Construction Management Problems Research Articles

Evolutionary fuzzy decision model for construction management using support vector machine

Construction projects are, by their very nature, challenging; and project decision makers must work successfully within an environment that is frequently complex and fraught with uncertainty. As many decisions must be made intuitively based on limited information, successful decision making depends heavily on two factors, including the experience of the expert(s) involved and the quality of knowledge accumulated from previous experience. Knowledge, however, is subject to various factors that cause its value and accuracy to deteriorate. Research has demonstrated that artificial intelligence has the potential to overcome these factors. The Evolutionary Fuzzy Support Vector Machine Inference Model (EFSIM), an artificial intelligence hybrid system that fuses together fuzzy logic (FL), a support vector machine (SVM) and fast messy genetic algorithm (fmGA), represents an alternative approach to retaining and utilizing experiential knowledge. A fmGA is used as an optimization tool to search simultaneously for fittest membership functions, defuzzification parameter ( dfp) and SVM hyperparameter (herein C and gamma, γ). Two simulations on actual construction management problems demonstrated the EFSIM to be an effective tool for solving various problems in the construction industry.

Этапы экономического развития Республики Азербайджан

In the present article, the experience of market economy construction and macroeconomic management problem solving at different stages of Azerbaijan's development is considered. To assess effectiveness of the economic policy pursued, a comparative approach is used. Key economy characteristics are compared with typical or average criteria applied to groups of countries with economies resembling Azerbaijan in a certain way.

Evolutionary support vector machine inference system for construction management

Problems in construction management are complex, full of uncertainty, and vary based on site environment. Two tools, the fast messy genetic algorithms (fmGA) and support vector machine (SVM), have been successfully applied to solve various problems in construction management. Considering the characteristics and merits of each, this paper combines the two to propose an Evolutionary Support Vector Machine Inference Model (ESIM). In the ESIM, the SVM is primarily employed to address learning and curve fitting, while fmGA addresses optimization. This model was developed to achieve the fittest C and γ parameters with minimal prediction error. This research further integrates the developed ESIM with an object-oriented (OO) computer technique to create an Evolutionary Support Vector Machine Inference System (ESIS). Simulations conducted to demonstrate the robustness of the model in application indicate that ESIS may be used as a multifarious intelligent decision support system in decision-making to help solve a wide range of construction management problems.

SELECTION OF THE EFFECTIVE DWELLING HOUSE WALLS BY APPLYING ATTRIBUTES VALUES DETERMINED AT INTERVALS

The higher life quality standards, the changes of habits and new well‐being requirements have led to an increase in the demand for housing. Decision‐making problems in construction management often involve a complex decision‐making process in which multiple requirements and conditions have to be taken into consideration simultaneously. However, not every attribute used in multidimensional scaling is equally and precisely weighted in the real world. Thus quantitative and qualitative assessments are often required to deal with uncertainty, subjective and imprecise data. The accuracy of performance measures in common multi-attribute methods is usually assumed to be accurate. Grey theory is a new technique for performing prediction, relational analysis and decision‐making in many areas. This paper considers the application of grey relations methodology for defining the utility of an alternative and is proposed as a method of multiple criteria COmplex PRoportional Assessment of alternatives with Grey relations (COPRAS‐G). In this model parameters of the alternatives are determined by the grey relational grade and are expressed in intervals. A case study of assessing external walls of four alternatives was used to demonstrate the applicability and the effectiveness of the proposed approach. The results show that this method can be implemented as an effective decision aid in multi‐attribute selection. Santrauka Straipsnio tikslas – pasiūlyti alternatyvią daugelio rodiklių vertinimo metodiką. Šios metodikos išskirtinumas – rodiklių reikšmės, aprašomos intervalais. Tai labiau atitinka realias aplinkos sąlygas ir galimas reikšmes. Modelis su intervaluose pateiktomis efektyvumo rodiklių reikšmėmis pritaikytas E. K. Zavadsko ir A. Kaklausko sukurtam metodui COPRAS (Complex Proportional Evaluation). Taip gautas naujas metodas COPRAS-G. Šis metodas pritaikytas daugiabučių namų išorinių sienų efektyvumo vertinimo uždaviniui spręsti. Sudaryta efektyvumo rodiklių sistema, nustatyta efektyvumo rodiklių reikšmė, rodiklių rangas. Pagal sudarytą modelį pritaikę COPRAS-G metodą, galime spręsti daugelį statybos, vadybos alternatyvų vertinimo uždavinių.

