| 1. |
- Chen, Shiwei, et al.
(författare)
-
A Discrete Event Simulation-Based Analysis of Precast Concrete Supply Chain Strategies Considering Suppliers’ Production and Transportation Capabilities
- 2019
-
Ingår i: ICCREM 2019. - Reston, VA : American Society of Civil Engineers (ASCE). ; , s. 12-24
-
Konferensbidrag (refereegranskat)abstract
- The production and transportation capabilities of a precast concrete (PC) component supplier have great impact on the construction of a PC building project. In China, the production and transportation capabilities of different PC suppliers can vary greatly, which will influence contractors’ selection of PC supply chain strategies. However, previous studies often considered the capabilities of PC suppliers to be ideal and failed to compare different PC supply chain strategies under different levels of suppliers capabilities. This study collects detailed data from a PC building project and uses discrete event simulation (DES) to compare different supply chain strategies under different production and transportation capability levels of PC suppliers. Construction duration, construction cost, and greenhouse gas emissions are selected as indicators to compare three different supply chain strategies: just-in-time, on-site storage, and off-site storage. The strengths and weaknesses of each strategy under different capabilities of PC suppliers are found. The results provides guidance for contractors in selecting supply chain strategies when considering PC suppliers’ production and transportation capabilities.
|
|
| 2. |
- Feng, Kailun, 1991-, et al.
(författare)
-
A predictive environmental assessment method for construction operations : Application to a Northeast China case study
- 2018
-
Ingår i: Sustainability. - : MDPI. - 2071-1050. ; 10:11
-
Tidskriftsartikel (refereegranskat)abstract
- Construction accounts for a considerable number of environmental impacts, especially in countries with rapid urbanization. A predictive environmental assessment method enables a comparison of alternatives in construction operations to mitigate these environmental impacts. Process-based life cycle assessment (pLCA), which is the most widely applied environmental assessment method, requires lots of detailed process information to evaluate. However, a construction project usually operates in uncertain and dynamic project environments, and capturing such process information represents a critical challenge for pLCA. Discrete event simulation (DES) provides an opportunity to include uncertainty and capture the dynamic environments of construction operations. This study proposes a predictive assessment method that integrates DES and pLCA (DES-pLCA) to evaluate the environmental impact of on-site construction operations and supply chains. The DES feeds pLCA with process information that considers the uncertain and dynamic environments of construction, while pLCA guides the comprehensive procedure of environmental assessment. A DES-pLCA prototype was developed and implemented in a case study of an 18-storey building in Northeast China. The results showed that the biggest impact variations on the global warming potential (GWP), acidification potential (AP), eutrophication (EP), photochemical ozone creation potential (POCP), abiotic depletion potential (ADP), and human toxicity potential (HTP) were 5.1%, 4.1%, 4.1%, 4.7%, 0.3%, and 5.9%, respectively, due to uncertain and dynamic factors. Based on the proposed method, an average impact reduction can be achieved for these six indictors of 2.5%, 21.7%, 8.2%, 4.8%, 32.5%, and 0.9%, respectively. The method also revealed that the material wastage rate of formwork installation was the most crucial managing factor that influences global warming performance. The method can support contractors in the development and management of environmentally friendly construction operations that consider the effects of uncertainty and dynamics.
|
|
| 3. |
- Feng, Kailun, 1991-, et al.
