) and the next day for a rainy day with rainfall > 0.25 cm (0.1 in.), 0.51 cm (0.2 in.), and 0.76 cm (0.3 in.) over a year, respectively; and ⁷—ToInspect for total number of days for stormwater inspections over a year.

shows an example of the results of classifying daily climate data for Talladega, AL (station number: USC00018024), in 2018. NWDR2T40 and WDayR2T40 are the number of non-workdays (NWs) and workdays (WDays, also called AWDs) in each month for the threshold P13 (R2 stands for P > 0.51 cm [0.2 in.] and T40 stands for T < 4.4 °C [40 °F]). The spreadsheet outputs 45 columns of results for each month, including 30 columns of NW and AWD (columns 2 and 3 in Table 3) results for 15 combinations of thresholds (Table 2), plus other general rainfall characteristics (columns 4 to 15 in Table 3) in addition to station number, year, and month information.

The number of workdays ranges from only 8 days in January and November to 20 days in October 2018 at Talladega. One day with missing rainfall data (TotRainMiss) occurred in November 2018, so 8 days of AWDs in November 2018 were not used to determine AAWDs in November for Talladega to reduce the uncertainty of or increase the accuracy of AAWDs. Monthly rainfall characteristics, e.g., minimum and maximum daily rainfall, average rainfall on rainy days, and average rainfall over the month, are also determined. Large rainfall events (LRGradR1, LRGradR2, and LRGradR3) account for days of rainfall larger than 1.9 cm [0.75 in.] and the next day for a rainy day with rainfall > 0.25 cm (0.1 in.), 0.51 cm (0.2 in.), and 0.76 cm (0.3 in.), respectively. When daily rainfall P > 1.9 cm (0.75 in.), it was classified as a stormwater field inspection for construction projects. It could have 30 stormwater inspections (ToInspect, out of a total of 111 rainy days) in 2018 at Talladega (Table 3), which is connected to the Construction General Permit (CGP) inspection requirements.

Figure 2a shows variations in the monthly non-workdays and workdays from 2018 to 2020 in Talladega. The non-workdays in colder months, November through March, tend to have higher numbers due to the low temperature of the winter in combination with early spring effects (Figure 2a). The workdays ranged from 6 (February 2022) to 21 (July 2019) days in these three years. Figure 2b shows January’s available workdays (AWDs) from 1901 to 2020 and the determined AAWDs at Talladega. The AWDs in January (Figure 2b) range from only 2 days in 1940, 1977, and 1978 to 20 days in 1933. January’s AAWDs are 11 days with a standard deviation (StdDev) of 4 days after processing 199 years of daily weather data.

Table 4. Determined AAWDs and statistical parameters of monthly AWDs for Talladega, AL (USC00018024, 122 years of weather data), using P13 (P > 0.51 cm [0.2 in.] and T < 4.4 °C [40 °F]) threshold.

Month	N-Years	AAWDs	StdDev	Minimum	Maximum	Median	80PerT	Skew
January	110	11	4	2	20	11	14	−0.0646
February	108	11	3	3	18	12	14	−0.3598
March	105	16	2	9	22	17	19	−0.2364
April	109	17	2	10	21	17	19	−0.5260
May	112	18	2	12	21	18	20	−0.7277
June	115	17	2	10	21	17	19	−0.4916
July	115	17	2	10	23	17	19	−0.2535
August	117	18	2	13	22	19	20	−0.4470
September	119	18	2	12	21	18	20	−0.5745
October	113	19	2	14	22	19	20	−0.5080
November	111	15	2	10	21	15	17	−0.1710
December	107	12	3	5	20	12	15	0.0257

Note: StdDev stands for the standard deviation from the mean or average AWDs (i.e., AAWDs). 80PerT stands for the 80th percentile of all monthly AWDs over the years (105–119 years) without missing data.

