journal article·2025-09-09Open access

Enhanced Expert Assessment of Asphalt-Layer Parameters Using the CIBRO Method: Implications for Pavement Quality and Monetary Deductions

Henrikas Sivilevičius, Ovidijus Šernas, Judita Škulteckė, Audrius Vaitkus, Rafal Mickevič, Laura Žalimienė

Applied Sciences· Volume 15· Issue 18

Abstract

Each layer of the constructed asphalt pavement is evaluated by measuring its quality indicators, as specified in the construction regulations ĮT ASFALTAS 08, and comparing the obtained values with the corresponding design or threshold values. Due to inherent variability in material properties and systematic or random errors during the production, transport, and installation of the asphalt mixture, the quality indicators of the asphalt layers often deviate from their optimal values. When deviations exceed permissible deviations (PD) or limit values (LV), monetary deductions (MDs) are applied. This study presents normalised values and variation dynamics for 10 quality indicators of the asphalt layer subject to MDs in Lithuania. Using the expertise of 71 road construction professionals and multi-criteria decision-making (MCDM) methods, the influence of these deviations on road quality was assessed. The experts ranked all indicators using percentage weights and the Analytic Hierarchy Process (AHP) method. Expert consensus was verified using concordance coefficients and consistency ratios. After eight statistical outliers were excluded, adjusted weights were calculated based on responses from 63 experts. The proposed method, termed CIBRO (Criteria Importance But Rejected Outliers), enables the objective prioritisation of asphalt quality indicators. The CIBRO method enhances expert concordance and results reliability by aligning criterion ranks with the normal distribution, complementing the Kendall rank correlation approach. The findings highlight that insufficient compaction, inadequate layer thickness, and binder content deviations are the most influential factors that affect layer quality. In contrast, deviations in pavement width, friction coefficient, and surface evenness (measured with a 3 m straight edge) were found to have a lesser impact. The CIBRO method offers a robust approach to assessing the importance of the quality of the asphalt layer, supporting improvements in construction standards and pavement assessment systems.
Source: crossref · Quality: baseline

Research context

OpenAlex topics: Asphalt Pavement Performance Evaluation · Infrastructure Maintenance and Monitoring · Material Properties and Failure Mechanisms

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

Affiliations

Civil Engineering Research Centre, Vilnius Gediminas Technical University, Sauletekio al. 11, LT-10223 Vilnius, Lithuania

Road Research Institute, Vilnius Gediminas Technical University, Linkmenu Street 28, LT-08217 Vilnius, Lithuania

Funding

Civil Engineering Research Centre

References (70)

