scholarly journals FORECASTING OF SPORTS FIELDS CONSTRUCTION COSTS AIDED BY ENSEMBLES OF NEURAL NETWORKS

2019 ◽  
Vol 25 (7) ◽  
pp. 715-729 ◽  
Author(s):  
Michał Juszczyk ◽  
Krzysztof Zima ◽  
Wojciech Lelek
Keyword(s):  
Neural Networks ◽  
Cost Analysis ◽  
Construction Projects ◽  
Practical Implementation ◽  
Ensemble Averaging ◽  
Construction Costs ◽  
Different Types ◽  
Selection Of ◽  
Sports Fields

The paper presents an original approach to construction cost analysis and development of predictive models based on ensembles of artificial neural networks. The research was focused on the application of two alternative approaches of ensemble averaging that allow for combining a number of multilayer perceptron neural networks and developing effective models for cost predictions. The models have been developed for the purpose of forecasting construction costs of sports fields as a specific type of construction objects. The research included simulation and selection of numerous neural networks that became the members of the ensembles. The ensembles included either the networks of different types in terms of their structure and activation functions or the networks of the same type. The research also included practical implementation of the developed models for cost analysis based on a sports field BIM model. This case study examined and confirmed all of the four models’ predictive capabilities and superiority over models based on single networks for the particular problem. Verification including testing and the case study enabled selection of the best ensemble-based model that combined ten networks of different types. The proposed approach is prospective for fast cost analyses and conceptual estimates in construction projects.

scholarly journals ANN Based Approach for Estimation of Construction Costs of Sports Fields

Complexity ◽  
2018 ◽  
Vol 2018 ◽  
pp. 1-11 ◽  
Author(s):  
Michał Juszczyk ◽  
Agnieszka Leśniak ◽  
Krzysztof Zima
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Cost Estimation ◽  
Construction Projects ◽  
Construction Costs ◽  
General Applicability ◽  
Artificial Neural ◽  
Multilayer Perceptron Networks ◽  
Cost Of Construction ◽  
Sports Fields

Cost estimates are essential for the success of construction projects. Neural networks, as the tools of artificial intelligence, offer a significant potential in this field. Applying neural networks, however, requires respective studies due to the specifics of different kinds of facilities. This paper presents the proposal of an approach to the estimation of construction costs of sports fields which is based on neural networks. The general applicability of artificial neural networks in the formulated problem with cost estimation is investigated. An applicability of multilayer perceptron networks is confirmed by the results of the initial training of a set of various artificial neural networks. Moreover, one network was tailored for mapping a relationship between the total cost of construction works and the selected cost predictors which are characteristic of sports fields. Its prediction quality and accuracy were assessed positively. The research results legitimatize the proposed approach.

scholarly journals Modelling Construction Site Cost Index Based on Neural Network Ensembles

Symmetry ◽  
2019 ◽  
Vol 11 (3) ◽  
pp. 411 ◽  
Author(s):  
Michał Juszczyk ◽  
Agnieszka Leśniak
Keyword(s):  
Neural Networks ◽  
Cost Estimation ◽  
Construction Projects ◽  
Construction Site ◽  
Ensemble Averaging ◽  
Construction Costs ◽  
Cost Index ◽  
Use Of Data ◽  
Neural Network Ensembles ◽  
Accuracy Of Prediction

Construction site overhead costs are key components of cost estimation in construction projects. The estimates are expected to be accurate, but there is a growing demand to shorten the time necessary to deliver cost estimates. The balancing (symmetry) between time of calculation and satisfaction of reliable estimation was the reason for developing a new model for cost estimation in construction. This paper reports some results from the authors’ broad research on the modelling processes in engineering related to estimation of construction costs using artificial intelligence tools. The aim of this work was to develop a model capable of predicting a construction site cost index that would benefit from combining several artificial neural networks into an ensemble. Combining selected neural networks and forming the ensemble-based models compromised their strengths and weaknesses. With the use of data including training patterns collected on the basis of studies of completed construction projects, the authors investigated various types of neural networks in order to select the members of the ensemble. Finally, three models that were assessed in terms of performance and prediction quality were proposed. The results revealed that the developed models based on ensemble averaging and stacked generalisation met the expectations of knowledge generalisation and accuracy of prediction of site overhead cost index. The proposed models offer predictions of cost in an accepted error range and prove to deliver better predictions than those based on single neural networks. The developed tools can be used in the decision-making process regarding construction cost estimation.

