Value engineering for drainage and stream way in roads and highways construction

Value Engineering (VE) is a total management approach to improve the quality of construction projects. It increases the efficiency and performance of the projects to gain the best integrated benefits. The VE focuses on function analysis of the researched subjects and strives to achieve the required function reliably at the lowest Life Cycle Cost (LCC). It seeks optimizing and improving decision making to realize the optimal expenditure of owner funds while meeting required function. The VE teamwork involving construction, design and maintenance staff reviewed the construction project features and acquire for ways to improve quality, control costs and time. This study focused on investigating the role of VE for existing main road construction projects. It uses the Drainage Engineering Systems (DES) and Surface Stream Way Drain (SSWD) after rainfall in the environmental health view point related to VE. It predicates to decrease the air pollution and increasing the health of environment. The main goal of this study is to design an enhanced VE framework with main factors of drainage management in the main road. In this study, VE questionnaire was sought to determine the overall thoughts, vistas, notion, comprehending and understanding in addition to the connection to LCC price for drainage and runoff of main roads, highways and streets. The quantitative data were analyzed using one-way ANOVA technique and Factor Analysis of smart PLS. The expert respondent provides scientific data, on the initial questionnaire with thirty perfect answers. The qualitative data was used to support the quantitative results to provide a mathematical framework between the twelve important main factors of VE, DES and SSWD related to the factors of Construction Management (CM), Materials (M), Environment (E), Human Resource (HR), Quality (Q), Aesthetic (A), Cost (C), Time (T), Waste Materials (WM), Safety and Safety Driving (S and SD) and Recycling (R). The findings revealed that the VE by working team can increase performance and increase runoff collection of main roads, highways and streets. The framework also decrease within the lowest possible cost, time, waste materials and increase possible quality, aesthetic, safety driving and most possibly can effect construction management, materials, recycling, human resource and environment. The new framework of VE accepts all twelve main factors with only aesthetics factor being rejected. The new VE framework is capable to save cost, time and increase quality of road drainage system

A decision support system for the selection of green roof for residential buildings

Green roofs have been installed as a sustainable approach for many years all around the world. There are myriad benefits for green roof installation in terms of private and public sectors such as energy saving, stormwater management, and carbon reduction. Furthermore, there are three types of green roofs with different levels of benefits and costs; however, there is lack of model, framework, or decision support system (DSS) to facilitate the process of decision making for selecting the optimum type of green roof. The aim of the research is to develop a DSS to determine the optimum type of green roof. The research was conducted on residential buildings due to the highest percentage of green roof installation among other building categories in Malaysia. Enhanced Fuzzy Delphi Method (EFDM) has been developed for this study as the approach for data collection, while Multi-Criteria Decision Making (MCDM) is adopted in order to develop the DSS. Moreover, Cybernetic Fuzzy Analytic Hierarchy Process (CFAHP) was also developed as the method used in MCDM. EFDM and CFAHP were developed due to the shortcomings of previous methods for the novelty in this research. A database was created for the DSS using EFDM, while CFAHP method was used for developing the DSS. Additionally, in terms of DSS evaluation, hypothetical examples were defined and after obtaining the results, multiple criteria approach was conducted to understand its level of effectiveness and efficiency. DSS evaluation has been conducted involving experts in the field of green roof. Finally, it was concluded that the DSS works well and can be utilized in construction industry in the design phase. The experts’ feedbacks showed that the developed DSS is effective and efficient, and were satisfied with the performance of the DSS

Identifying and prioritizing cost reduction solutions in the supply chain by integrating value engineering and gray multi-criteria decision-making

Value engineering is an appropriate policy for creating and improving value, which reduces unnecessary costs and maintains core functionality. Despite the mentioned benefits, this approach has so far received little attention in the area of supply chain management. Although this approach is highly structured, limitations such as overemphasizing the cost criterion and failure to meet other criteria, utilizing team members’ votes to rank solutions, ignoring inherent uncertainty and ultimately disagreement between value engineering team members have reduced the effectiveness of this approach. The present study aims to provide a coherent framework for utilizing a value engineering approach to supply chain cost management and overcome the aforementioned limitations by utilizing gray multi-criteria decision-making. In this regard, in the first phase, the initial list of improvement solutions is determined, the criteria extracted from the literature are localized using value engineering team members’ opinion. These criteria are weighted using the gray stepwise weight assessment ratio analysis (SWARA-Gray) method. Then, the score of each solution is calculated by the value engineering team based on the list of criteria as a gray number. The scores are aggregated using the gray evaluation based on distance from average solution (EDAS-Gray) method, and the solutions are prioritized. Finally, the application of the proposed framework is investigated in a real case study in a power plant in Iran. The results of the research show that the final rankings of the solutions rarely changed for different methods; so the model used in this study has acceptable stability. First published online 24 September 202