shows an example of the statistical summary of monthly AWDs, including minimum, maximum, median, average, standard deviation from the average, skewness coefficient, and 80th percentile of AWDs. AAWDs for Talladega, AL, from January to December range from 11 to 19 days, with standard deviations of 2 to 4 days (i.e., StdDev in Table 4). Monthly AWDs’ median values (50th percentiles) are the same as or one day larger than AAWDs. The eighty percentile values (80PerT in Table 4) of monthly AWDs are one to three days larger than the corresponding AAWDs, with larger differences in colder months. In the winter months (December, January, and February), monthly AWDs had a large variation, e.g., from 2 to 20 in January (18 days of difference, more than 3 weeks of available workdays or AWDs), with an AAWD of 11 days. In the past two studies (1989 and 2013), ALDOT engineers used weather data over a few years (about five years, personal communication) to determine AAWDs to represent AAWDs for the stations. This practice is not recommended because of larger variations in the monthly AWDs over the years, especially in the winter months. North Dakota DOT used weather stations containing at least 30 years of climate data to determine available workdays. This study used 10 to 122 years of weather data from 88 stations to determine AAWDs (Table 1), with an average of 42 years of daily weather data.

There are 122 years of weather data in Talladega, but the years used to determine AAWDs range from 105 to 119 years (Table 4) because of excluding months with missing data. The skewness coefficients are mostly negative, meaning more AWDs are larger than AAWDs for those months. Figure 3a shows the AWD distribution in May using a histogram at Talladega, which is a left-skewed distribution (skewness coefficient of −0.737). The AAWDs in May are 18 days, with AWDs from 12 to 21 days, 45 years of AWDs < 18 days, and 54 years of AWDs > 18 days. Figure 3b shows the AWDs’ average (i.e., AAWDs), minimum, maximum, and 80th percentile values in twelve months that indicate a seasonal pattern. There are lower AAWDs in the colder months (11 or 12 days) and not much difference in the warmer months (AAWDs of 17 to 19 days) in Talladega.

Table 5. Average AAWDs and statistical parameters of monthly AAWDs from 14 stations in the ALDOT East Central Region based on threshold condition 13 (Table 1).

Month	AAWDs	StdDev	Minimum	Maximum	Median	80PerT	No. Stations
January	11	2	9	16	10	11	13
February	12	1	10	15	11	12	13
March	17	1	15	18	17	17	13
April	17	0	17	17	17	17	13
May	18	0	18	18	18	18	13
June	17	0	16	17	17	17	13
July	17	0	17	18	17	17	13
August	19	1	17	19	19	19	14
September	18	0	17	18	18	18	14
October	19	0	18	19	19	19	14
November	15	1	14	17	15	15	13
December	13	2	10	17	12	13	13

lists the example statistical parameters of monthly AAWDs in a region, and the numbers are for 14 stations in the ALDOT East Central Region based on threshold condition P13 (Table 2). AAWDs in the second column of Table 5 are the average monthly AAWDs from 13 or 14 stations representing AAWDs for the region. Monthly AAWDs determined from less than 10 years of AWDs were not used for calculating the region’s AAWDs. The ALDOT Southeast Region has 24 weather stations, but up to 6 stations in some months do not have 10 years of AWDs for determining valid AAWDs. In some months, the standard deviations are zero, so AAWDs from these stations are the same or with a one-day difference. In winter months, monthly AAWDs differ up to 7 days, e.g., 9 to 16 days in January and 10 to 17 days in December. The above results on the variations in monthly AAWDs over stations in the East Central Region are the same for the other four regions (Figure 1) [21]. In 1989 and 2013, ALDOT engineers selected/used one weather station (large city) in each region to determine AAWDs to represent AAWDs for the whole region. Based on Table 5 and the results in the other four regions, determining monthly AAWDs from a weather station with long weather data records is acceptable, especially in warmer months, because AAWDs have small variations among weather stations in the same region. It is worth determining the regional AAWDs from more stations for construction projects in colder months due to larger variations (Table 5).

After comparing and examining regional average AAWDs in all five ALDOT Regions, it is recommended that ALDOT’s West Central Region and East Central Region be combined into ALDOT Central Region, and ALDOT Southeast Region and Southwest Region be combined into ALDOT South Region, since the differences between the two regions are small.

Table 6. Monthly Average Available Workdays (AAWDs) in three Alabama climate zones.

