Search results

Current practices for inspecting highway construction predominantly rely on manual processes, which result in subjective assessments, errors and time inefficiencies. The purpose of this study is to address the inefficiencies and potential inaccuracies inherent in manual highway construction inspections. By leveraging computer vision and ontology reasoning, the study seeks an automated and efficient approach to generate structured construction inspection knowledge in the format of checklists for construction activities on highway construction job sites.

This study proposes a four-module framework based on computer vision and ontology reasoning to enable the automatic generation of checklists for quality inspection. The framework includes: (1) the interpretation of construction scenes based on computer vision, (2) the representation of inspection knowledge into structured checklists through specification processing, (3) the connection of construction scenes and inspection knowledge via ontology reasoning and (4) the development of a prototype for the automatic generation of checklists for highway construction.

The proposed framework is implemented across four distinct highway construction scenarios. The case demonstrations show that the framework can interpret construction scenes and link them with relevant inspection knowledge automatically, resulting in the efficient generation of structured checklists. Therefore, the proposed framework indicates considerable potential for application in the automatic generation of inspection knowledge for the quality inspection of highway construction.

The scientific and practical values of this study are: (1) the establishment of a new method that promotes the automated generation of structured inspection knowledge for highway construction by integrating computer vision and ontology reasoning and (2) the development of a novel framework that provides efficient and immediate access to inspection knowledge related to what needs to be inspected at highway construction job sites.

This paper aims to report the effect of titanium oxide (TiO₂) particles on the specific wear rate (SWR) of alkaline treated bamboo and flax fiber-reinforced composites (FRCs) under dry sliding condition by using a robust statistical method.

In this research, the epoxy/bamboo and epoxy/flax composites filled with 0–8 Wt.% TiO₂ particles have been fabricated using simple hand layup techniques, and wear testing of the composite was done in accordance with the ASTM G99-05 standard. The Taguchi design of experiments (DOE) was used to conduct a statistical analysis of experimental wear results. An analysis of variance (ANOVA) was conducted to identify significant control factors affecting SWR under dry sliding conditions. Taguchi prediction model is also developed to verify the correlation between the test parameters and performance output.

The research study reveals that TiO₂ filler particles in the epoxy/bamboo and epoxy/flax composite will improve the tribological properties of the developed composites. Statistical analysis of SWR concludes that normal load is the most influencing factor, followed by sliding distance, Wt.% TiO2 filler and sliding velocity. ANOVA concludes that normal load has the maximum effect of 31.92% and 35.77% and Wt.% of TiO2 filler has the effect of 17.33% and 16.98%, respectively, on the SWR of bamboo and flax FRCs. A fairly good agreement between the Taguchi predictive model and experimental results is obtained.

This research paper attempts to include both TiO₂ filler and bamboo/flax fibers to develop a novel hybrid composite material. TiO₂ micro and nanoparticles are promising filler materials, it helps to enhance the mechanical and tribological properties of the epoxy composites. Taguchi DOE and ANOVA used for statistical analysis serve as guidelines for academicians and practitioners on how to best optimize the control variable with particular reference to natural FRCs.