The International Roughness Index (IRI) has become the reference scale for assessing pavement roughness in many highway agencies worldwide. This research aims to develop two Artificial Neural Network (ANN) models for Double Bituminous Surface Treatment (DBST) and Asphalt Concrete (AC) pavement sections using Laos Pavement Management System (PMS) database for National Road Network (NRN). The final database consisted of 269 and 122 observations covering 1850 km of DBST NRN and 718 km of AC NRN, respectively. The proposed models predict IRI as a function of pavement age and Cumulative Equivalent Single-Axle Load (CESAL). The obtained data were randomly divided into training (70%), validation (15%), and testing (15%) datasets. The statistical evaluation results of the training dataset reveal that both ANN models (DBST and AC) have good prediction ability with high values of coefficient of determination (R2 = 0.96 and 0.94) and low values of Mean Absolute Error (MAE = 0.23 and 0.19) and Mean Squared Percentage Error (RMSPE = 7.03 and 9.98). Eventually, the goodness of fit of the proposed ANN models was compared with the Multiple Linear Regression (MLR) models previously developed under the same conditions. The results show that ANN models yielded higher prediction accuracy than MLR models.
Laos Pavement Management System (PMS) manages 7700 km of National Roads (NRs) and estimates their Maintenance and Rehabilitation (MR) needs based on assessing pavement roughness conditions. This research aims to develop two International Roughness Index (IRI) models for Double Bituminous Surface Treatment (DBST) and Asphalt Concrete (AC) pavement sections using Adaptive Neuro-Fuzzy Inference System (ANFIS). A historical database of 14 years was employed for predicting the IRI as a function of pavement age and Cumulative Equivalent Single-Axle Load (CESAL). The optimum ANFIS structure comprises a hybrid learning algorithm with six fuzzy rules of generalized bell curve membership functions (Gbellmf) for the DBST model and nine fuzzy rules of two-sided Gaussian membership functions (Gauss2mf) for the AC model. Both models used the constant membership function for the output variable (IRI). The statistical evaluation results revealed that both ANFIS models (DBST and AC) have a good prediction capacity with high values of coefficient of determination (R2 0.93 and 0.88) and low values of Mean Absolute Error (MAE 0.28 and 0.27) and Root Mean Squared Percentage Error (RMSPE 7.03 and 9.98). In addition, results revealed that ANFIS models yielded higher prediction accuracy than Multiple Linear Regression (MLR) models previously developed under the same conditions.
Aggregate size and its source are crucial for concrete properties. This paper presents the evaluation of the properties of self-compacting concrete (SCC) which mixed with coarse aggregate from different sources. Coarse aggregate from three sources which are commonly available in Vientiane Capital, Mekong River (MK), Ngum River (NG) and Crushed Mountain Stone (MT) were used for this study. Six different mixed proportions with two maximum aggregate size 19mm and 12.5mm for each aggregate source were prepared. Ordinary Portland Cement Type I and Fly ash class C were used as binders. The replacement ratio of fly ash to cement was fixed at 20% and w/p ratio was 0.36. Sika Viscocrete-3180MR was used as water reducing admixture with 0.95% by weight of binders. The results show that, for the fresh concrete properties, the coarse aggregate with maximum size of 12.5mm from Mekong River is the most suitable aggregate comparing to other sources. For the hardened concrete with 28-day curing, the highest compressive strength is the mixed proportion MK12 for aggregate size 12.5mm. As seen, the coarse aggregate with 12.5mm of Mekong River is the most appropriate aggregate among three sources which has a flow-ability and higher compressive strength.
The fatigue damage analysis of welded structural members based on continuum damage mechanics is carried out. For high cycle fatigue, plastic deformation and damage occurs at the micro-scale and it is difficult to evaluate it by the macro behavior. Therefore, a two-scale model presented by Lemaitre is introduced to evaluate the damage evolution. In this study, to determine the material parameters for damage evolution at the micro-scale, the identification method is proposed, and confirmed the validity of this procedure. In order to consider the effect of residual stress on fatigue behavior of welded joints, the inherent strain method is also applied to this analysis. It is confirmed that the proposed method could give the reliable fatigue lifetime of welded structural members.
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