ICPT - 2021http://dl.lib.uom.lk/handle/123/201742024-03-29T13:43:03Z2024-03-29T13:43:03ZProceedings of 12th International Conference on Road and Airfield Pavement Technology, 2021 (Pre Text)http://dl.lib.uom.lk/handle/123/202652023-10-13T22:07:50Z2021-01-01T00:00:00ZProceedings of 12th International Conference on Road and Airfield Pavement Technology, 2021 (Pre Text)
Pasindu, HR; Bandara, S; Mampearachchi, WK; Fwa, TF
2021-01-01T00:00:00ZA framework for selecting safety treatments for rural roadsRanawaka, RKTKPasindu, HRDias, TWKIMhttp://dl.lib.uom.lk/handle/123/202642023-10-13T01:50:59Z2021-01-01T00:00:00ZA framework for selecting safety treatments for rural roads
Ranawaka, RKTK; Pasindu, HR; Dias, TWKIM
Pasindu, HR; Bandara, S; Mampearachchi, WK; Fwa, TF
Road safety is a vital element of the road’s overall function, which is
often neglected in decision-making for road maintenance management. As a result,
the safety issues, especially in rural roads, remain without funding to implement the
necessary countermeasures. One constraint faced by local authorities is the lack of
analysis tools to select appropriate safety treatmentswithin the available budget. This
study presents a methodology to logically determine the safety treatment criteria for
a selected road to increase the safety performance at project level. The decisions
regarding the safety treatments are taken based on a linear programming model
which optimizes the safety performance of the selected road. Cumulative Safety
Index (CSI) represents the safety performance of the road, which is determined based
on the prevailing issues on that road. The model comprises the objective function
by which maximizes the safety performance of the selected road concerning the
number of prevailing safety issue types. This model is used to identify the optimal
safety treatment scheme for the road chosen, ensuring prevailing safety issues of
the road are effectively addressed. The objective function consists of the Initial CSI
of the selected road and the safety improvement after treating relevant issue type
coupled with a binary decision variable. This model can also be considered an input to
road asset management systems where multi-objective optimization (MOO) models
maximize the network pavement condition and maximize overall network safety
performance.
2021-01-01T00:00:00ZAnalysis of skidding potential and safe vehicle speeds on wet horizontal pavement curvesPeng, JChu, LFwa, TFhttp://dl.lib.uom.lk/handle/123/202542023-10-13T01:50:30Z2021-01-01T00:00:00ZAnalysis of skidding potential and safe vehicle speeds on wet horizontal pavement curves
Peng, J; Chu, L; Fwa, TF
Pasindu, HR; Bandara, S; Mampearachchi, WK; Fwa, TF
Skidding on wet horizontal pavement curves is a major traffic safety
concern. Themaximum safe driving speed against skidding is an important threshold
for safe driving. However, because of the complex tire-pavement-fluid interaction
mechanism and the large number of variables involved (including curve geometric
parameters, pavement surface properties, properties of tire in motion, and water
film thickness), currently there is no practical working procedure that allows pavement
engineers to determine the maximum safe driving speed on a horizontal curve
under a given wet weather condition. This paper presents a finite element model to
predict the maximum safe driving speed on a wet curved roadway section based on
solid mechanics and hydrodynamics. The numerical simulation model was developed
and validated against experimental skid resistance values on slip angles from
0° to 90°. Based on skidding analysis, the maximum safe driving speed on a horizontal
curve is derived by comparing the available tire-pavement frictional resistance
and the required friction to prevent skidding caused by the centrifugal force of the
vehicle concerned. An illustrative case study is presented to compare the calculated
maximum safe vehicle speed with AASHTO design speed. The analysis presented
suggested that the proposed approach offers a useful tool to calculate maximum safe
speeds on in-service pavement curves for safe driving.
2021-01-01T00:00:00ZAsphalt pavement texture level and distribution uniformity evaluation using three-dimensional methodDong, SHan, Shttp://dl.lib.uom.lk/handle/123/202532023-10-13T01:54:05Z2021-01-01T00:00:00ZAsphalt pavement texture level and distribution uniformity evaluation using three-dimensional method
Dong, S; Han, S
Pasindu, HR; Bandara, S; Mampearachchi, WK; Fwa, TF
To supplement the research on the evaluationmethod of asphalt pavement
texture, novel three-dimensional (3D) methods are proposed. First, pavement textures
were measured in laboratory from asphalt mixture specimens using laser texture
scanner (LTS), and the macro-texture and micro-texture were extracted based on
spectrum analysis techniques. Then, macro-texture level evaluation indices f 8mac
and f 9mac together with micro-texture level evaluation indices f 8mic and f 9mic were
proposed based on the gray level co-occurrence matrix (GLCM) method, and the
hyperparameters existing inGLCMwere discussed. Through the correlation analysis
with mean texture depth (MTD) measured by sand patch method (SPM) and friction
coefficient μ measured by walking friction tester (WFT), the optimum pavement
texture level evaluation indices were determined. Additionally, the evaluation index
σ of distribution uniformity of pavement texture (DUPT) was proposed based on the
uniformity of deviations between sub-surfaces and the average surface of pavement
texture. Finally, the correlations of σ with texture profiles were studied. The results
show that f 8mac and f 8mic are the optimum indices for pavement texture level. MTD
has significant correlation with f 8mac, and the correlation coefficient R is 0.9348;
friction coefficient μ has significant correlation with f 8mic, and the R is 0.8030. The
hyperparameters of GLCM selected in this study were proved effective. Moreover,
the effectiveness of σ is also validated by calibratingwith standard grooved surface. It
can be concluded that the proposed indices in this study are suitable to the evaluation
of pavement texture level and pavement texture distribution.
2021-01-01T00:00:00Z