Decision making for contractor insurance deductible using the evolutionary support vector machines inference model

Loss risk during the course of a construction project may be described in terms of frequency (i.e., loss frequency) and severity (i.e., loss severity). This study focused on improving the methodology used to evaluate loss risk. The authors first identified the common attributes of building construction project loss through a review of the literature and interviews with experts. Objective factors adequate to describe loss attributes were selected as model inputs. The loss prediction model was created using the evolutionary support vector machine inference model (ESIM) and deployed to evaluate loss frequency and loss severity. This research combined the deductible efficient frontier curve with the indifference curve of risk versus insurance cost, and developed criteria for optimal insurance deductible decision making.     

Least modification principle for case-based reasoning: a software project planning experience

A software project plan is composed of stages of activities and detailed tasks to be performed, and precedence restrictions among them. A project network is very complex and its construction requires a vast amount of field knowledge and experience. To assist the construction of a software project network, we adopt the case-based reasoning approach. However, the software project network may be designed differently depending upon the adopted development methodology and the style of the manager, so full automation of adjusting a past case is almost impossible. Thus, reducing the modification effort to a minimum is very important for enhancing the project planner's performance.In this research, we develop the framework of the Least Modification Principle (LMP) for Case-based Reasoning to solve this kind of problem. LMP is applicable when a reliable estimation of modification effort is possible. To apply the LMP for project network planning, we have selected 17 factors and the values for each factor to specify software projects. The modification effort is estimated based on the syntactic structure of modification rules. The performance of LMP is demonstrated with each of 31 test cases based on the other 30 past cases. We found that the LMP approach can significantly outperform the Ordinary Factor Matching approach.     

An Intranet-based cost control system

Information plays a key role in construction project management. In order for a construction project to be well managed, data from past projects, stored in a historical database, as well as data from the project at hand, must be readily available. It is an essential and valuable resource for project planning, control, reporting, and decision-making tasks. In each of these tasks, effective management of information is an integral part of a successful project management system, whose primary objective is completing the project on time and within budget limitations while meeting established quality requirements and other specifications. This paper describes the design and implementation of an Intranet-based cost control system. The Internet is utilized as a mechanism for communicating project control data and information.     

Large-scale group decision-making (LSGDM) for performance measurement of healthcare construction projects: Ordinal Priority Approach

People with various skill sets and backgrounds are usually found working on projects and thus, group decision-making (GDM) is one of the most important functions within any project. However, when projects concern healthcare or other critical services for proletariat or general public (especially during COVID19), the importance of GDM can hardly be overstated. Measuring the performance of healthcare construction projects is a critical activity and should be gauged based on the input from a large number of stakeholders. Such problems are usually recognized as large-scale group decision-making (LSGDM). In the current study, we aim to propose a decision support system for measuring the performance of healthcare construction projects against a large number of experts using ordinal data. The study identifies several key indicators from literature and recorded the observations of a large number of experts about these indicators. After that, the acceptable range of complexity is specified, the Silhouette plot is provided to find the optimal number of clusters, and the ordinal K-means method is employed to cluster the experts’ opinions. Later, the confidence level is measured using a novel Weighted Kendall’s W for the optimal number of the clusters, and the threshold is checked. Finally, the conventional problem is solved using the Group Weighted Ordinal Priority Approach (GWOPA) model in multiple attributes decision making (MADM), and the performance of the projects is determined. The validity of the proposed approach is confirmed through a comparative analysis. Also, a real-world case is solved, and the performance of some healthcare construction projects in China is gauged with a comprehensive sensitivity analysis.

     

Data dimensionality reduction with application to simplifying RBF network structure and improving classification performance

For high dimensional data, if no preprocessing is carried out before inputting patterns to classifiers, the computation required may be too heavy. For example, the number of hidden units of a radial basis function (RBF) neural network can be too large. This is not suitable for some practical applications due to speed and memory constraints. In many cases, some attributes are not relevant to concepts in the data at all. In this paper, we propose a novel separability-correlation measure (SCM) to rank the importance of attributes. According to the attribute ranking results, different attribute subsets are used as inputs to a classifier, such as an RBF neural network. Those attributes that increase the validation error are deemed irrelevant and are deleted. The complexity of the classifier can thus be reduced and its classification performance improved. Computer simulations show that our method for attribute importance ranking leads to smaller attribute subsets with higher accuracies compared with the existing SUD and Relief-F methods. We also propose a modified method for efficient construction of an RBF classifier. In this method we allow for large overlaps between clusters corresponding to the same class label. Our approach significantly reduces the structural complexity of the RBF network and improves the classification performance.     