Month	North Region	Central Regions	South Regions
January	9 (3, 6–15, 21) 1	11 (3, 6–17, 31)	13 (1, 10–15, 29)
February	10 (2, 8–14, 21)	12 (2, 9–15, 31)	14 (1, 11–15, 28)
March	16 (1, 15–18, 22)	17 (1, 15–18, 31)	18 (1, 16–19, 29)
April	17 (0, 16–17, 22)	17 (0, 16–18, 31)	17 (0, 17–18, 28)
May	18 (0, 17–18, 22)	18 (0, 17–19, 31)	18 (0, 17–19, 30)
June	17 (1, 16–18, 21)	17 (0, 16–18, 31)	17 (0, 16–18, 28)
July	18 (0, 17–18, 22)	17 (0, 16–18, 31)	17 (1, 16–18, 27)
August	19 (0, 17–18, 23)	19 (1, 17–19, 32)	18 (1, 17–19, 26)
September	18 (0, 17–19, 23)	18 (0, 17–18, 32)	18 (0, 17–18, 29)
October	18 (1, 18–20, 23)	19 (0, 18–20, 31)	19 (0, 19–20, 30)
November	14 (1, 13–16, 23)	15 (1, 13–17, 31)	16 (1, 15–17, 31)
December	11 (2, 9–16, 21)	13 (2, 9–17, 31)	15 (1, 12–17, 29)
Annual AAWDs	185	193	200
% of 365 days	51%	53%	55%

Note: ¹—the first number is the regional average AAWD for the month. Inside the brackets, the first number is the standard deviation of the average AAWD, the second and third numbers are the minimum and maximum AAWDs out of the stations, and the fourth number is the number of weather stations in the region for the month with 10 years or more of AWDs to determine valid AAWDs.

shows the summary results for monthly Average Available Workdays or AAWDs in three Alabama climate zones: North, Central, and South Regions. Table 6 lists the monthly AAWDs in the three climate zones from January to December, including the standard deviations, ranges (minimum to maximum), and number of stations used inside the brackets. For example, January’s AAWD is 13 days in the South Regions and ranges from 10 to 15 days with a standard deviation of 1 day from 21 stations. Again, variations in monthly AAWDs are small in warmer months and slightly larger in the colder months (December, January, and February). Annual AAWDs are 185, 193, and 200 days for the North, Central, and South Alabama Regions, 51% to 55% of 365 days. These annual AAWDs are eight (Divisions 1 and 2), five, or seven (Divisions 3 to 5) and two to five (Divisions 6 to 9) more days when compared with AAWDs from the ALDOT 1998 and 2003 studies [16].

To understand variations in AWDs in each month, a statistical summary (minimum, maximum, average) of the minimum and maximum AWDs and the AWD differences (maximum minus minimum AWDs) out of all stations in each ALDOT climate zone was determined. Figure 4 shows the statistical summary results for the North Region, and similar results for the other two climate zones are given in a report by Mejia Reyes et al. [21]. January’s minimum AWDs range from 1 to 5 days, and the maximum AWDs range from 9 to 21 days (Figure 4a) out of 21 weather stations (Table 6) in the North Region. The maximum and minimum AWDs differ from 6 to 19 days in January and 4 to 11 days in July (Figure 4b). There are smaller variations in the warmer months and more in the winter months. The minimum difference (MinDiff in Figure 4b) typically occurs in those stations with 10–25 years of AWD data to determine AAWDs (smaller variations with fewer years of weather data). For example, the Central Regions have 33 weather stations (19 + 14, Table 1) and 31 or 32 stations with 10 or more years of AWDs to determine monthly AAWDs. In January, 2 stations had AWDs for only 9 years (not used for AAWD determination), 8 stations had AWDs from 10 to 21 years (where MinDiff occurs), and the other 23 stations had AWDs for 28 to 110 years with an average of 45 years of AWDs to compute AAWDs, which indicates that long periods of weather data have been used to determine representative and reliable AAWDs in this study.

The results from Figure 2, Figure 3 and Figure 4 and Table 3, Table 4, Table 5 and Table 6 were derived using the adverse threshold parameter P13 (P > 0.51 cm [0.2 in.] and T < 4.4 °C [40 °F]) to specify non-workdays for construction projects. The results for the other 14 adverse threshold parameters (Table 2) were also derived from the VBA programs. Figure 5 shows the results of a sensitivity analysis: how AAWDs are affected by the rainfall thresholds (P > 0.0 cm [0.0 in.], 0.25 cm [0.1 in.], 0.51 cm [0.2 in.], 0.64 cm [0.25 in.], and 0.76 cm [0.3 in.]) when the daily mean temperature threshold was fixed as T < 4.4 °C (40 °F). The AAWDs for all regions consistently increased as the precipitation threshold increased, since a higher precipitation threshold leads to fewer adverse weather conditions (non-workdays). Additionally, a similar curve pattern emerged, reflecting the impact of seasonal changes among all ALDOT Regions: more AAWDs in the summer months and fewer in the winter months. Figure 5 is for the ALDOT North Region, and similar results for the other regions were developed and presented by Mejia Reyes [16]. Also, larger AAWDs with lower temperature thresholds across all regions were observed in the colder months (November, December, January to March) when fixing the precipitation threshold >0.51 cm (0.2 in.) [16], but there was no influence on AAWDs in the summer months since daily mean temperatures are greater than 4.4 °C (40 °F).

