A Random Factorial Design of Experiments Study on the Influence of Key Factors and Their Interactions on the Measurement Uncertainty: A Case Study Using the ZEISS CenterMax

Kritikos, Michaela; Maure, Lissette Concepción; Cespedes, Alfredo Alejandro Leyva; Sobrino, Daynier Rolando Delgado; Hrušecký, Róbert

doi:10.3390/app10010037

Cited by 11 publications

(5 citation statements)

References 13 publications

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“…The percentage of plant life is obtained using the following formula (Wahyuningtyas et al 2022 The research data obtained were processed using the Anova table and calculated using the Statistical Analysis System version 9.1.3. The statistical model used in this research activity is (Kritikos et al 2019):…”

Section: Discussionmentioning

confidence: 99%

The growth performance of Rhizophora apiculata using the cut-propagule method for mangrove rehabilitation in Indonesia

2022

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Abstract. Usman AHA, Hartoyo APP, Kusmana C. 2022. The growth performance of Rhizophora apiculata using the cut-propagule method for mangrove rehabilitation in Indonesia. Biodiversitas 23: 6366-6378. A high decrease in the size of Indonesia's mangrove forests requires effective and efficient rehabilitation efforts. One of the efforts is using Rhizophora apiculata as the adaptive and fast-growing mangrove species and its cut-propagule seedling method for accelerating plant propagation. The objectives of this study were to analyze the growth performance of R. apiculata propagules with various treatments of propagule cutting, seedling media, and giving growth regulators (PGR), as well as to determine the best combination of treatment for the growth of R. apiculata. An experimental study was conducted in a greenhouse using a randomized design with 3 factors (propagule cutting, seedling media, and giving PGR) and 3 replications. The interaction of the combination of cut-propagule treatment, planting media, and growth regulator (AxBxC) only had a significant effect on the increase in the number of leaves of R. apiculata. Complete propagules and the lower part of cutting propagules with a combination of mud+sand seedling media and 10,000 ppm PGR (a0b1c1 and a2b1c1) resulted in the best value on the growth of R. apiculata. The cut-propagule seedling method of R. apiculata still showed good growth so it can be used as an alternative to mass vegetative reproduction in mangrove rehabilitation activity.

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Section: Discussionmentioning

confidence: 99%

The growth performance of Rhizophora apiculata using the cut-propagule method for mangrove rehabilitation in Indonesia

2022

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“…The response was measured at predetermined settings of the experimental conditions when performing the experiment. Each experimental condition employed to obtain a response measurement was a run [79].…”

Section: Data Collection and Analysismentioning

confidence: 99%

Development of a Prediction System for Design Defects of Buildings

Nouh,

El-Kasaby,

Aboulata

2023

Civ Eng J

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The research aimed to establish a quantitative model to predict the occurrence of five types of design defects: unsafe design, incompatible design, inefficient design, design ambiguity, and design delays. It also examined the contractor's liability for design to determine who is ultimately responsible for design defects disputes between the employer and contractor. The study employed a descriptive methodology to identify fifteen potential influencing factors on design defects. Data were collected from 42 construction projects and tested using Design of Experiments (DOE). Out of these fifteen factors, only five—design schedule, design firm experience, stakeholders’ involvement, project delivery system, and information accuracy—had a significant impact on design defects. These factors were used in the development of a new model to predict the occurrence of design defects. The model was validated using data that was held separate from the model development process for validation purposes. The calculated Mean Absolute Percentage of Error (MAPE) for the new model is 19%, which is considered a "good" prediction accuracy. The research achieved three significant milestones: 1) It identified the ultimate responsibility for the consequences of design defects in construction projects across different jurisdictions. This information can be used to resolve potential disputes between employers, designers, and contractors’ organizations. 2) It provided guidelines on how to minimize design defects in construction projects, thereby mitigating the consequences of such defects. 3) It developed a new model to predict the occurrence of design defects in construction projects. This model aids decision-makers in responsible organizations during the process of predicting the appropriate reserve to be added to the project schedule and budget. Consequently, it helps mitigate the risk of design defect-related delays and cost overruns. Doi: 10.28991/CEJ-SP2023-09-017 Full Text: PDF

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“…The response was measured at predetermined settings of the experimental conditions when performing the experiment. Each experimental condition used to obtain a response measurement was a run (Kritikos et al , 2020).…”

Section: Analyzementioning

confidence: 99%

Establishment of prediction system for cost of defect liability phase in construction project

Nouh

El-Kasaby

Hussein

2022

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Purpose This study aims to establish a new system to predict the defect liability phase (DLP) cost using the Six Sigma methodology, which investigates sources of variations and reduces the error level to 3.4 per million through five phases: define, measure, analyze, design and verify. Design/methodology/approach After the initial handover of the construction project, the DLP follows the practical completion. During this stage, the contractor is responsible for the remedy of any defects that appeared in the project. Many researchers have studied defect reasons and their associated costs in different industries, while the construction industry remains a green field for this kind of research. The objective of this study was to develop a model to predict the DLP cost. The research methodology adopted the five stages of the Six Sigma cycle: defining objectives, measuring the data, analyzing performance, designing the model and verifying the results. Twenty factors were identified as potential factors affecting the DLP cost. Factors were categorized into two main clusters: project data and organization data. Interviews were conducted with 42 project management experts, who have 8–35 years of experience in construction project management, to rank the 20 factors based on their importance. Simo’s procedure was used to obtain the weight of each factor affecting the DLP cost based on the opinions of the experts. The Pareto principle was used to select the “Vital Few” factors affecting the DLP cost, and six factors were selected. The design of experiments (DOE) was used to establish a dynamic model to predict the DLP cost using a sample of 41 construction projects obtained from the above-mentioned 42 project management experts. The model accuracy was verified using data obtained from a different sample of five construction projects, which were not used to establish the model. Findings The results showed that among the 20 factors, only six were found to have a cumulative impact of 50% over the cost of the DLP: type of project, project contract value, nationality of the employer, project manager experience, DLP duration and sector of the employer. A model was established through the DOE to predict the DLP cost using the values of the aforementioned factors. Research limitations/implications As a natural limitation of using DOE, the newly developed model can be applied to predict the DLP cost based on data within the range of data used during the model development, which means that model is confined within the specific measured values of factors. Furthermore, it will be beneficial for future studies to study the impact of other factors related to the types of materials or equipment used in building the project because it was not considered during this study because of the huge diversities in these factors and difficulties in determining its impact on the DLP cost. Practical implications The unique results of using DOE through Minitab software facilitated obtaining of a dynamic model, which means that researchers can modify any value of the six factors and monitor instantly the expected change in the DLP cost, which will allow a better understanding of the impact of each factor on the DLP cost. Moreover, the new model will help contractors to predict the expected DLP cost to be added for their project budget, which will mitigate the risk of cost overrun resulted from the cost of defect rectification. Originality/value A dynamic model was established to predict the DLP cost using the DOE. The new model was validated, and the prediction error ranged from −18% to +21%.

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A Random Factorial Design of Experiments Study on the Influence of Key Factors and Their Interactions on the Measurement Uncertainty: A Case Study Using the ZEISS CenterMax

Cited by 11 publications

References 13 publications

The growth performance of Rhizophora apiculata using the cut-propagule method for mangrove rehabilitation in Indonesia

The growth performance of Rhizophora apiculata using the cut-propagule method for mangrove rehabilitation in Indonesia

Development of a Prediction System for Design Defects of Buildings

Establishment of prediction system for cost of defect liability phase in construction project

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