(författare)
-
An Integrated Environment–Cost–Time Optimisation Method for Construction Contractors Considering Global Warming
- 2018
-
Ingår i: Sustainability. - : MDPI. - 2071-1050. ; 10:11
-
Tidskriftsartikel (refereegranskat)abstract
- Construction contractors play a vital role in reducing the environmental impacts during the construction phase. To mitigate these impacts, contractors need to develop environmentally friendly plans that have optimal equipment, materials and labour configurations. However, construction plans with optimal environment may negatively affect the project cost and duration, resulting in dilemma for contractors on adopting low impacts plans. Moreover, the enumeration method that is usually used needs to assess and compare the performances of a great deal of scenarios, which seems to be time consuming for complicated projects with numerous scenarios. This study therefore developed an integrated method to efficiently provide contractors with plans having optimal environment-cost-time performances. Discrete-event simulation (DES) and particle swarm optimisation algorithms (PSO) are integrated through an iterative loop, which remarkably reduces the efforts on optimal scenarios searching. In the integrated method, the simulation module can model the construction equipment and materials consumption; the assessment module can evaluate multi-objective performances; and the optimisation module fast converges on optimal solutions. A prototype is developed and implemented in a hotel building construction. Results show that the proposed method greatly reduced the times of simulation compared with enumeration method. It provides the contractor with a trade-off solution that can average reduce 26.9% of environmental impact, 19.7% of construction cost, and 10.2% of project duration. The method provides contractors with an efficient and practical decision support tool for environmentally friendly planning.
|
|
| 4. |
- Feng, Kailun, 1991-, et al.
(författare)
-
Assessing environmental performance in early building design stage : An integrated parametric design and machine learning method
- 2019
-
Ingår i: Sustainable cities and society. - : Elsevier. - 2210-6707. ; 50
-
Tidskriftsartikel (refereegranskat)abstract
- Decisions made at early design stage have major impacts on buildings’ life-cycle environmental performance. However, when only a few parameters are determined in early design stages, the detailed design decisions may still vary significantly. This may cause same early design to have quite different environmental impacts. Moreover, default settings for unknown detailed design parameters clearly cannot cover all possible variations in impact, and Monte Carlo analysis is sometimes not applicable as parameters’ probability distributions are usually unknown. Thus, uncertainties about detailed design make it difficult for existing environmental assessment methods to support early design decisions.Thus, this study developed a quantitative method using parametric design technology and machine learning algorithms for assessing buildings’ environmental performance in early decision stages, considering uncertainty associated with detailed design decisions. The parametric design technology creates design scenarios dataset, then associated environmental performances are assessed using environmental assessment databases and building performance simulations. Based on the generated samples, a machine learning algorithm integrating fuzzy C-means clustering and extreme learning machine extracts the case-specific knowledge regarding designed buildings’ early design associated with environmental uncertainty. Proposed method is an alternative but more generally applicable method to previous approaches to assess building's environmental uncertainty in early design stages.
|
|
| 5. |
- Feng, Kailun, 1991-, et al.
(författare)
-
Embedding Ensemble Learning into Construction Optimisation : A Computational Reduction Approach
- 2020
-
Tidskriftsartikel (refereegranskat)abstract
- Simulation-based optimisation (SO), which combines simulation and optimisation technologies, is a popular approach for construction planning optimisation. However, in the framework of SO, the simulation is continuously invoked during the optimisation trajectory, which increases the computing loads to levels that are unrealistic to support the real-time construction decision. This study proposes ensemble learning embedded simulation optimisation (ESO) as an alternative approach for construction optimisation. The ensemble learning (EL) algorithm modifies the SO framework through establishing a connection between the simulation and optimisation technologies. This approach reduces the computing loads associated with the conventional SO framework by accurately learning from simulations and providing efficient fitness evaluations for optimisation. A large-scale project application shows that the proposed approach was able to reduce the computing loads of SO by approximately 90% yet still provide comparable optimisation quality. The proposed method is an alternative approach to SO that can be run on standard computing platforms and supports nearly real-time optimisation decisions.
|
|
| 6. |
- Feng, Kailun, 1991-, et al.
