Earth Dam Construction Simulation Considering Stochastic Rainfall Impact

Zhang, Jun; Zhong, Dexing; Wu, Binping; Lv, Fei; Cui, Bo

doi:10.1111/mice.12337

Cited by 14 publications

(8 citation statements)

References 59 publications

(68 reference statements)

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“…Therefore, for the three HRs of the TAHR, the mixed exponential is the most adequate distribution for the estimation of the daily rainfall probabilities. Abas et al (2014), Li et al (2013), Moreno‐Pérez, Woolhiser and Roldán‐Cañas (2014), Wan et al (2005), and Zhang, Znong, Wu, Fei, and Cui (2018) also indicated the use of the mixed exponential for rainfall modeling.…”

Section: Resultsmentioning

confidence: 98%

Cumulative distribution function of daily rainfall in the Tocantins–Araguaia hydrographic region, Amazon, Brazil

Progênio

Blanco

2020

Natural Resource Modeling

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Knowing the temporal and spatial variability of the probability of rainfall occurrence is indispensable for the planning and management of agricultural and agroindustrial activities. This study aims to evaluate the applicability of four cumulative distribution functions (CDFs; exponential, two-parameter exponential, mixed exponential, and gamma) to estimate the probability of daily rainfall in the Tocantins-Araguaia hydrographic region (TAHR). The CDFs were applied to 196 rainfall gauge stations distributed in three homogeneous regions (HRs) of rainfall in the TAHR. In relation to the CDFs, the Kolmogorov-Smirnov test and the probability-probability plot showed that the mixed exponential best adhered to the observed data for most of the months of the year in the three HRs that were analyzed. In the few months that this function did not have a good statistical performance, the gamma function was the one with the best fit quality. This study can be used as a guide for studies in other river basins in Brazil and around the world to estimate CDFs of daily rainfall. Recommendations for Resource Managers• Stochastic models using cumulative distribution functions (CDFs) allow the simulation of rainfall data by means of their frequency of occurrence. Some CDFs have been used to verify the behavior and variability of precipitation over the years. For example, the mixed exponential, the Weibull with two parameters, the generalized Pareto, the lognormal, and the gamma with two parameters.• Daily rainfall modeling is important to recognize the occurrence patterns for the prediction of the climatic behavior of a region. Sectors such as agriculture, flood control, and human supply projects need knowledge of precipitation for planning, management, and operation.• The knowledge of the rainfall behavior is very important in Tocantins-Araguaia hydrographic region because it is located in the arc of deforestation in the Amazon. In this case, the dry season and temperatures have been increasing due to global climate change and deforestation itself. K E Y W O R D S adherence test, agroindustrial activities, ecotone, homogeneous regions, water resources

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Section: Resultsmentioning

confidence: 98%

Cumulative distribution function of daily rainfall in the Tocantins–Araguaia hydrographic region, Amazon, Brazil

Progênio

Blanco

2020

Natural Resource Modeling

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“…The ability of discrete-event simulation (DES) to incorporate the variability associated with external factors, such as weather, to determine the impact of uncertainty on system outcomes is wellestablished. As presented in Section 2.2 (Table 1), several studies have successfully applied DES to investigate the effects of adverse weather on construction activities [3,15,23,25,29,41], and DES was shown to be a reasonable tool for scheduling construction activities of onshore wind projects [45]. However, a simulation model capable of considering the impact of weather on the activities on onshore wind farm construction has not been developed.…”

Section: Simulation As a Proposed Approachmentioning

confidence: 99%

Simulation-Based Approach for Lookahead Scheduling of Onshore Wind Projects Subject to Weather Risk

Mohamed

Jafari

Chehouri³

et al. 2021

Sustainability

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Executed outdoors in high-wind areas, adverse weather conditions represent a significant risk to onshore wind farm construction activities. While methods for considering historical weather data during pre-construction scheduling are available, approaches capable of quantitatively assessing how short-term weather fluctuations may impact upcoming construction activities have yet to be developed. This study is proposing a hybrid simulation-based approach that uses short-term precipitation, wind speed, and temperature forecasts together with planned and as-built activity durations to develop lookahead (e.g., upcoming 14-day) schedules for improved project planning and control. Functionality and applicability of the method was demonstrated on a case study of a 40 MW onshore wind project, and the method was validated using event validity, face validation, and sensitivity analysis. As expected, favorable weather conditions experienced during the tested lookahead periods resulted in a negligible impact (less than 10% reduction) on the productivity of weather-sensitive activities, which translated into a project delay of one day. The responsiveness of the framework was confirmed through sensitivity analysis, which demonstrated a 50% reduction in productivity resulting from poor weather conditions. The ability of the method to provide decision-support not currently offered by commercially-available scheduling systems was confirmed by subject experts, who endorsed the ability of the method to enhance lookahead scheduling and to facilitate the monitoring and control of weather impact uncertainty on project durations.

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“…Therefore, the optimal use of resources is a critical task for contractors. Optimizing earthmoving operations has involved the development of various models employing different techniques, including simulation optimization [6,8,9], simulation and genetic algorithm (Parente and Gomez) in [10], computer simulation [11,12], linear programming [13], and artificial intelligence [14].…”

Section: Modeling and Simulationmentioning

confidence: 99%

Stochastic Earthmoving Fleet Arrangement Optimization Considering Project Duration and Cost

2020

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Earthmoving is one of the main processes involved in heavy construction and mining projects. It requires a significant share of the project budget and can dictate its overall success. Earthmoving related activities have a stochastic nature that affects the project cost and duration. In common practice, the equipment required for a project is selected using average operating cycles, neglecting the stochastic nature of operations and equipment. Ultimately this can lead to rough estimates and poor results in meeting the projects’ objectives. This research aims to provide a decision-support tool for earthmoving operations and achieve the best arrangement of equipment based on project objectives and equipment specifications by utilizing historical data. Operation simulation is identified as an efficient technique to model and analyze the stochastic aspects of the cost and duration of earthmoving operations in construction projects. Therefore, two simulation models—namely the Decision-Support Model and the Estimation Model, have been developed in the Symphony.net modeling environment to address the industry needs. The Decision-Support Model provides the best arrangement of equipment to maximize global resource utilization. In contrast, the Estimation Model captures more of the project details and compares various equipment arrangements. In this paper, these models are created, and the modeling logic is validated through a case study employing a real-world earthmoving project that demonstrates the model’s capabilities. The decision support model showed promising results in determining the optimized fleet selection when considering equipment and shift variations, and the cost model helped better quantifying the impact of the decision on the cost and profit of the project. This modeling approach can be reproduced by others in any case of interest to gain optimal fleet management.

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Earth Dam Construction Simulation Considering Stochastic Rainfall Impact

Cited by 14 publications

References 59 publications

Cumulative distribution function of daily rainfall in the Tocantins–Araguaia hydrographic region, Amazon, Brazil

Cumulative distribution function of daily rainfall in the Tocantins–Araguaia hydrographic region, Amazon, Brazil

Simulation-Based Approach for Lookahead Scheduling of Onshore Wind Projects Subject to Weather Risk

Stochastic Earthmoving Fleet Arrangement Optimization Considering Project Duration and Cost

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