Show source-provided references
1. Sivilevičius, H., and Žuraulis, V. (2025). Modelling the impact of interaction factors for transport system elements on quality of life using multi-criteria decission-making and applied statistical methods. Sustainability, 17.10.3390/su17051784 ↗
2. Stakeholder-sensitive social welfare-oriented benefit analysis for sustainable infrastructure project development10.1061/(ASCE)CO.1943-7862.0000788 ↗
3. Modelling and significance assessment of road construction participant and user benefits using expert evaluation methods10.3846/tede.2024.21881 ↗
4. Improving asphalt paving productivity10.3141/1575-04 ↗
5. Milad, A., Babalghaith, A.M., Al-Sabaeei, A.M., Dulaimi, A., Ali, A., Reddy, S.S., Bilema, M., and Yusoff, N.I.M. (2022). A Comparative Review of Hot and Warm Mix Asphalt Technologies from Environmental and Economic Perspectives: Towards a Sustainable Asphalt Pavement. Int. J. Environ. Res. Public Health, 19.10.3390/ijerph192214863 ↗
6. Liu, Q., and Oeser, M. (2021). The Influence of Mixing Conditions on the Macro-Scale Homogeneity of Asphalt Mixtures Blended with Reclaimed Asphalt Pavement (RAP). Materials, 14.10.3390/ma14154137 ↗
7. A review of changes in composition of hot mix asphalt in the United States10.1080/15459620903249125 ↗
8. Cooling Time Estimation of Newly Placed Hot-Mix Asphalt Pavement in Different Weather Conditions10.1061/(ASCE)CO.1943-7862.0000832 ↗
9. Effect of Variability on End Product Specifications10.1061/(ASCE)0733-9364(1998)124:2(139) ↗
10. Evaluating Cost Overruns of Asphalt Paving Project Using Statistical Process Control Methods10.1061/(ASCE)0733-9364(2005)131:11(1173) ↗
11. Montgomery, D. (2013). Design and Analysis of Experiments, John Wiley and Sons.
12. Time of sampling strategies for asphalt pavement quality assurance10.1061/(ASCE)0733-9364(2002)128:1(85) ↗
13. Evaluation of Superpave HMA Mixture Properties at the Plant versus behind the Paver: Statistical Comparison of QC and QA Data10.1061/(ASCE)TE.1943-5436.0000399 ↗
14. Risk and expected pay factor analysis for assessing gap and dense-graded Superpave mixture specifications10.1080/10298436.2014.916404 ↗
15. Numerical analysis of asphalt mixture and comparison with physical Marshall test10.3846/13923730.2014.920413 ↗
16. Multi-faceted investigation into the effects of hot-mix asphalt parameters on Marshall Stability10.1016/j.conbuildmat.2012.11.002 ↗
17. Effect of “n” Exponent in Fuller Equation of Gradation on Hot Mix Asphalt Resistance to Permanent Deformation
18. Statistical analysis of component deviation from job mix formula in hot mix asphalt10.48084/etasr.5225 ↗
19. Design of maximum density aggregate grading10.1016/S0950-0618(02)00032-6 ↗
20. Effect of Film Thickness on Resistance to Permanent Deformation in Asphalt Mixtures10.3846/bjrbe.2015.42 ↗
21. Effect of Aging on Adhesion Properties of Asphalt Mixtures with the Use of Bitumen Bond Strength and Surface Energy Measurement Tests
22. Grey relational-regression analysis for hot mix asphalt design10.1016/j.conbuildmat.2010.12.011 ↗
23. Tan, T., Fan, Z., Xing, C., Tan, Y., Xu, H., and Oeser, M. (2020). Evaluation of Geometric Characteristics of Fine Aggregate and Its Impact on Viscoelastic Property of Asphalt Mortar. Appl. Sci., 10.10.3390/app10010130 ↗
24. The influence of aggregate, filler and bitumen on asphalt mixture moisture damage10.1016/j.conbuildmat.2007.07.009 ↗
25. Determination of fine aggregate angularity in relation with the resistance to rutting of hot-mix asphalt10.1016/j.conbuildmat.2004.05.004 ↗
26. Properties of gap-aggregate gradation asphalt mixture and permanent deformation10.1016/j.conbuildmat.2004.05.005 ↗
27. The bitumen batching system’s modernization and its effective analysis at the asphalt mixing plant10.3846/transport.2010.40 ↗
28. The Effect of Compaction Degree and Binder Content on Performance Properties of Asphalt Mixtures10.3846/bjrbe.2016.26 ↗
29. Status of Roller Mountable Microwave Asphalt Pavement Density Sensor10.1061/(ASCE)0733-9364(2001)127:1(46) ↗
30. Determination and evaluation of railway aggregate sub-ballast gradation and other properties variation10.3846/13923730.2016.1177586 ↗
31. Roberts, F.L., Kandhal, P.S., Brown, E.R., Lee, D.-Y., and Kennedy, T.W. (1996). Hot Mix Asphalt Materials, Mixture Design, and Construction, NAPA Research and Education Foundation.
32. Field Evaluation of Asphalt Film Thickness as a Design Parameter in Superpave Mix Design
33. Characterization of Air Void Distribution in Asphalt Mixes using X-ray Computed Tomography10.1061/(ASCE)0899-1561(2002)14:2(122) ↗
34. Influence of Air Voids on Mechanical Properties of Asphalt Mixtures10.1080/14680629.2011.9695258 ↗
35. Influence of Aggregate Type and Gradation on Voids of Asphalt Concrete Pavements10.1061/(ASCE)0899-1561(1998)10:2(76) ↗