3.3. Verification Using Completed Construction Projects

To validate and ensure the accuracy of the spreadsheet tools (with all VBA codes) created to determine AAWDs due to adverse weather for roadway projects, project daily records from five completed representative ALDOT projects, each from a different ALDOT Region, were processed. For each completed project, the ALDOT representative provided the research team with the following essential information: (1) project information, (2) contract information, (3) key project/construction dates, (4) daily work report, and (5) time charges summary. The weather station closest to the project location was selected for verification based on the project information. The calculated AWDs and non-workdays (NWDs) from the weather station were compared to the recorded AWDs and NWDs from the project’s records based on what was claimed and/or reported by the contractor to ALDOT. Analyzing any discrepancies between calculated and recorded AWDs and NWDs data was to determine whether calibration adjustments may be required.

As an illustration, typical verification results are presented for a completed project in the North Region (DeKalb County, AL). This was a construction project for the replacement of the grade, drain, pave, and retaining wall of the bridge located at SR-117 over the west fork of the Little River in Mentone, AL. The project extended from January 2018 through to August 2021 for about 1320 days (~3.5 yrs), but the contract time was only 170 workdays. The closest station “Valley Head 1 SSW–USW00063862” (4.0 km [2.5 mi] from the project site) with available daily data from 2007 to 2022 was used for verification. Throughout the project’s duration, from the project’s daily records, a total of 611 days were claimed as non-workdays due to factors related to the study criteria (adverse weather: 131 rainy days, 44 wet days, 31 cold days, 445 weekends and state holidays); there were 223 workdays, and 487 days were considered as non-workdays due to other factors non-related to the study (or weather) criteria, such as utility coordination, punch items, waiting for final inspection, department actions, and others. Based on the data of the closest weather station processed using weather condition P13 (P > 0.51 cm [0.2 in.] and T < 4.4 °C [40 °F]), a total of 690 days were classified as non-workdays and 631 days as AWDs [16]. When comparing the results of USW00063862 versus the project’s daily records, there is a difference of 79 days for the study-defined non-workdays (+13% from climate data) and a difference of 408 AWDs (+183%). The large difference in AWDs is because the contractor or project claimed 487 non-workdays due to other factors. For these 487 days, the project daily record from the contractor did not provide any weather information about rain, wet conditions, or cold temperatures. This was reflected in the large difference between 131 project rainy days and 362 rainy days from the closest station. For those 131 rainy days in the project record, the closest station reported 119 days with rainfall, with 12 days of discrepancy. The distance between the project site and the closest station is 4.0 km (2.5 mi). The non-uniformity of rainfall spatial distribution may explain this difference.

The project record did not report rainfall depth or air temperature but only stated the rain, wet, and cold conditions for 131 non-workdays. There were 119 days with rainfall at the closest station, 18 days with daily rainfall P < 0.25 cm (0.1 in.), 16 days with 0.25 cm (0.1 in.) ≤ P < 0.51 cm (0.2 in.), 3 days with 0.51 cm (0.2 in.) ≤ P < 0.64 cm (0.25 in.), 3 days with 0.64 cm (0.25 in.) ≤ P < 0.76 cm (0.3 in.), and 79 days with P > 0.76 cm (0.3 in.); therefore, it seemed that the contractor used P > 0 cm (0 in.) to classify the rainy days, which is the current ALDOT adverse weather threshold for rainfall. If we used the P > 0.51 cm (0.2 in.) threshold, only 85 of 119 days could be classified as non-workdays. When the daily rainfall is greater than 1.9 cm (0.75 in.), the following day can be considered a wet day or non-workday for construction projects. The project record reported 44 wet days, and the closest station had 45 wet days for 175 project’s rainy/wet days. However, over the whole project period, the closest station had 436 rainy/wet days; again, the discrepancy is 487 non-workdays due to other factors without climate information. In summary, the large number of non-workdays due to other factors (not weather-related) affected not only the project completion date but also the comparison between calculated AAWDs based on rainfall and temperature thresholds and the project records. This is true for all five completed projects examined in the verification process.