Constraint-based clustering and its applications in construction management

Both mixed data types and cluster constraints are frequently encountered in the classification problems of construction management. For example, in a bridge let project, engineers generally group the bridges into several subgroups based on their proximities, structure type, material, etc. Moreover, constraints may be set for each cluster to ensure the project’s overall effectiveness. In this study, an effective clustering algorithm – the constrained k-prototypes (CKP) algorithm – is proposed to resolve the abovementioned problems. Several tests and experimental results have shown that CKP cannot only handle mixed data types but also satisfy user-specified constraints. In order to demonstrate the applicability of CKP, it is also applied to real-world problems in construction management.

Optimizing Material Hoisting Operations and Storage Cells in Single Multi-storey Tower Block Construction by Genetic Algorithm

During construction, materials have to be stored properly and transported within a site area or even different storeys in a high-rise structure. The total costs for storage and transportation are prime concerns. Optimizing material storage locations so as to pay the least transportation costs induced during the project period is a typical construction management problem. In this paper, a genetic algorithm (GA) approach to optimize the vertical hoisting and storage layout for a high-rise multi-storey tower building in confined sites is studied. Costs include the storage and distribution costs which are calculated in terms of vertical transportation, horizontal movements and then vertical distribution expenses. The sum of these costs is set as the objective function for optimization. Since it is a typical multiple-level warehouse layout and distribution problem, which is NP-hard in nature, an effective assignment system with GA approach is applied. An example of 10 types of materials to be stored and distributed in a 30-storey building is used as a numerical example to illustrate the model development. The results also demonstrate the effectiveness of applying the GA in minimizing the total transportation costs that verify the practical application of the proposed approach.

Systematic Evaluation of Construction Equipment Alternatives: Case Study

Selecting the right equipment for the project is inherently a multifaceted cost and benefit evaluation process that is further compounded by the complexity of today’s building projects and the lack of systematic tools for the consideration of soft factors. This paper presents a detailed application example of a model based on an analytical hierarchy process (AHP) approach. This model was developed to address the difficulties experienced during the multifaceted process. The example illustrates how an AHP-based model helps address the multitude of qualitative, intangible factors, both among the factors themselves and then vis-à-vis costs, by means of a systematic and traceable process. The method enables project managers and their equipment selection teams to exercise their knowledge, intuition, and professional judgment, and at the same time to address the context and specifics of the particular projects under examination. The example should be helpful for construction practitioners dealing with similar equipment selection issues. Researchers may find interest in the implementation of a multiattribute-decision-making method for a typical construction management problem.

Object-Oriented Evolutionary Fuzzy Neural Inference System for Construction Management

Problems in construction management are complex, full of uncertainty, and vary with environment. Fuzzy logic, neural networks, and genetic algorithms ~GAs! have been successfully applied in construction management to solve various kinds of problems. Considering the characteristics and merits of each method, this paper combines the above three techniques to develop an Evolutionary Fuzzy Neural Inference Model ~EFNIM!. Integrating these three methods, the EFNIM uses GAs to simultaneously search for the fittest membership functions with the minimum fuzzy neural network ~FNN! structure and optimum parameters of FNN. Thus, the best adaptation mode is automatically identified. Furthermore, this research work integrates the EFNIM with an object-oriented ~OO! computer technique to develop an OO Evolutionary Fuzzy Neural Inference System for solving construction management problems. Simulations are conducted to demonstrate the application potential of the EFNIS. This system could be used as a multifarious intelligent decision support system for decision-making to solve manifold construction management problems.

Undergraduate Research Mentoring Model in Construction Engineering and Management

The construction industry is a major player in the nation's economy. The complex nature of the construction industry, coupled with the challenges of global competitiveness and changing regulatory requirements, has created the need for providing higher levels of education, experience, and training for construction professionals. An essential and integral component of the required education and training must be the research training of undergraduate civil and construction students, encouraging them to pursue advanced education and research careers in this area. With this in mind, the writer developed a Research Experiences for Undergraduates ~REU! summer program at Western Michigan University that focuses on construction engineering and management issues and problems. This construction-oriented undergraduate research training program is the first REU site in the United States to be funded by the National Science Foundation. This paper will describe the structure of the REU program, the types of activities undertaken by the REU participants, and the results of the program evaluation and assessment.

