Comparison of Three Retaining Wall Condition Assessment Rating Systems

Gabr, M. A.; Rasdorf, William; Findley, Daniel J.; Butler, Cedrick J.; Bert, Steven A.

doi:10.1061/(asce)is.1943-555x.0000403

Cited by 2 publications

(2 citation statements)

References 6 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Decision-making related to extensions, acquisitions, and strategic change relies on inspecting the performance of a facility [3,11]. To this end, many studies have attempted to develop performance assessment methodologies for various facilities [12][13][14][15][16]. Although these studies have contributed to improving performance assessment, they tended to focus on methods to understand the current state of facilities, there being insufficient consideration for predicting the future state of facilities using past data.…”

Section: Literature Review 21 Performance Evaluation and Predictionmentioning

confidence: 99%

Framework of Prediction Model for Mid- to Long-Term Performance Changes of Urban Railway Facilities Based on Performance Evaluation Reports

et al. 2021

View full text Add to dashboard Cite

To maintain railway facilities in an appropriate state, systematic management based on mid- and long-term maintenance plans through future performance prediction must be carried out. To this end, it is necessary to establish and utilize a model that can predict mid- to long-term performance changes of railway facilities by predicting performance changes of individual sub-facilities. However, predicting changes in the performance of all sub-facilities can be difficult as it requires large volumes of data, and railway facilities are a collection of numerous sub-facilities. Therefore, in this study, a framework for a model that can predict mid- to long-term performance changes of railway facilities through analysis of continuously accumulated performance evaluation results is proposed. The model is a system with a series of flows that can classify performance evaluation results by individual sub-facilities, predict performance changes by each sub-facility using statistical methods, and predict mid- to long-term performance changes of the facility. The developed framework was applied to 36,537 sub-facilities comprising 12 lines of two urban railways in South Korea to illustrate the model and verify its applicability and effectiveness. This study contributes in terms of its methodology in establishing a framework for predicting mid- to long-term performance changes, providing the basis for the development of an automated model able to continuously predict performance changes of individual sub-facilities. In practical terms, it is expected that railway facility managers who allow trade-off between reliability and usability can contribute to establishing the mid- to long-term maintenance plans by utilizing the model proposed in this study, instead of subjectively building them.

show abstract

Section: Literature Review 21 Performance Evaluation and Predictionmentioning

confidence: 99%

Framework of Prediction Model for Mid- to Long-Term Performance Changes of Urban Railway Facilities Based on Performance Evaluation Reports

et al. 2021

View full text Add to dashboard Cite

show abstract

“…These walls are effective in controlling small-and medium-sized slopes or landslides and are divided into mortar rubble, plain concrete, and reinforced concrete types according to their material composition. Since reinforced concrete retaining walls can withstand greater soil pressure and landslide thrust than the other two types of walls, they are more widely used [1,2].…”

Section: Introductionmentioning

confidence: 99%

Deformation State Analysis and Design Method of Bottom-Confined Gravity Retaining Walls

et al. 2023

View full text Add to dashboard Cite

The deformation failure modes of gravity retaining walls include overturning, toppling, deep sliding, and eccentric failure. The complex eccentric failure mode, which involves the multi-stage deformation of retaining walls, suffers from a lack of mechanical models and design theories. Based on the basic theory of reinforced concrete construction, this study established a deformation calculation model of bottom-confined reinforced concrete gravity retaining walls (RC-GRWs), which are composed of tensile, compressive, and rigid zones. Then, this study defined RC-GRWs’ normal-operation, cracking, yield, and ultimate limit states, as well as the displacement of RC-GRWs in these states, according to the concrete cracking and steel reinforcement yield conditions. Accordingly, this study proposed a design method and process to determine the deformation of bottom-confined RC-GRWs. A project case shows that the calculation method for RC-GRW deformation proposed in this study can effectively assess the deformation state of in-service retaining walls and that optimized cracked retaining walls can meet the deformation-based design requirements.

show abstract

Comparison of Three Retaining Wall Condition Assessment Rating Systems

Cited by 2 publications

References 6 publications

Framework of Prediction Model for Mid- to Long-Term Performance Changes of Urban Railway Facilities Based on Performance Evaluation Reports

Framework of Prediction Model for Mid- to Long-Term Performance Changes of Urban Railway Facilities Based on Performance Evaluation Reports

Deformation State Analysis and Design Method of Bottom-Confined Gravity Retaining Walls

Contact Info

Product

Resources

About