A case-based reasoning system for radiotherapy treatment planning for brain cancer

In this thesis, a novel case-based reasoning (CBR) approach to radiotherapy treatment planning for brain cancer patients is presented. In radiotherapy, tumour cells are destroyed using ionizing radiation. For each patient, a treatment plan is generated that describes how the radiation should be applied in order to deliver a tumouricidal radiation dose while avoiding irradiation of healthy tissue and organs at risk in the vicinity of the tumour. The traditional, manual trial and error approach is a time-consuming process that depends on the experience and intuitive knowledge of medical physicists. CBR is an artificial intelligence methodology, which attempts to solve new problems based on the solutions of previously solved similar problems. In this research work, CBR is used to generate the parameters of a treatment plan by capturing the subjective and intuitive knowledge of expert medical physicists stored intrinsically in the treatment plans of similar patients treated in the past. This work focusses on the retrieval stage of the CBR system, in which given a new patient case, the most similar case in the archived case base is retrieved along with its treatment plan. A number of research issues that arise from using CBR for radiotherapy treatment planning for brain cancer are addressed. Different approaches to similarity calculation between cases are investigated and compared, in particular, the weighted nearest neighbour similarity measure and a novel non-linear, fuzzy similarity measure designed for our CBR system. A local case attribute weighting scheme has been developed that uses rules to assign attribute weights based on the values of the attributes in the new case and is compared to global attribute weighting, where the attribute weights remain constant for all target cases. A multi-phase case retrieval approach is introduced in which each phase considers one part of the solution. In addition, a framework developed for the imputation of missing values in the case base is described. The research was carried out in collaboration with medical physicists at the Nottingham University Hospitals NHS Trust, City Hospital Campus, UK. The performance of the developed methodologies was tested using brain cancer patient cases obtained from the City Hospital. The results obtained show that the success rate of the retrieval mechanism provides a good starting point for adaptation, the next phase in development for the CBR system. The developed automated CBR system will assist medical physicists in quickly generating treatment plans and can also serve as a teaching and training aid for junior, inexperienced medical physicists. In addition, the developed methods are generic in nature and can be adapted to be used in other CBR or intelligent decision support systems for other complex, real world, problem domains that highly depend on subjective and intuitive knowledge

A case-based reasoning system for radiotherapy treatment planning for brain cancer

In this thesis, a novel case-based reasoning (CBR) approach to radiotherapy treatment planning for brain cancer patients is presented. In radiotherapy, tumour cells are destroyed using ionizing radiation. For each patient, a treatment plan is generated that describes how the radiation should be applied in order to deliver a tumouricidal radiation dose while avoiding irradiation of healthy tissue and organs at risk in the vicinity of the tumour. The traditional, manual trial and error approach is a time-consuming process that depends on the experience and intuitive knowledge of medical physicists. CBR is an artificial intelligence methodology, which attempts to solve new problems based on the solutions of previously solved similar problems. In this research work, CBR is used to generate the parameters of a treatment plan by capturing the subjective and intuitive knowledge of expert medical physicists stored intrinsically in the treatment plans of similar patients treated in the past. This work focusses on the retrieval stage of the CBR system, in which given a new patient case, the most similar case in the archived case base is retrieved along with its treatment plan. A number of research issues that arise from using CBR for radiotherapy treatment planning for brain cancer are addressed. Different approaches to similarity calculation between cases are investigated and compared, in particular, the weighted nearest neighbour similarity measure and a novel non-linear, fuzzy similarity measure designed for our CBR system. A local case attribute weighting scheme has been developed that uses rules to assign attribute weights based on the values of the attributes in the new case and is compared to global attribute weighting, where the attribute weights remain constant for all target cases. A multi-phase case retrieval approach is introduced in which each phase considers one part of the solution. In addition, a framework developed for the imputation of missing values in the case base is described. The research was carried out in collaboration with medical physicists at the Nottingham University Hospitals NHS Trust, City Hospital Campus, UK. The performance of the developed methodologies was tested using brain cancer patient cases obtained from the City Hospital. The results obtained show that the success rate of the retrieval mechanism provides a good starting point for adaptation, the next phase in development for the CBR system. The developed automated CBR system will assist medical physicists in quickly generating treatment plans and can also serve as a teaching and training aid for junior, inexperienced medical physicists. In addition, the developed methods are generic in nature and can be adapted to be used in other CBR or intelligent decision support systems for other complex, real world, problem domains that highly depend on subjective and intuitive knowledge