Effect of Proportion of Missing Data on Application of Data Imputation in Pavement Management Systems

Missing data are commonly found in pavement condition/performance databases. A common practice today is to apply statistical imputation methods to replace the missing data with imputed values. It is thus important for pavement management decision makers to know the uncertainty and errors involved in the use of datasets with imputed values in their analysis. An equally important information of practical significance is the maximum allowable proportion of missing data (i.e. level of data missingness in the pavement condition/performance records) that will still produce results with acceptable magnitude of error or risk when using imputed data. This paper proposes a procedure for determining such useful information. A numerical example analyzing pavement roughness data is presented to demonstrate the procedure through evaluating the error and reliability characteristics of imputed data. The roughness data of three road sections were obtained from the LTPP database. From these data records, datasets with different proportions of missing data were randomly generated to study the effect of level of data missingness. The analysis shows that the errors of imputed data increased with the level of data missingness, and their magnitudes are significantly affected by the effect of pavement rehabilitation. On the application of data imputation in PMS, the study suggests that at 95% confidence level, 25% of missing data appears to be a reasonable allowable maximum limit for analyzing pavement roughness time series data not involving rehabilitation within the analysis period. When pavement rehabilitation occurs within the analysis period, the maximum proportion of imputed data should be limited to 15%

Modifying effect of dual antiplatelet therapy on incidence of stent thrombosis according to implanted drug-eluting stent type

Aim To investigate the putative modifying effect of dual antiplatelet therapy (DAPT) use on the incidence of stent thrombosis at 3 years in patients randomized to Endeavor zotarolimus-eluting stent (E-ZES) or Cypher sirolimus-eluting stent (C-SES). Methods and results Of 8709 patients in PROTECT, 4357 were randomized to E-ZES and 4352 to C-SES. Aspirin was to be given indefinitely, and clopidogrel/ticlopidine for ≥3 months or up to 12 months after implantation. Main outcome measures were definite or probable stent thrombosis at 3 years. Multivariable Cox regression analysis was applied, with stent type, DAPT, and their interaction as the main outcome determinants. Dual antiplatelet therapy adherence remained the same in the E-ZES and C-SES groups (79.6% at 1 year, 32.8% at 2 years, and 21.6% at 3 years). We observed a statistically significant (P = 0.0052) heterogeneity in treatment effect of stent type in relation to DAPT. In the absence of DAPT, stent thrombosis was lower with E-ZES vs. C-SES (adjusted hazard ratio 0.38, 95% confidence interval 0.19, 0.75; P = 0.0056). In the presence of DAPT, no difference was found (1.18; 0.79, 1.77; P = 0.43). Conclusion A strong interaction was observed between drug-eluting stent type and DAPT use, most likely prompted by the vascular healing response induced by the implanted DES system. These results suggest that the incidence of stent thrombosis in DES trials should not be evaluated independently of DAPT use, and the optimal duration of DAPT will likely depend upon stent type (Clinicaltrials.gov number NCT00476957

Influence of pavement materials on the thermal environment of outdoor spaces

Building and Environment273289-295BUEN

An ANN model to correlate roughness and structural performance in asphalt pavements

In this paper, using a large database from the Long Term Pavement Performance program, the authors developed an Artificial Neural Network (ANN) to estimate the structural performance of asphalt pave- ments from roughness data. Considering advantages of modern high-performance survey devices in the acquisition of road pavement functional parameters, it would be of practical significance if the struc- tural state of a pavement could be estimated from its functional conditions. To differentiate various road section conditions, several significant input parameters, related to traffic, weather, and structural aspects, have been included in the analysis. The results are very interesting and prove that the ANN represents an adequate model to evidence this relation. The papers shows the effectiveness of the adoption of a large database for the analysis of the correlation. ANN provides also better results in comparison with Linear Regression. Further, the authors trained three different ANNs to analyse the effects of modified datasets and different variables. The numerical outcomes confirm that, by using this approach, it is possible to cor- relate with good accuracy roughness and structural performance, allowing road agencies to actually reduce the deflection test frequency, since they are generally more costly, time consuming, and disrup- tive to traffic than functional surveys

Clogging evaluation of permeable bases

10.1061/(ASCE)0733-947X(2003)129:3(309)Journal of Transportation Engineering1293309-31