(författare)
-
Embedding ensemble learning into simulation-based optimisation : a learning-based optimisation approach for construction planning
- 2023
-
Ingår i: Engineering Construction and Architectural Management. - : Emerald Group Publishing Limited. - 0969-9988 .- 1365-232X. ; 30:1, s. 259-295
-
Tidskriftsartikel (refereegranskat)abstract
- Purpose - Simulation-based optimisation (SO) is a popular optimisation approach for building and civil engineering construction planning. However, in the framework of SO, the simulation is continuously invoked during the optimisation trajectory, which increases the computational loads to levels unrealistic for timely construction decisions. Modification on the optimisation settings such as reducing searching ability is a popular method to address this challenge, but the quality measurement of the obtained optimal decisions, also termed as optimisation quality, is also reduced by this setting. Therefore, this study aims to develop an optimisation approach for construction planning that reduces the high computational loads of SO and provides reliable optimisation quality simultaneously.Design/methodology/approach - This study proposes the optimisation approach by modifying the SO framework through establishing an embedded connection between simulation and optimisation technologies. This approach reduces the computational loads and ensures the optimisation quality associated with the conventional SO approach by accurately learning the knowledge from construction simulations using embedded ensemble learning algorithms, which automatically provides efficient and reliable fitness evaluations for optimisation iterations.Findings - A large-scale project application shows that the proposed approach was able to reduce computational loads of SO by approximately 90%. Meanwhile, the proposed approach outperformed SO in terms of optimisation quality when the optimisation has limited searching ability.Originality/value - The core contribution of this research is to provide an innovative method that improves efficiency and ensures effectiveness, simultaneously, of the well-known SO approach in construction applications. The proposed method is an alternative approach to SO that can run on standard computing platforms and support nearly real-time construction on-site decision-making.
|
|
| 7. |
- Feng, Kailun, 1991-
(författare)
-
Environmentally Friendly Construction Processes Under Uncertainty : Assessment, Optimisation and Robust Decision-Making
- 2020
-
Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- The construction processes of building and civil infrastructure are broadly recognised as large contributors to the environmental degradation, which cause environmental impacts directly and indirectly by massive energy use, intensive greenhouse emissions, and significant resources consumption. The report from Royal Swedish Academy of Engineering Sciences (IVA) and the Swedish Construction Federation (Byggföretagen) shows that the total carbon dioxide equivalents emitted per year from construction processes are the same size as emissions from all of the cars in Sweden, and more than that is generated by all lorries and busses. Under the current scenario of practices and technologies, 35-60% of the remaining carbon budget of Paris Agreement in next 30 years would be taken by construction processes. To follow the fossil free Sweden initiative, Swedish construction and civil engineering sector has set the roadmap to reduce the greenhouse gas emissions of construction processes by 50% from 2015 to 2030 and reach net-zero emissions by 2045.Construction processes are usually carried out in contexts full of uncertainty, which causes significant challenges to achieve the target of environmentally friendly construction. The reasons can be summarised as three aspects. Firstly, most of related environmental impact assessment methods still base on static data, which lack the ability to capture the influence of uncertainty in an environmental assessment. Secondly, the uncertainty in construction processes leads to high level of computational loads, which results in that current studies present limitations on effectively providing real-time environmental optimisation. Thirdly, the robust decision-making has been proposed as an enabling method to yield decisions with robust performances with relatively less influences by uncertainty. However, current robust decision-making methods sometimes are not applicable for construction environmentally friendly decisions because the knowledge of uncertainty such as prior probability distributions are partly unknown due to characteristics of construction.The overall purpose of this thesis, therefore, was to formulate a holistic approach to assess, optimise and provide robust decision-making for environmentally friendly construction under the uncertain contexts. The developed environmental impact assessment method integrates discrete-event simulation (DES) and process-based life-cycle assessment (pLCA). It takes advantage of discrete-event simulation to reinforce the uncertainty analysis ability of conventional environmental assessment methods. The optimisation method achieves real-time environmental optimisation by introducing machine learning (ML) technology into simulation-based optimisation. It significantly reduces the computational loads of optimisation by the ML’s real-time feedback ability during uncertainty quantification. The developed robust decision-making method combines discrete-event simulation (DES) and data mining (DM) technologies to address the uncertain contexts of construction. It utilities construction performance dataset, i.e. a data-driven method, to quantify the robustness and identify the vulnerability of environmentally friendly decisions in the situation of partly unknown probability distributions.To achieve research purpose, the research is designed as an explorative procedure in a loop of (1) problem identification, (2) method development, and (3) method examination. In the first step, the requirements of environmentally friendly construction in real practices are identified, the current limitations and knowledge gaps that block the environmentally friendly construction are revealed. In the second step, to solve the identified problems, the holistic approach is designed. The theoretical methods that base on relevant theories are established, and prototypes that base on relevant technologies are developed. In the last step, the holistic approach is implemented into real construction cases. The procedure will be loop to solve identified problems until the research purpose has been fully achieved.The research provides a systematic tool to handle uncertainty and to support environmentally friendly construction practices for project decision-makers. Firstly, developed approach enables to assess the environmental impacts of construction processes involving uncertainty, which help to better understand the influences of uncertainty and develop construction planning that can improve the environmental performance. And the optimisation section enables the real-time decision support of an environmental optimisation by considering multi-objective and in a great deal of construction alternatives, which helps to efficiently narrow down numerous construction alternatives and provide practical environmentally optimal planning. Finally, it provides the decision-makers with robust environmentally friendly decisions on the construction planning that are least affected by uncertainty, and provides vulnerable scenarios of uncertain factors for an informed uncertainty management in the progress of construction.