DEVELOPMENT OF FRAMEWORK FOR BUILDING MATERIALS-RELATED DECISIONS: A CASE STUDY

2020 ◽  
Vol 7 (2) ◽  
Author(s):  
Don Amila Sajeevan Samarasinghe
Keyword(s):  
Supply Chain ◽  
Construction Projects ◽  
Building Materials ◽  
Residential Buildings ◽  
Survey Methods ◽  
Materials Management ◽  
Construction Costs ◽  
Management Processes ◽  
Collaborative Efforts

Building materials occupy a large proportion of construction costs, comprising of nearly 50%, although the exact percentage varies from project to project. Given how important building materials are, due attention must be given to the strategies for procuring them. This study investigates building material purchasing practices and examines significant factors that could impact the optimum building materials for a specific project selection. This paper is an outcome of a PhD study conducted to improve supply chain practices relating to building materials for residential buildings in New Zealand in such a way that delivers the highest possible value to all stakeholders. The broader PhD study employed both qualitative (subject matter expert interviews) and quantitative (questionnaire survey) methods to gather information from those who supply and manufacture building materials, architects, builders, and homeowners. It found that the facilitation of effective materials management processes is reliant on the collaborative efforts of the entire supply chain in any construction project. When determining “best buy” decisions for key material inputs, the roles of the contractor, clients, and suppliers cannot be disconnected. Decisions relating to building materials were categorized into demand-side or supply-side choices, and a framework was developed to support supply chain stakeholder decisions in selecting appropriate materials for residential construction projects.

Decision-Making for the Selection of Suppliers Based on the Process Quality Assessment

2020 ◽  
Vol 27 (06) ◽  
pp. 2050016
Author(s):  
Kuen-Suan Chen ◽  
Chiao-Tzu Huang ◽  
Tsang-Chuan Chang
Keyword(s):  
Supplier Selection ◽  
Pairwise Comparison ◽  
Process Quality ◽  
Index Formula ◽  
Practical Implementation ◽  
Sample Data ◽  
Index Estimation ◽  
Selection Of

Supplier selection is a practical problem in supply chain management and quality is the most important criterion in supplier selection. In this study, we developed a supplier selection model based on process quality, in which the Six Sigma quality index [Formula: see text] is used as a tool to assess the process quality provided by suppliers. Note that index estimation based on sample data is prone to uncertainty in the assessment of process quality. Therefore, we derived the confidence interval of [Formula: see text] via mathematical programming to reduce the likelihood of assessment miscalculations, and then used this interval to perform a pairwise comparison of suppliers. Our goal was to identify criteria that can be used to select the optimal suppliers for long-term collaborations and sustainable partnerships. A case study is also presented to demonstrate the practical implementation of the proposed method.

EPILEPTIC SEIZURES IN A DISCRETE MODEL OF NEURAL NETWORKS OF THE BRAIN

1999 ◽  
Vol 10 (05) ◽  
pp. 815-821 ◽  
Author(s):  
DANIEL VOLK
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Discrete Model ◽  
Epileptic Seizures ◽  
Inhibitory Neurons ◽  
Population Activity ◽  
Different Types ◽  
The Brain ◽  
Global Activity ◽  
Selection Of

A discrete model of a neural network of excitatory and inhibitory neurons is presented which yields oscillations of its global activity. Different types of dynamics occur depending on the selection of parameters: oscillating population activity as well as randomly fluctuating but mainly constant activity. For certain sets of parameters the model also shows temporary transitions from apparently random to periodic behavior in one run, similar to an epileptic seizure.

scholarly journals Non-Specific Binding and Cross-Reaction of ELISA: A Case Study of Porcine Hemoglobin Detection

Foods ◽  
2021 ◽  
Vol 10 (8) ◽  
pp. 1708
Author(s):  
Xingyi Jiang ◽  
Meng Wu ◽  
Jonathan Albo ◽  
Qinchun Rao
Keyword(s):  
Specific Binding ◽  
Sandwich Elisa ◽  
Cross Reaction ◽  
Different Types ◽  
Control Food ◽  
Secondary Antibodies ◽  
Food Safety And Quality ◽  
Porcine Hemoglobin ◽  
Selection Of

Different types of enzyme-linked immunosorbent assays (ELISA) have been widely used to control food safety and quality. To develop an accurate and reproducible ELISA, false immunodetection results caused by non-specific binding (NSB) and cross-reaction must be prevented. During the case study of sandwich ELISA development for the detection of porcine hemoglobin (PHb), several critical factors leading to NSB and cross-reaction were found. First, to reduce the NSB of the target analyte, the selection of microplate and blocker was discussed. Second, cross-reactions between enzyme-labeled secondary antibodies and sample proteins were demonstrated. In addition, the function of (3-aminopropyl)triethoxysilane (APTES) was evaluated. Overall, this study highlights the essence of both antibody and assay validation to minimize any false-positive/negative immunodetection results.

