A Video-Enabled Dynamic Site Planner

Liu, Chin-Wei; Kang, Shih-Chung

doi:10.1061/9780784413616.194

Cited by 31 publications

(9 citation statements)

References 13 publications

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“…It works on the assumption that number of clusters 'c' is known for the given dataset and attempts to minimize the objective function [20,21]. The main disadvantage of this algorithm is that it follows hyperline constraint [22,23]. FCM assigns each pixel a degree of sharing for each class.…”

Section: Fuzzy Based Modified Possibilistic C-means (Mpcm)mentioning

confidence: 99%

Fuzzy machine learning approach for transitioned building footprints extraction using dual-sensor temporal data

2021

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This study presents a fuzzy approach, for detection of transitioned building footprints in urban area using medium resolution datasets. Multi-temporal remote sensing data sets from Landsat-8 Operational Land Imager and Sentinel-2A were used for generation of temporal indices database. The database was generated using class-based sensor independent-normalized difference vegetation index approach, with an aim to reduce spectral dimensionality of each image and maintain temporal dimensionality. The temporal indices database was subsequently used as input in Modified Possibilistic c-means classifier for transitioned building footprints extraction. The identified transitioned building locations were validated using ground samples as well as from Google images at four different test sites. For accuracy assessment, F-measure was calculated and its value was 0.75 or higher for all training and testing sites. Thus, using proposed fuzzy approach, transitioned building footprints were accurately identified compared to traditional techniques.

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Section: Fuzzy Based Modified Possibilistic C-means (Mpcm)mentioning

confidence: 99%

Fuzzy machine learning approach for transitioned building footprints extraction using dual-sensor temporal data

2021

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“…For example, it may classify a water temperature value greater than 30 • C as 'hot', whereas a value even slightly less than 30 • C is 'not hot'. Zadeh [19] introduced fuzzy logic to extend the law of bivalence. Fuzzy logic introduces the concept of partial truth-values that lie between 'completely true' and 'completely false'.…”

Section: Fuzzificationmentioning

confidence: 99%

“…A fuzzy logic model is a typical example of a data-based empirical-statistical model integrated with known processes. Fuzzy logic is a modeling approach [19] with the ability to reflect human behavior, which enables it to deal with uncertain and ambiguous subjects [20] such as currency exchange rates [21], weather prediction [22], and risk assessment [23]. In environmental science, fuzzy logic is widely used to develop environmental indices, especially with respect to water quality.…”

Section: Introductionmentioning

confidence: 99%

A Fuzzy Logic Model for Early Warning of Algal Blooms in a Tidal-Influenced River

Yang

Chen

et al. 2021

Water

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Algal blooms are one of the most serious threats to water resources, and their early detection remains a challenge in eutrophication management worldwide. In recent years, with more widely available real-time auto-monitoring data and the advancement of computational capabilities, fuzzy logic has become a robust tool to establish early warning systems. In this study, a framework for an early warning system was constructed, aiming to accurately predict algae blooms in a river containing several water conservation areas and in which the operation of two tidal sluices has altered the tidal currents. Statistical analysis of sampled data was first conducted and suggested the utilization of dissolved oxygen, velocity, ammonia nitrogen, total phosphorus, and water temperature as inputs into the fuzzy logic model. The fuzzy logic model, which was driven by biochemical data sampled by two auto-monitoring sites and numerically simulated velocity, successfully reproduced algae bloom events over the past several years (i.e., 2011, 2012, 2013, 2017, and 2019). Considering the demands of management, several key parameters, such as onset threshold and prolongation time and subsequent threshold, were additionally applied in the warning system, which achieved a critical success index and positive hit rate values of 0.5 and 0.9, respectively. The differences in the early warning index between the two auto-monitoring sites were further illustrated in terms of tidal influence, sluice operation, and the influence of the contaminated water mass that returned from downstream during flood tides. It is highlighted that for typical tidal rivers in urban areas of South China with sufficient nutrient supply and warm temperature, dissolved oxygen and velocity are key factors for driving early warning systems. The study also suggests that some additional common pollutants should be sampled and utilized for further analysis of water mass extents and data quality control of auto-monitoring sampling.

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“…Current industry practice primarily relies on a planner's experience and personal judgment to have a feel about site space, visualize it in a 2D drawing format, anticipate conflicts, evaluate the impact on project performance, and resolve these conflicts proactively [1] [6]. Due to the highly dynamic and complex nature of construction field operation, this informal and subjective approach in managing space can result in inaccurate and unreliable site planning that further leads to poor productivity and safety performance [7].…”

Section: Introductionmentioning

confidence: 99%

“…range camera and laser scanner), and spatial computational methods, site spatial planning is evolving from a 2D format and a manual procedure to a virtual 3D modeling approach with automated data processing that minimizes manual data entry. Virtual construction environment continuously capturing asbuilt or future site conditions in a digital format enables planners to better visualize and explore dynamic site space for planning purposes [6] [6]. It has a great potential to minimize subjectivity in the current planning practice and improve the accuracy and timeliness of recognizing and avoiding conflictsError!…”

Section: Introductionmentioning

confidence: 99%

Dynamic Space Conflict Modeling for Construction Operations

Saeedfar¹,

Song²,

Gore³

2016

Proceedings of the International Symposium on Automation and Robotics in Construction (IAARC)

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-Site planning is critical for efficient and safe construction operations. Current industry practice primarily relies on highly experienced planers and their personal judgment to anticipate and prevent space conflicts during construction operation. Inaccurate and unreliable site space planning not only leads to major loss of time and cost, but also negatively impact safety performance. Recent research has introduced new planning methods during the pre-construction phase to enhance site safety and productivity. This paper proposes a site spatial planning solution called Dynamic Space Conflict Modeling (DSCM), which can model dynamic field operations on a construction job site in a 3D model format based on the space demand of each activity and each resource. DSCM provides a process in which schedulers use time, crew information, and space demand of resources as inputs and capture the live space conflict as the output.

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A Video-Enabled Dynamic Site Planner

Cited by 31 publications

References 13 publications

Fuzzy machine learning approach for transitioned building footprints extraction using dual-sensor temporal data

Fuzzy machine learning approach for transitioned building footprints extraction using dual-sensor temporal data

A Fuzzy Logic Model for Early Warning of Algal Blooms in a Tidal-Influenced River

Dynamic Space Conflict Modeling for Construction Operations

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