A study on framework for effective R&D performance analysis of Korea using the Bayesian network and pairwise comparison of AHP

To effectively evaluate and analyze R&D performance, it is necessary to measure the relative importance of performance analysis factors and quantitative analysis methods that consider the objectivity and relevance of detail factors that constitute performance evaluation. This study suggests a framework for R&D performance evaluations by computing weights through an AHP (Analytical Hierarchy Process) expert survey and by applying a Bayesian Network approach whereby, through which, giving objectivity and allowing inference analyses. This framework can be used as a performance analysis indicator, which uses input and output performance factors in order to perform quantitative analysis for projects. We can quantitatively define the satisfactory level of each project and each performance analysis factor by assigning probability values. It is possible to analyze the relationship between project evaluation results (qualitative evaluation) and performance analysis indicator (quantitative performance). This performance analysis framework can infer posteriori probability using the prior probability and the likelihood function of each performance factor. In addition, by inferring the relationships among performance factors, it allows performing probability analyses on the successful and unsuccessful factors, which can provide further feedback. In conclusion, the framework would improve the national R&D program in terms of financial investment efficiency by aligning budget allocation and performance evaluation.     

A tale of two coalitions – marginalising the users while successfully implementing an enterprise resource planning system

Classic analyses of system implementations view user participation as a key element for successful implementation. However, under some conditions, avoiding user participation offers an alternative route to a successful implementation; this is advisable especially when the user network is weak and aligning user needs with the technological capabilities will take too much resource. To illustrate such situation, we analyse how a successful implementation outcome of an enterprise resource planning (ERP) system emerged in a recently established conglomeration of two previously independent universities. The ERP was used to replace several legacy student administration systems for both political and functional reasons. It was deemed successful by both project consultants and the new university's management while the users were marginalised (‘black boxed’) and left to ‘pick up the pieces’ of an incomplete system using traditional methods such as shadow systems and work‐a‐rounds. Using a process approach and an actor–network theory ‘reading’ of related socio‐technical events, we demonstrate how three networks of actors – management, the project team and the administrative users – collided and influenced the implementation outcome and how the management and project network established the ERP as a reliable ally while at the same time the users – while being enrolled in the network – were betrayed through marginalisation. Our analysis also suggests a useful way to conduct a ‘follow the network’ analysis explaining and accounting for the observed implementation outcome. We illustrate the benefits of using a socio‐technical processual analysis and show how stable actor networks must be constructed during large‐scale information technology change and how different actor groups perceive and influence differently the implementation outcome.     

ARP预算管理信息系统应用探索

随着国家建设和信息产业的不断发展,研究所在发展的同时业务范围也在逐步扩张和多元化,如何在这一过程中实现合理、高效的预算管理和监控,成为研究所预算管理的核心.本项目基于ARP综合财务和科研项目系统,根据研究所以课题为成本中心的核算体制模式,建立ARP预算管理系统,关联科研项目预算与实际支出,通过课题预算录入、报销预算控制及实时预算查询等业务流程,实现事前预算编制、事中预算控制和事后预算分析的预算管理模式,为研究所管理者和科研人员提供预算执行情况、收支明细查询等综合信息服务.     

Critical success factors in software projects

Software projects are still late, over budget, and unpredictable. Sometimes the entire project fails before ever delivering an application. The article presents a clear, commonsense review of fundamental project management techniques which reminds us that we still have a long way to go. It presents five essential factors to managing a successful software project: (1) start on the right foot; (2) maintain momentum; (3) track progress; (4) make smart decisions; (5) institutionalize post-mortem analyses     

A cloud approach to unified lifecycle data management in architecture,engineering, construction and facilities management: Integrating BIMs and SNS

The problem of data integration throughout the lifecycle of a construction project among multiple collaborative enterprises remains unsolved due to the dynamics and fragmented nature of the construction industry. This study presents a novel cloud approach that, focusing on China’s special construction requirements, proposes a series of as-built BIM (building information modeling) tools and a self-organised application model that correlates project engineering data and project management data through a seamless BIM and BSNS (business social networking services) federation. To achieve a logically centralised single-source data structure, a unified data model is constructed that integrates two categories of heterogeneous databases through the adoption of handlers. Based on these models, key technical mechanisms that are critical to the successful management of large amounts of data are proposed and implemented, including permission, data manipulation and file version control. Specifically, a dynamic Generalised List series is proposed to address the sophisticated construction file versioning issue. The proposed cloud has been successfully used in real applications in China. This research work can enable data sharing not only by individuals and project teams but also by enterprises in a consistent and sustainable way throughout the life of a construction project. This system will reduce costs for construction firms by providing effective and efficient means and guides to complex project management, and by facilitating the conversion of project data into enterprise-owned properties.     

