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Pavement surveying and distress mapping is completed by roadway authorities to quantify the topical and structural damage levels for strategic preventative or rehabilitative action. The failure to time the preventative or rehabilitative action and control distress propagation can lead to severe structural and financial loss of the asset requiring complete reconstruction. Continuous and computer-aided surveying measures not only can eliminate human error when analyzing, identifying, defining, and mapping pavement surface distresses, but also can provide a database of road damage patterns and their locations. The database can be used for timely road repairs to gain the maximum durability of the asphalt and the minimum cost of maintenance. This paper introduces an autonomous surveying scheme to collect, analyze, and map the image-based distress data in real time. A descriptive approach is considered for identifying cracks from collected images using a convolutional neural network (CNN) that classifies several types of cracks. Typically, CNN-based schemes require a relatively large processing power to detect desired objects in images in real time. However, the portability objective of this work requires to utilize low-weight processing units. To that end, the CNN training was optimized by the Bayesian optimization algorithm (BOA) to achieve the maximum accuracy and minimum processing time with minimum neural network layers. First, a database consisting of a diverse population of crack distress types such as longitudinal, transverse, and alligator cracks, photographed at multiple angles, was prepared. Then, the database was used to train a CNN whose hyperparameters were optimized using BOA. Finally, a heuristic algorithm is introduced to process the CNN’s output and produce the crack map. The performance of the classifier and mapping algorithm is examined against still images and videos captured by a drone from cracked pavement. In both instances, the proposed CNN was able to classify the cracks with 97% accuracy. The mapping algorithm is able to map a diverse population of surface cracks patterns in real time at the speed of 11.1 km per hour.

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His area of expertise is in cementitious composites which includes: fracture and fatigue mechanics of quasi-brittle materials, recycled concrete, conductive concrete, reinforced concrete, pervious concrete, geopolymer, and structural dynamics. He currently teaches a wide array of courses that includes statics, reinforced concrete design, structural analysis, and materials engineering. Dr. Brake actively integrates project based and peer assisted learning pedagogies into his curriculum. Dr. Fatih Alperen Adam c American Society for Engineering Education, 2016 Integrating a 3D printer and a truss optimization project in Statics AbstractThere is a need to integrate hands-on engineering analysis and design problems and exciting build technology in lower division engineering courses to stimulate interest and increase students' capacity to solve divergent, open-ended based problems. In this evidence-based practice paper, we discuss the development, execution, and impact of a truss optimization project given to students in Statics. The students were required to optimize two different polyactic acid (PLA) 3D printed trusses using compression and tensile mechanical strength properties. The students were required to provide the optimal tension and compression member cross-section to be 3D printed, provide a three-dimensional drawing using a user-friendly open source CAD software, and provide a summary of the results in a poster presentation. The optimal truss design was then printed, assembled, and tested in the classroom and the results were compared to the engineering design solutions. Students perception of their ability to interpret and analyze data, organize presentations, solve engineering problems, tinker, engineer, and design was assessed using a 21 item online survey. The survey items were taken from both validated instruments and the ABET Criteria 3 course outcomes: a, b, and e. Survey data was collected from the test population (students given the project) and compared to a control population (students not given the project) to assess the impact of the project on engineering self-efficacy. The results indicate there is a statistically significant gain in student engineering skills self-efficacy, student perception of their ability to analyze and interpret data, ability to solve engineering problems, and organize a presentation.