36. Further exploration of the pavement oxidation model—Diffusion-reaction balance in asphalt10.1016/j.conbuildmat.2017.11.095 ↗
37. Influence of Aging on Surface Free Energy of Asphalt Binder
38. Tiwari, N., Rondinella, F., Satyam, N., and Baldo, N. (2023). Alternative Fillers in Asphalt Concrete Mixtures: Laboratory Investigation and Machine Learning Modeling towards Mechanical Performance Prediction. Materials, 16.10.3390/ma16020807 ↗
39. Adhesive and Cohesive Properties of Asphalt-Aggregate Systems Subjected to Moisture Damage10.1080/14680629.2010.9690325 ↗
40. Investigation of In-Place asphalt film thickness and performance of hot-mix asphalt mixtures10.1061/(ASCE)0899-1561(2009)21:6(262) ↗
41. Influence of Air Void Distribution on the Moisture Susceptibility of Asphalt Mixes10.1061/(ASCE)0899-1561(2007)19:8(655) ↗
42. Alternative Approach toward the Aging of Asphalt Binder10.3141/2505-04 ↗
43. Minimum Voids in Mineral Aggregate in Hot-Mix Asphalt Based on Gradation and Volumetric Properties10.1177/0361198196154500110 ↗
44. Effect of Asphalt Film Thickness on Short- and Long-Term Aging of Asphalt Paving Mixtures10.1177/0361198196153500111 ↗
45. Research and evaluation of ruts in the asphalt pavement on Lithuanian highways10.3846/13923730.2013.817481 ↗
46. Nondestructive Evaluation of Thickness and Bearing Capacity of Roadway Pavement Structure
47. Asphalt layer density and air voids content: GPR and laboratory testing data reliance10.7250/bjrbe.2020-15.486 ↗
48. Framework for Empirical Process to Improve Nonnuclear Gauge Performance in Hot-Mix Asphalt Pavement Construction10.1061/(ASCE)CO.1943-7862.0000638 ↗
49. Expert System to Control Construction Problems in Flexible Pavements10.1111/mice.12001 ↗
50. Application of analytic hierarchy process (AHP) for sustainable pavement performance management in Qatar10.1108/ECAM-02-2020-0136 ↗
51. Continuous Quality Improvement Techniques for Data Collection in Asset Management Systems10.1061/(ASCE)CO.1943-7862.0000427 ↗
52. ĮT ASFALTAS 08 (2008). Automobilių Kelių Dangos Konstrukcijos Asfalto Sluoksnių Įrengimo Taisyklės [The Installation Rules of the Roads Pavement Asphalt Layers], Lietuvos Automobilių Kelių Direkcija.
53. Sudėtingų dydžių kompleksinis vertinimas [Complex evaluation of complex quantities]10.3846/1648-0627.2008.9.160-168 ↗
54. Kendall, M., and Gibbons, J.D. (1990). Rank Correlation Methods, Oxford University Press. [5th ed.].
55. Application of expert evaluation method to determine the importance of operating asphalt mixing plant quality criteria and rank correlation10.3846/bjrbe.2011.07 ↗
56. Sivilevičius, H., and Martišius, M. (2023). The Significance of the Factors Increasing the Asphalt Pavement Recycling Rate in the Country, Determined Using Multiple-Criteria Decision-Making Methods. Appl. Sci., 13.10.20944/preprints202310.1660.v1 ↗
57. Sachs, L. (1972). Statistische Auswertungsmethoden [Statistical Evaluation Methods], Springer.10.1007/978-3-662-10037-0 ↗
58. Čekanavičius, V., and Murauskas, G. (2006). Statistika ir jos Taikymai. I Knyga, TEV. [Statistics and its applications. Book I].
59. Integrated determination of objective criteria weights in MCDM10.1142/S0219622016500036 ↗
60. Maskeliūnaitė, L., and Sivilevičius, H. (2021). Identifying the importance of criteria for passenger choice of sustainable travel by train using ARTIW and IHAMCI methods. Appl. Sci., 11.10.3390/app112311503 ↗
61. Saaty, T.L. (1980). The Analytic Hierarchy Process, McGraw-Hill.10.21236/ADA214804 ↗
62. Analytical hierarchy process: Revolution and evolution10.1007/s10479-021-04432-2 ↗
63. Improved AHP method and its application in risk identification10.1061/(ASCE)CO.1943-7862.0000605 ↗
64. Ai, Q., Huang, J., Du, S., Yang, K., and Wang, H. (2022). Comprehensive Evaluation of Very Thin Asphalt Overlays with Different Aggregate Gradations and Asphalt Materials Based on AHP and TOPSIS. Buildings, 22.10.3390/buildings12081149 ↗
65. Key criteria for evaluating the green performance of ports10.1520/JTE20140354 ↗
66. Sivilevičius, H., and Maskeliūnaitė, L. (2025). Assessment of the Quality of Passenger Transportation by Train Using Multiple Criteria Decision Making Methods, Springer Nature.10.1007/978-3-031-85877-2 ↗
67. Taha, H.A. (2011). Operations Research. An Introduction, Pearson/Prentice Hall. [9th ed.].
68. Saaty, T. (1996). Decision Making with Dependence and Feedback: The Analytic Network Process: The Organization and Prioritization of Complexity, RWS Publications.
69. Understanding Statistics in Maintenance Quality Assurance Programs10.1177/0361198106194800103 ↗
70. Statistical Evaluation of Different Sample Sizes for Local Calibration Process in the Highway Safety Manual10.3141/2515-13 ↗