Risk-Based Protocol for Inspection of Transportation Construction Projects Undertaken by State Departments of Transportation

Mostafavi, Ali; Abraham, Dulcy M.; Noureldin, Samy; Pankow, Greg; Novak, Joseph; Walker, Ronald F.; Hall, Kevin G; George, Bren

doi:10.1061/(asce)co.1943-7862.0000664

Cited by 24 publications

(9 citation statements)

References 23 publications

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“…Examples of such problems abound in pricing of airline tickets (revenue management), in queuing control for wireless communications (where variance in rate of dropped calls is of concern), and financial transactions (portfolio allocation). In the operation of complex systems and System of Systems (SoS) theory, these algorithms can be used for dynamic decision-making in numerous scenarios, including where a government has to allocate capital to infrastructure projects (Mostafavi et al 2012) and where a centralized agent needs to control a team of robots (Dudek et al 1996). In such systems, the state of all the sub-systems (infrastructure projects or robots) can be combined to form an aggregate state for the MDP in which the decision-maker is the supervising system which is responsible for selecting actions that influence the SoS as a whole.…”

Section: Discussionmentioning

confidence: 99%

Variance-penalized Markov decision processes: dynamic programming and reinforcement learning techniques

Gosavi

2014

International Journal of General Systems

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In control systems theory, the Markov decision process (MDP) is a widely used optimization model involving selection of the optimal action in each state visited by a discrete-event system driven by Markov chains. The classical MDP model is suitable for an agent/decisionmaker interested in maximizing expected revenues, but does not account for minimizing variability in the revenues. An MDP model in which the agent can maximize the revenues while simultaneously controlling the variance in the revenues is proposed. This work is rooted in machine learning/neural network concepts, where updating is based on system feedback and step sizes. First, a Bellman equation for the problem is proposed. Thereafter, convergent dynamic programming and reinforcement learning techniques for solving the MDP are provided along with encouraging numerical results on a small MDP and a preventive maintenance problem.

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Section: Discussionmentioning

confidence: 99%

Variance-penalized Markov decision processes: dynamic programming and reinforcement learning techniques

Gosavi

2014

International Journal of General Systems

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“…One of these strategies is to employ a risk-based inspection method to better allocate inspection resources and reduce the demand for inspection staff while complying with quality requirements. For example, in 2012, an initial study was conducted for the InDOT to evaluate its current inspection practices and efficiently allocate its limited inspection resources ( 9 ). The study conducted a risk analysis for the QA activities of InDOT to prioritize inspection and testing of construction activities.…”

Section: Research Motivationmentioning

confidence: 99%

“…Previous studies attempted to provide a narrowed-down list of quality assurance (QA) inspection activities ( 9 – 12 ). For instance, Mostafavi et al identified and assessed QA activities to prioritize inspections of Indiana DOT (InDOT) ( 9 ). The researchers used a fuzzy set to assess probabilities of failure consequences.…”

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confidence: 99%

Approach to Determination of Risk-Based Inspection Workload for Highway Construction Projects

Mohamed

Tran

2022

Transportation Research Record: Journal of the Transportation R

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State departments of transportation (DOTs) are increasingly charged with developing and managing more highway construction projects while facing a substantial challenge in performing construction inspection activities with reduced resources of funding and inspection staff. Several state DOTs have applied workload reduction approaches including risk-based inspection methods to offset the shortage of inspection resources. The purpose of this study was to develop a framework to determine an appropriate inspection workload associated with construction inspection risk levels. The framework included three stages: (1) determination of project needs of inspection workforce, (2) utilizing risk-based prioritization of inspection activities, and (3) allocation of the workforce based on risk levels of inspection activities. The proposed framework was developed based on data collected from a comprehensive literature review and survey questionnaires of 50 state DOTs. The findings from this study indicated that almost all 38 responding state DOTs have a shortage of inspection staff. Four main strategies that state DOTs used to overcome the shortage of inspection workforce include outsourcing quality assurance (QA) inspections to consultants, using contractors’ test results for acceptance, reducing the number of inspections and testing frequency, and prioritizing QA inspections. The framework was tested and calibrated with a case study project in Kansas. The result shows that the proposed framework can be implemented by state DOTs to increase the value of inspection with a limited workforce. The framework also allows project managers to better understand and allocate the available inspection resources to alleviate the risk of quality shortfall.

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“…Electronic methods are commonly used to store and manage the data, but the field personnel still generally rely on collecting information using a paper-based method (1,2). Because the number and complexity of infrastructure projects managed by DOTs have steadily increased while their workforce shrinks (2)(3)(4)(5), DOTs are searching for ways to make their operations, including project inspection and management, more efficient and better able to handle the multitude of associated data.…”

mentioning

confidence: 99%

Factors Influencing Adoption of Information Technologies for Public Transportation Project Inspection: A WSDOT Case Study

Yamaura

Muench

Willoughby³

2019

Transportation Research Record: Journal of the Transportation R

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This paper presents a case study of the organizational change process associated with the Washington State Department of Transportation (WSDOT) year-long research program that implemented a cloud-based mobile project inspection application to 18 project engineering offices (PEO) across the state. Ultimately, four out of the 18 PEOs decided to adopt the new technology. Data from semi-structured interviews and a user study conducted two months after implementation are used to identify organizational change strategies used by WSDOT, and how those relate to ideas from the general literature on change management. The loss of upper management program leaders, inadequate communication and training to prepare personnel for the change, and policy and procedural uncertainties in integrating the change with other systems and operations were found to be factors that may have influenced the outcome of the program. While this paper focuses on one DOT’s efforts, other DOTs may have similar organizational structures and implementation efforts, and the findings and lessons learned could serve as a representative model for how such implementation might best be accomplished in a DOT and how that might differ from traditional change management guidance.

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Risk-Based Protocol for Inspection of Transportation Construction Projects Undertaken by State Departments of Transportation

Cited by 24 publications

References 23 publications

Variance-penalized Markov decision processes: dynamic programming and reinforcement learning techniques

Variance-penalized Markov decision processes: dynamic programming and reinforcement learning techniques

Approach to Determination of Risk-Based Inspection Workload for Highway Construction Projects

Factors Influencing Adoption of Information Technologies for Public Transportation Project Inspection: A WSDOT Case Study

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