Hybrid use of AI techniques in developing construction management tools

Problems in construction management are complex, full of uncertainty, and vary with environment. Fuzzy logic (FL), neural networks (NNs), and genetic algorithms (GAs) have been successfully applied in construction management to solve various kinds of problems. Considering the characteristics and merits of each method, this paper combines the above three techniques to develop an Evolutionary Fuzzy Neural Inference Model (EFNIM). Integrating these three methods, the EFNIM uses GAs to simultaneously search for the fittest membership functions (MFs) with the minimum fuzzy neural network (FNN) structure and optimum parameters of FNN. Furthermore, this research work proposes an object-oriented (OO) system development process to integrate the EFNIM with OO computer technique to develop an OO Evolutionary Fuzzy Neural Inference System (OO-EFNIS) for solving construction management problems. Simulations are conducted to demonstrate the application potential of the EFNIS. This system could be used as multifarious intelligent decision support system for decision-making to solve manifold construction management problems.

Factors influencing construction time and cost overruns on high-rise projects in Indonesia

Many variables have an impact upon construction time and cost overruns in Indonesia. A questionnaire survey was undertaken of project managers working on high-rise construction projects in two Indonesian cities: Jakarta and Yogyakarta. The variables identified were ranked according to their perceived importance and frequencies of occurrence. Inflationary increases in material cost, inaccurate material estimating and project complexity are the main causes of cost overruns. The predominant causes of delay are design changes, poor labour productivity and inadequate planning. Using factor analysis techniques, delay and cost overrun variables were grouped into factors, and their relationships analysed. Although Indonesia specific, the results reflect construction management problems common to developing countries.

Neural Network–Spreadsheet Integration for Earth‐Moving Operations

Abstract: This paper examines the potential to combine trained artificial neural networks with application development software to expand their applicability to construction management problems. The problem of determining optimal equipment combinations for the dozer/scraper earth‐moving team, under various physical conditions of the haul road, was chosen for its limited scope and lack of effective solution methods currently available. An introduction to the problem provides the current state of technology for handling this problem. The problem is analyzed to determine the information and resource requirements as well as the input and output forms that would be desirable for such a program. The paper proceeds through the design process and development of the program schematic. Programming of the system is examined, including the development process used to train the neural network, conversion of the network to a spreadsheet form, and spreadsheet manipulation of the network outputs. The paper concludes with recommendations for expansion of the program for more complex haul road conditions and equipment types.

Construction risk management

Abstract Today's construction industry involves more dynamic and uncertain planning than ever before. To approach complex problems in construction management, decision-makers should follow a systematic and professional approach in risk management. This paper explores the application of knowledge-base and database systems, and fuzzy sets in construction risk management. An integrated knowledge system is presented.

Project Scheduling Using Fuzzy Set Concepts

Probabilistic methods are being used increasingly in construction engineering. However, when a parameter is expressed in linguistic rather than mathematical terms, classical probability theory fails to incorporate the information. The linguistic variables can be translated into mathematical measures using fuzzy set and system theory. A construction management problem, i.e., estimation of the duration of an activity, is solved using this theory. In order to implement the proposed technique, various membership functions need to be estimated using judgment or with the assistance of experts. The proposed technique is not sensitive to small variations in the membership values. This is a very desirable property. However, the method is sensitive to the choice of the fuzzy relations. The uncertainty in the fuzzy relations can be modeled along with other sources of uncertainty. The mean and variance of the parameters involved in the problem under consideration are estimated here using a new method. The method maxi...

Marketing Construction Management Services

A survey of ninety-five construction management firms and two hundred twenty-two construction management clients was undertaken to assess marketing practices and preferences. Forty-nine firms and forty-six clients responded to the survey. An analysis of the firms' responses indicated that the most important determinent of contract awards was the amount of general experience of the firm. Clients stressed the importance of the quality of services provided by the firm. Clients desired more information to assess the quality of the firm's proposed project manager. A marketing model is developed to assist construction management firms in positioning their marketing efforts. The model focuses upon the project manager, the project team, and requires that the firm include information to enable the clients to evaluate the firm's flexibility of their problem solutions to the clients' construction management problems.