Software project management with GAs

A Project Scheduling Problem consists in deciding who does what during the software project lifetime. This is a capital issue in the practice of software engineering, since the total budget and human resources involved must be managed optimally in order to end in a successful project. In short, companies are principally concerned with reducing the duration and cost of projects, and these two goals are in conflict with each other. In this work we tackle the problem by using genetic algorithms (GAs) to solve many different software project scenarios. Thanks to our newly developed instance generator we can perform structured studies on the influence the most important problem attributes have on the solutions. Our conclusions show that GAs are quite flexible and accurate for this application, and an important tool for automatic project management.     

Location-based measurement and visualization for interdependence network on construction sites

Appropriately assigning workers to tasks is vitally important in project management. To do this, project managers need to objectively and effectively measure and visualize the spatiotemporal orders of real construction process as well as coordination structure of the workforce. However, currently there is no method/tool available to project managers to represent spatiotemporal orders of construction processes. To address this issue, this paper presents a novel approach to measuring the real spatiotemporal order of onsite tasks as well as the task interdependence by an interdependence network. This approach extracts the distance of workspace distributions as a key interdependence indicator from historical location tracks across different construction stages according to the area-restricted nature of construction activities. It then integrates generated interdependence into a network over time, to imply the cooperation patterns in stages and a task delivery across stages with a holistic view. To validate the approach, location data were collected from 31 workers working in a high-rise housing construction project for one week to construct the interdependence network of this project, which was used to quantitatively evaluate the performance of construction schedule, assignments and cooperation. Results show that the interdependence network is able to provide insightful information on how workers perform individual tasks onsite and it is also an effective tool to identify and display the interactions among site workers.     

Software effort estimation accuracy prediction of machine learning techniques: A systematic performance evaluation

Software effort estimation accuracy is a key factor in effective planning, controlling, and delivering a successful software project within budget and schedule. The overestimation and underestimation both are the key challenges for future software development, henceforth there is a continuous need for accuracy in software effort estimation. The researchers and practitioners are striving to identify which machine learning estimation technique gives more accurate results based on evaluation measures, datasets and other relevant attributes. The authors of related research are generally not aware of previously published results of machine learning effort estimation techniques. The main aim of this study is to assist the researchers to know which machine learning technique yields the promising effort estimation accuracy prediction in software development. In this article, the performance of the machine learning ensemble and solo techniques are investigated on publicly and non-publicly domain datasets based on the two most commonly used accuracy evaluation metrics. We used the systematic literature review methodology proposed by Kitchenham and Charters. This includes searching for the most relevant papers, applying quality assessment (QA) criteria, extracting data, and drawing results. We have evaluated a state-of-the-art accuracy performance of 35 selected studies (17 ensemble, 18 solo) using mean magnitude of relative error and PRED (25) as a set of reliable accuracy metrics for performance evaluation of accuracy among two techniques to report the research questions stated in this study. We found that machine learning techniques are the most frequently implemented in the construction of ensemble effort estimation (EEE) techniques. The results of this study revealed that the EEE techniques usually yield a promising estimation accuracy than the solo techniques.     

有线电视工程预算系统的设计

工程预算是有线电视工程建设中必须的一项工作。叙述了有线电视工程预算编制的方法和步骤,设计了一种基于Access数据库的有线电视工程预算系统,并介绍了预算系统的设计方案和基本的功能。详细介绍了系统关键表和预算窗体的设计、材料表和机械台班表的数据追加、总计数额的计算、报表的设计和主界面的设计等建立系统所用到的关键技术。实践说明该系统可满足有线电视工程预算工作的需求。     

Local Model and Controller Network Design for a Single-Link Flexible Manipulator

This paper describes a new genetic learning approach to the construction of a local model network (LMN) and design of a local controller network (LCN) with application to a single-link flexible manipulator. A highly nonlinear flexible manipulator system is modelled using an LMN comprising Autoregressive–moving-average model with exogenous inputs (ARMAX) type local models (LMs) whereas linear Proportional-integral-derivative (PID) type local controllers (LCs) are used to design an LCN. In addition to allowing the simultaneous optimisation of the number of LMs and LCs, model parameters and interpolation function parameters, the approach provides a flexible framework for targeting transparency and generalisation. Simulation results confirm the excellent nonlinear modelling properties of an LM network and illustrate the potential benefits of the proposed LM control scheme.     