scholarly journals Optimal Construction Scheme for Controlling Construction Errors of a Cable-strut Tensile Structure

2016 ◽  
Vol 10 (1) ◽  
pp. 751-758 ◽  
Author(s):  
Chen Lian-meng ◽  
Deng Hua ◽  
Cui Yu-hong ◽  
Zhou Yi-yi
Keyword(s):  
Construction Projects ◽  
Stochastic Theory ◽  
Statistical Characteristics ◽  
Construction Costs ◽  
Construction Scheme ◽  
Fundamental Relationship ◽  
And Control ◽  
The Relationship ◽  
Optimal Construction

In a real cable-strut tensile structure, construction errors are inevitable. To explore the optimal construction scheme and control construction errors, this study optimized the construction scheme of a cable-strut tensile structure. First, a mathematical model of the element length error was investigated based on stochastic theory. By combining the balance equation, geometric equation and physical equation, the fundamental relationship between the pre-stress deviation and element length error was derived. Because the pre-stress in the active cable can be controlled exactly and the cable’s pre-stress deviations were zero during construction, the relationship between the pre-stress deviation of a passive cable and the element length error was obtained. Then, the statistical characteristics of the pre-stress deviation were obtained under different construction schemes using statistical theory. Finally, an example was analysed as a case study. The study showed that different elements have different error sensitivities and that different construction schemes have different error effects. Using the method proposed in this paper, the error effect of different construction schemes can be analysed, and the optimal construction scheme, with lower error effects and lower construction costs, can be selected for actual construction projects.

scholarly journals NEW GROUP DECISION-MAKING ARCAS APPROACH BASED ON THE INTEGRATION OF THE SWARA AND THE ARAS METHODS ADAPTED FOR NEGOTIATIONS

2017 ◽  
Vol 18 (4) ◽  
pp. 599-618 ◽  
Author(s):  
Dragisa STANUJKIC ◽  
Edmundas Kazimieras ZAVADSKAS ◽  
Darjan KARABASEVIC ◽  
Zenonas TURSKIS ◽  
Violeta KERŠULIENĖ
Keyword(s):  
Decision Making ◽  
Group Decision Making ◽  
Operation Research ◽  
Group Decision ◽  
Negotiation Process ◽  
Decision Makers ◽  
Practical Implementation ◽  
The One ◽  
Selection Of

Groups are generally considered to be more effective as compared to single individuals. The practical implementation of Operation Research methods in group negotiations needs simple contexts and clear cause-and-effect relationships easily discernible by everyone. This paper proposes a multi-criteria group decision-making approach allowing decision makers/experts involved in a negotiation process to better express and defend their preferences in the selection of the best alternative. In the proposed approach, the most appropriate alternative is the alternative with the largest number of appearances in the first position or in ranking lists, or the one determined based on negotiations of decision makers/experts. The proposed ARCAS approach is based on the use of the ARAS method, a new normalization procedure, and the SWARA method. In the proposed approach, each decision maker/expert involved in evaluation has an opportunity to set the preferred level of rating for each criterion used in such evaluation. Finally, a case study is presented in order to highlight the proposed approach. The obtained results confirm the usability and efficiency of the proposed approach.

scholarly journals Causes of Problems in Post-Disaster Emergency Re-Construction Projects—Iraq as a Case Study

2021 ◽  
pp. 1087724X2199003
Author(s):  
Rasha A. Waheeb ◽  
Bjørn S. Andersen
Keyword(s):  
Construction Projects ◽  
Weather Factors ◽  
Cost Overruns ◽  
Study Approach ◽  
Security Issues ◽  
Statistical Relationships ◽  
Project Data ◽  
Post Disaster ◽  
Selection Of

This study examines the causes of time delays and cost overruns in a selection of thirty post-disaster reconstruction projects in Iraq. Although delay factors have been studied in many countries and contexts, little data exists from countries under the conditions characterizing Iraq during the last 10-15 years. A case study approach was used, with thirty construction projects of different types and sizes selected from the Baghdad region. Project data was gathered from a survey which was used to build statistical relationships between time and cost delay ratios and delay factors in post disaster projects. The most important delay factors identified were contractor failure, redesigning of designs/plans and change orders, security issues, selection of low-price bids, weather factors, and owner failures. Some of these are in line with findings from similar studies in other countries and regions, but some are unique to the Iraqi project sample, such as security issues and low-price bid selection. While many studies have examined factors causing delays and cost overruns, this study offers unique insights into factors that need to be considered when implementing projects for post disaster emergency reconstruction in areas impacted by wars and terrorism.

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