|
|
| 8. |
- Feng, Kailun, 1991-, et al.
(författare)
-
The Environmental Performance of Prefabricated Building and Construction : A Critical Review
- 2017
-
Ingår i: ICCREM 2017. - Reston, VA : American Society of Civil Engineers (ASCE). - 9780784481080 ; , s. 18-42
-
Konferensbidrag (refereegranskat)abstract
- The building industry consumes a large amount of nature resources and generates significant environment impacts around the world. To mitigate the resource consumption and associated environmental impacts, prefabricated building and construction has been proven to be one of the solutions. Several studies have been published in the past decade to explore the potential environmental benefits of prefabrication. However, a systematic and holistic review in the context of environmental impacts of prefabrication is still lacking. The research aims to reveal state-of-the-art and identify the research trends regarding environmental performance of prefabricated building and construction. To do so, a 4-stage literature retrieval and integrated analysis framework is designed to efficiently capture and examine the subject of interest. The results revealed that the most focused research subjects in this field are environmental and energy performances assessment, and carbon and energy are most used metrics to evaluate these performances. Almost all reviewed research demonstrated that prefabrication was better than conventional building on environmental performance. Nevertheless, the most frequently used assessment method is process based life cycle assessment developed from previous conventional building analysis, which may not be the proper method for prefabrication building. Operation, maintenance, and demolition obtain less study attention and it may worsen the final decision. Present data source that usually based on other research may invalid the final results. The revealed trends and gaps can serve as in-depth information and motivation for researchers to make further progress in prefabricated building study
|
|
| 9. |
- Feng, Kailun, 1991-, et al.
(författare)
-
Uncertainty Analysis Approach for Construction under Deep Uncertainty
- 2020
-
Ingår i: Journal of construction engineering and management. - : American Society of Civil Engineers (ASCE). - 0733-9364 .- 1943-7862.
-
Tidskriftsartikel (refereegranskat)abstract
- Construction processes usually occur under uncertain conditions, such as uncertain labour work productivity, equipment failure rate, weather situation and off-site transport condition. These uncertain factors can significantly affect project outcomes. However, for projects lacking a full understanding of uncertain factors, uncertainty analysis approaches relying on prior probability distribution or reasonable range are no longer applicable. Situations in which uncertain factors cannot be fully understood in decision-making are defined as deep uncertainty problems.This study proposes an uncertainty analysis approach that integrates process simulation and data mining to be a data-driven method for decision-making in construction projects under deep uncertainty. In process simulation, a Latin Hypercube Sampling (LHS) generates the samples of uncertainty scenario, and Discrete-Event Simulation (DES) quantifies robustness of alternative schemes under uncertain scenarios. In data mining, the Patient Rule Induction Method (PRIM) algorithm reveals the vulnerability of decisions that lead to unacceptable project performance. A real construction case was used to test the presented approach, with the results revealing that the approach is valuable for decision-makers who need to analyse uncertainty without reliable prior probability distributions and reasonable range of certain uncertain factors. It quantified the robustness of various construction schemes, as well as identified the vulnerable scenarios that could jeopardise project completion. The developed approach is an applicable uncertainty analysis approach to support decision-making of construction project under deep uncertainty.
|
|
| 10. |
- Feng, Kailun, 1991-, et al.