Information modelling for case-based construction planning of highway bridge projects

In practice, construction planning and control draws on large-scale project and corporate data repositories, which are often unstructured. This article argues that the development of a large-scale data repository should be the precursor to any case-based reasoning system development. The article presents a large number of conceptual object models, which were developed to identify the attributes and relationships between product and planning information comprehensively, using bridges as a representative product. The models were used to develop a large information repository implemented in a database management system to facilitate real world project information collation, organisation, and management to reflect the large-scale nature of construction projects in practice. The database acts as a source of cases and sub-cases that are retrieved and mapped into a case-base. These cases are considered individually for indexing, matching, retrieval, and validation purposes, facilitating the re-use of parts of multiple cases to construct new project plans. A prototype software model, CBRidge Planner, which was developed and tested with real world project cases to demonstrate the approach is presented.     

A model of information systems development project performance

abstract. Performance in information systems development (ISD) projects can be critical to business success. But, while project performance has been the subject of much debate, there has been little empirical research into its determinants. A survey of IS projects in the UK and New Zealand is analysed to test hypotheses concerning performance in terms of both project completion and budget (time-cost) variances. Subsequently, a secondary analysis of the findings is used to build a more complete empirical model of project performance. The paper helps to develop the theory of IS development project performance and also has significant implications for practice. Discussion of the findings highlights the importance of project team dynamics, risk management, senior management support for strategic projects and user participation in ensuring successful IS project performance.     

Optimal 5G network slicing using machine learning and deep learning concepts

Network slicing is predetermined to hold up the diversity of emerging applications with enhanced performance and flexibility requirements in the way of splitting the physical network into numerous logical networks. Consequently, a tremendous data count has been generated with an enormous number of mobile phones due to these applications. This has made remarkable challenges and has a considerable influence on the network slicing performance. This work aims to design an efficient network slicing using a hybrid learning algorithm. Thus, we proposed a model, which involves three main phases: (a) Data collection, (b) Optimal weighted feature extraction (OWFE), and (c) Slicing classification. First, we collected the 5G network slicing dataset, which involves the attributes associated with various network devices like “user device type, duration, packet loss ratio, packet delay budget, bandwidth, delay rate, speed, jitter, and modulation type.” Next, we performed the OWFE, in which a weight function is multiplied with the attribute values to have high scale variation. We optimized this weight function by the hybridization of two meta-heuristic algorithms—glowworm swarm optimization and deer hunting optimization algorithm (DHOA)—and named the proposed model glowworm swarm-based DHOA (GS-DHOA). For the given attributes, we classified the exact network slices like “eMBB, mMTC, and URLLC” for each device by a hybrid classifier using deep belief and neural networks. The weight function of both networks is optimized by the GS-DHOA. The experiment results revealed that the proposed model could influence the provision of accurate 5G network slicing.     

Petrophysical data prediction from seismic attributes using committee fuzzy inference system

This study presents an intelligent model based on fuzzy systems for making a quantitative formulation between seismic attributes and petrophysical data. The proposed methodology comprises two major steps. Firstly, the petrophysical data, including water saturation (S_w) and porosity, are predicted from seismic attributes using various fuzzy inference systems (FISs), including Sugeno (SFIS), Mamdani (MFIS) and Larsen (LFIS). Secondly, a committee fuzzy inference system (CFIS) is constructed using a hybrid genetic algorithms-pattern search (GA-PS) technique. The inputs of the CFIS model are the outputs and averages of the FIS petrophysical data. The methodology is illustrated using 3D seismic and petrophysical data of 11 wells of an Iranian offshore oil field in the Persian Gulf. The performance of the CFIS model is compared with a probabilistic neural network (PNN). The results show that the CFIS method performed better than neural network, the best individual fuzzy model and a simple averaging method.     

Identifying factors affecting software development cost and productivity

Software systems of today are often complex, making development costs difficult to estimate. This paper uses data from 50 projects performed at one of the largest banks in Sweden to identify factors that have an impact on software development cost. Correlation analysis of the relationship between factor states and project costs was assessed using ANOVA and regression analysis. Ten out of the original 31 factors turned out to have an impact on software development project cost at the Swedish bank including the: number of function points, involved risk, number of budget revisions, primary platform, project priority, commissioning body’s unit, commissioning body, number of project participants, project duration, and number of consultants. In order to be able to compare projects of different size and complexity, this study also considers the software development productivity defined as the amount of function points per working hour in a project. The study at the bank indicates that the productivity is affected by factors such as performance of estimation and prognosis efforts, project type, number of budget revisions, existence of testing conductor, presentation interface, and number of project participants. A discussion addressing how the productivity factors relate to cost estimation models and their factors is presented. Some of the factors found to have an impact on cost are already included in estimation models such as COCOMO II, TEAMATe, and SEER-SEM, for instance function points and software platform. Thus, this paper validates these well-known factors for cost estimation. However, several of the factors found in this study are not included in established models for software development cost estimation. Thus, this paper also provides indications for possible extensions of these models.