Assessment of Construction Project Complexity

Objective: Project complexity is a crucial factor in project management that presents auxiliary obstacles to reaching project objectives (cost, time, safety, and quality). This study aims at understanding project complexity and factors affecting project complexity. The overall objective of the study is to determine the nature of complexity and characteristics, identify the important complex factors that influence the complexity of the project, factor weight of the complex factors, and develop a proposed construction complexity index (CCI). Methods: According to the literature review, the Analytic Hierarchy Process (AHP) method is used to measure the affecting factors of project complexity. Results: This paper developed an index to measure complexity based on factor weights called construction complexity index (CCI). The validity of this index was verified by studying 3 cases. The construction complexity index (CCI) proposed here allows measuring the complexity of the projects in Egypt. The results of this paper provide guidelines on how to successfully manage the complexity of the project. Conclusion: Project complexity management relates to the challenge of dealing with technical competence, professional diversity, uncertainties, and unforeseen events in project implementation. Project managers, who are critical to effectiveness or failure, need skills such as adaptation, creativity, and flexibility to meet this challenge. Therefore, this study provides guidelines to help practitioners to develop their capabilities in managing complex projects. Moreover, this paper enables participants to identify factors affecting the complexity of projects and how to calculate this complexity through the complex index. The outcomes of this study can be used by practitioners to develop a complexity assessment and management tool, which would enable industry practitioners to allocate resources effectively on complex construction projects. This research aimed to develop a measure by which the complexity of construction projects in Egypt can be evaluated and establish guidelines on avoiding complexity in projects.

Entropy Metrics of Radar Signatures of Sea Surface Scattering for Distinguishing Targets

This paper analyzes sea clutter by a random series without assuming the scattering being independent. We quantitated the complexity of sea clutter by applying multiscale sample entropy. We found that above certain wave heights or wind speeds, and for HH or VV polarization, the target can be distinguished from sea clutter by regarding (i) the sample entropy at large scale factors or (ii) the complexity index (CI) as entropy metrics. This is because the backscattering amplitudes of range bins with the primary target were found equipped with the lowest sample entropy at large scale factors or the lowest CI compared to that of range bins with sea clutter only. To further cover low-to-moderate sea states, we constructed a polarized complexity index (PCI) based on the polarization signatures of the multiscale sample entropy of sea clutter. We demonstrated that the PCI is yet another alternative entropy metric and can achieve a superb performance on distinguishing targets within 1993’s IPIX radar data sets. In each data set, the range bins with the primary target turned to have the lowest PCI compared to that of range bins with sea clutter alone. Moreover, in our experiment using 1993’s IPIX radar data sets, the PCIs of range bins with sea clutter only were almost the same and stable in each data set, further suggesting that the proposed PCI metric can be applied in the presence of no or multiple targets through proper fitting curves.

Measuring the Nature of Individual Sequences

This study reviews and compares indicators that can serve to characterize numerically the nature of state sequences. It also introduces several new indicators. Alongside basic measures such as the length, the number of visited distinct states, and the number of state changes, we shall consider composite measures such as turbulence and the complexity index, and measures that take account of the nature (e.g., positive vs. negative or ranking) of the states. The discussion points out the strange behavior of some of the measures—Elzinga’s turbulence and the precarity index of Ritschard, Bussi, and O’Reilly in particular—and propositions are made to avoid these flaws. The usage of the indicators is illustrated with two applications using data from the Swiss Household Panel. The first application tests the U-shape hypothesis about the evolution of life satisfaction along the life course, and the second one examines the scarring effect of earlier employment sequences.