(författare)
-
Weakness of Embodied Energy Assessment on Construction : A Literature Review
- 2016
-
Ingår i: ICCREM 2016. - Reston, VA : American Society of Civil Engineers (ASCE). - 9780784480274 ; , s. 547-559
-
Konferensbidrag (refereegranskat)abstract
- Construction industry consumes a large amount of energy and resources in both developed and developing countries. The opportunities for improving energy efficiency of construction could be research considering energy assessment and thereby providing suggestions for practical action. The research on embodied energy (EE) is an important endeavor of this orientation. The aim of this review paper is to investigate and analyze research weakness of present embodied energy study on construction. To do so, a professional searching tool called Academic 2.0 was employed to collect relevant publications from multiple databases in this research field. After obtaining relevant studies, paper elimination and information collection are performed based on predefined criteria. Data analysis was then performed and showed that assessment method, data sources, and research boundary differed dramatically amongst present study. And, they are weaknesses of current study. These results could help researchers deeply understand present study weakness and then overcome it in further study
|
|
| 11. |
- Krantz, Jan, 1984-, et al.
(författare)
-
‘Eco-Hauling’ Principles to Reduce Carbon Emissions and the Costs of Earthmoving : a Case Study
- 2019
-
Ingår i: Journal of Cleaner Production. - : Elsevier. - 0959-6526 .- 1879-1786. ; 208, s. 479-489
-
Tidskriftsartikel (refereegranskat)abstract
- Mitigating emissions of carbon dioxide and other greenhouse gases is critical if we are to meet the increasing threats posed by global warming. Previous studies have shown conclusively that a substantial part of all carbon dioxide emissions comes from transportation, and that Eco-Driving principles based upon strategic, tactical, and operational decisions have the potential to reduce these emissions. However, these well-established principles have been neglected within the construction industry despite the large number of transport-related activities that attend most construction projects. This paper therefore aims to increase awareness and understanding within the industry of the potential reductions of both carbon dioxide emissions and the costs of earthmoving activities that could be achieved through the use of Eco-Driving principles. A new concept labeled ‘Eco-Hauling’, which extends the Eco-Driving concept to earthmoving, is proposed. A case study of a road project has been conducted and used to demonstrate the new concept. Discrete-event simulation is used to support the data analysis as it enables modeling of the dynamic interactions between equipment and activities of multiple different construction scenarios. The presented findings show that a combination of decisions taken from the proposed Eco-Hauling concept can enable earthmoving contractors to substantially reduce carbon dioxide emissions and costs while maintaining productivity. This study has implications for the general advancement of Eco-Driving theory, as well as for project management as it sets out a viable approach for reducing greenhouse gas emissions in construction projects.
|
|
| 12. |
- Lu, Weizhuo, et al.
(författare)
-
Big-data driven building retrofitting : An integrated Support Vector Machines and Fuzzy C-means clustering method
- 2020
-
Ingår i: WSBE 20 - World Sustainable Built Environment - Beyond2020 2-4 November 2020, Gothenburg, Sweden. - : Institute of Physics (IOP).
-
Konferensbidrag (refereegranskat)abstract
- It has become a mainstream to use physical models to quantify expected energy savings from alternative retrofit methods and technologies. However, they are not suitable for predicting energy use of buildings when detailed and specified input parameters are unavailable. The overall purpose of the research is to support the stakeholders in taking decisions on refurbishments options when not all of physical information is available, in order to achieve the Swedish Energy Agency's measurements of near-zero energy buildings. The research will transfer big data from Swedish Energy Performance Certificates for building retrofitting. A Support Vector Machines and Fuzzy C-means clustering (SVM-FCM) integrated machine learning algorithm is used directly to extract the case-specific knowledge from EPC big data regarding building characteristics and energy saving of retrofit measures. It enables to prioritize retrofit measures and compute their expected energy savings for buildings. This proposed data driven method is an attempt of taking advantage of big data for practical building retrofit selection.
|
|