4. Summary and Conclusions

Excel spreadsheets with VBA codes were developed to determine monthly AAWDs using historical long-term daily weather data. Long-term weather data from NOAA’s two databases, (1) Global Summary of the Day and (2) Global Historical Climatology Network with different formats, were processed. Non-workdays for construction projects include weekends, state legal holidays, and adverse weather days, which were determined from 15 combinations of daily rainfall and daily mean air temperature thresholds. Monthly available workdays or AWDs were first determined each month in each year for each station, and monthly AAWDs were determined as average AWDs for each station over years with valid data and then each ALDOT Region using all stations within a region. For the case study, the method and tools were applied to five ALDOT Regions with 88 weather stations (cities). The maximum number of years of weather data used was 122 at Talladega (East Central Region). There were 23, 19, 14, 8, and 24 stations used to determine AAWDs for the ALDOT North Region, West Central Region, East Central Region, Southwest Region, and Southeast Region, respectively. The following conclusions were drawn up from this case study:

(1)

Based on the state-of-practice survey (30 DOTs responded), DOTs primarily used the completion date and calendar-day contracts. The construction type is a critical factor in determining project duration. While 60% of the responding states have guidelines (like tools, charts, or contract language) for identifying non-workdays due to adverse weather, 40% lack such guidance. Material shortage, poor project management, and adverse weather conditions are among the top three contributors to delays in DOT projects.

(2)

A more robust and easily updatable method was developed and implemented in Excel spreadsheets by leveraging long-term (10–122 years) local climate data and advanced data processing techniques with VBA to determine AAWDs for highway construction projects. Monthly regional AAWDs can be grouped into three climate zones in Alabama: ALDOT North Region, Central Regions (East Central and West Central Regions), and South Regions (Southeast and Southwest Regions) with annual AWWDs of 185, 193, and 200 days, respectively (Table 6). Monthly AAWDs with a seasonal pattern range from 9 days in January in the North Region to a maximum of 19 days in August to October in each ALDOT Region.

(3)

The standard deviations of average AAWDs (Table 6) from all stations in a region or zone were low and ranged from zero to three days. The warmer months (April to October) had almost the same AAWDs because of zero or one day for standard deviations, but winter months had larger variations. The minimum and maximum AAWDs only differed by one or two days from April to October but up to seven days in January and December. This means that the AAWDs in the summer/fall months can be determined from one station (e.g., with a long data record and few missing data) in the region, which is what ALDOT did in two previous studies (one representative station for each region). For the winter months, it is necessary to use local weather data to determine AAWDs.

(4)

The maximum difference in AWDs over the available years in summer is 13 days (2.5 weeks) and 20 days (4 weeks) in the winter months. Therefore, monthly AWDs can vary significantly from year to year, depending on precipitation (wet or drought days) and air temperature (hot or cold days) conditions at the study site. Therefore, the use of Excel-based tools developed for this study is recommended to determine monthly AWDs during the project period for construction contract planning/management, especially for the winter months, using long-term climate data from a nearby weather station. Guidance, electronic data files, and training videos are provided for future applications of the developed tools [21].

(5)

A verification process was conducted and completed to confirm and ensure the accuracy of the tools created for AAWDs’ determination. The large number of non-workdays due to other factors (not weather-related, such as utility coordination, punch items, waiting for final inspection, department actions, etc.) affected not only the project completion date (e.g., a contract with 170 workdays took 1320 calendar days to complete) but also the comparison between calculated AAWDs based on rainfall and temperature thresholds and the project records.

Failure to accurately account for weather-related non-workdays during the pre-construction phase of a project can result in unrealistic contract durations, project delays, and increased conflicts between contractors and DOTs, often leading to claims and legal challenges. Providing a data-driven, long-term climate-based and efficient tool for determining AAWDs can help DOTs and construction management agencies to understand and quantify adverse weather days or non-workdays for more reliable construction project planning, better planning for resource allocation, and aid in minimizing claims due to weather-related delays.