Automated contract time determination system for highway projects

Jeong, Hyung Seok; Atreya, Siddharth; Oberlender, Garold D.; Chung, BooYoung

doi:10.1016/j.autcon.2009.04.004

Cited by 15 publications

(16 citation statements)

References 2 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Existing studies on generating activity sequences have been focused on utilizing the expertise of experienced schedulers or logical assumptions about construction activity sequences. For example, Jeong et al [8] developed fourteen different highway scheduling templates based on the Oklahoma DOT schedulers' experience. Bruce et al [5] utilized a list of controlling activities and developed templates based on experts' inputs for 12 types of road and bridge construction projects.…”

Section: Prior Studiesmentioning

confidence: 99%

“…Many academic studies have focused on the first two steps: a) identifying construction activities and b) determining activity durations using historical and reliable production rates to enhance the accuracy of schedule development [6,7]. Regarding determining the sequence of activities, many studies pointed out the heavy reliance of owners and contractors on the experience and knowledge of experienced schedulers as the single most crucial source [5,8].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Mining Daily Work Report Data for Detecting Patterns of Construction Sequences

Shrestha¹,

Le²,

Jeong³

et al. 2019

Proceedings of the Creative Construction Conference 2019

View full text Add to dashboard Cite

Sequencing construction activities in highway projects is a complex planning process which requires not only considerable knowledge and practical experience of the planner/scheduler about various relevant aspects, such as the activities themselves, construction and procurement processes, and construction methods, but also input from other key members of the project regarding specific constraints and requirements. Moreover, sequencing is an iterative process; the sequence developed in the planning phase is likely to change in the construction phase. Therefore, learning from as-built schedules of past completed projects is needed to improve the planning and scheduling processes for future projects. In current practices, most state Departments of Transportation (DOTs) still mainly rely on schedulers' experience for schedule development. A data-driven systematic approach is still lacking, although the highway agencies have been spending a significant amount of money, time, and effort to collect various digital data during the construction process. This study aims to leverage historical digital daily work report data available in the DOTs' database to detect patterns of construction sequences in highway projects. Daily work report data collected from a state DOT were used to conduct a case study that developed a Sequential Pattern Mining algorithm to extract frequent sequential relationships among the activities for one major type of highway projects. Peer-review under responsibility of the scientific committee of the Creative Construction Conference 2019.

show abstract

Section: Prior Studiesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Mining Daily Work Report Data for Detecting Patterns of Construction Sequences

Shrestha¹,

Le²,

Jeong³

et al. 2019

Proceedings of the Creative Construction Conference 2019

View full text Add to dashboard Cite

show abstract

“…Inefficient activity sequence identification results in inaccurate critical path of the project and project duration and contract time determination. Inaccurate estimation of the contract time can lead to an increase in construction cost, inconvenience to the public, and a raise in the number of litigations [2,3,4].…”

Section: Introductionmentioning

confidence: 99%

“…Research studies tried to address this issue. Typical activity dependency rules have been identified based on physical and material constraints by analysing construction drawing and using experts' knowledge [7,4]. A recent study developed a data mining model to analyse historical schedules to obtain common sequential patterns among activities [8].…”

Section: Introductionmentioning

confidence: 99%

“…Some researchers used expert knowledge to identify the common sequence of activities. Jeong et al [4] obtained Oklahoma DOT schedulers' knowledge to develop fourteen highway scheduling templates. They illustrated activities using flow diagrams to show the sequence, concurrency, and time overlapping of activity implementation in fourteen highway project types.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A Deep Learning Algorithms to Generate Activity Sequences Using Historical As-built Schedule Data

Alikhani¹,

Le²,

Jeong³

2020

Proceedings of the Creative Construction E-Conference 2020

View full text Add to dashboard Cite

Project schedule development requires having knowledge about the project's activities and the proper sequence of them. In traditional practice, arranging project activities in a feasible sequential order heavily relies on the project scheduler's practical experience. However, personal experience is limited and prone to include human errors. In this paper, a Deep Learning model is employed to be trained on historical project schedules to predict sequential activities. The proposed model uses a Bidirectional Long Short-Term Memory Recurrent Neural Networks that learns the activity predecessors in the forward direction and the activity successors in the backward direction. The model receives one or more activities and predicts subsequent and precedent activities in a sequential order that have the highest likelihood of occurrence in the historical data. The model is compared with a Sequential Pattern Mining technique that identifies the most probable sequential patterns of activities. The two methods are applied to as-built highway project schedules obtained from a highway agency in the U.S to compare the performance of the two methods. While the Sequential Pattern Mining model provides sequential patterns for certain activities, the Deep Learning model generates a back-tail and a front-tail of activities with any arbitrary length for to provide a more flexible support tool for project schedulers.

show abstract