Pavement maintenance and rehabilitation prioritization are conducted based on the accessibility of overall measures for evaluating the condition of each section in the pavement network. Regularly, the pavement condition of each section has been evaluated by some common condition indicators. One of the most important indicators is the present serviceability index (PSI) which is adapted to depict the functional performance regarding ride quality. The main aim of this study is to develop a prediction model of ride quality for flexible pavement using the fuzzy logic technique. The data of input variables are extracted from the database of Long-Term Pavement Performance (LTPP). The research involved 36 pavement sections with 319 data samples for pavement networks of different states in the USA. The ride quality measure which is PSI estimated by the AASHTO equation represents the output variable, whereas patching area, cracking length, slope variance, and rut depth are considered input variables. The results showed that the fuzzified model of ride quality prediction has a decent accuracy with a high determination coefficient. In addition, based on the testing results, the developed prediction model showed a strong accuracy to predict the ride quality index
Several different deterministic and probabilistic mathematical approaches have been used to develop modal split models. The data collected by a questionnaire survey approach is frequently associated with subjectivity, imprecision, and ambiguity. additionally, several linguistic terms are used to express some of the transportation planning variables. This can be solved by modeling mode choosing behavior with artificial intelligence techniques such as fuzzy logic. In this research, Ramadi city in Iraq has been selected as a study area. For the purpose of obtaining data, the study area was divided into traffic analysis zones (TAZ). The total number of traffic zones was set as 28 traffic zones, 22 were internal traffic zones and 6 external traffic zones. Field surveys and questionnaires are used to collect data on traffic, land use, and socioeconomic characteristics factors (age, gender, vehicle ownership, family income, trip purpose, trip origin and destination, trip time, waiting duration, duration inside mode, trip origin and destination, trip cost, and type of mode used for transport). The results showed that the modal split models based on the fuzzy inference system can deal with linguistic variables as well as address uncertainty and subjectivity and they gave very good prediction accuracy for future prediction. Fuzzy inference system proved that all factors affected the mode choice with a very strong correlation coefficient (R) equal to 93.1 for general trips but when the results were compared with multiple linear regression model found that the correlation coefficient (R) equal to 28.9 for general trips and the most influential factors on the mode choice are car ownership, age and trip cost. Thus, it can be concluded that fuzzy logic models were more capable of capturing and integrating human knowledge in mode selection behavior. In addition, this study will help decision-makers to plan transportation policies for Ramadi city
In the civil engineering, the prediction of cracks for tunnel lining is too hard because it depends by different factors for example concrete strength, tunnel operation conditions, stress and geological surroundings. The aim of this study is to design a Fuzzy inspect System (FIS) for evaluating the concrete cracks of tunnel lining. Fuzzy logic is a method to signify a type of uncertainty which is understandable for user. The system has been designed to meet permit crack formula that issued in “Highway Tunnel Design Specifications”. When the maximal permit crack width as example is chosen as 0.7mm, 1.2mm and 3.3mm separately the fuzziness set accordingly is Minor , moderate and severe. The average error for the predicted crack (element sample) in FIS is 8.34%. The fuzzy evaluation model is based on the information of a real in-service PESHRAW highway tunnel, which reflects field status. Therefore, this evaluation is comfortable.