| 1. |
- Chen, Shiwei, et al.
(författare)
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A Discrete Event Simulation-Based Analysis of Precast Concrete Supply Chain Strategies Considering Suppliers’ Production and Transportation Capabilities
- 2019
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Ingår i: ICCREM 2019. - Reston, VA : American Society of Civil Engineers (ASCE). ; , s. 12-24
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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.
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| 2. |
- Chen, Shiwei, et al.
(författare)
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A Simulation-Based Optimisation for Contractors in Precast Concrete Projects
- 2019
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Konferensbidrag (refereegranskat)abstract
- PurposeThis paper aims to provide decision support for precast concrete contractors about both precastconcrete supply chain strategies and construction configurations.Design/Methodology/ApproachThis paper proposes a simulation-based optimisation for supplychain and construction (SOSC) during the planning phase of PC building projects. The discrete eventsimulation is used to capture the characteristics of supply chain and construction processes, and calculate construction objectives under different plans. Particle swarm optimisation is combined with simulation tofind optimal supply chain strategies and construction configurations.FindingsThe efficiency of SOSC is compared with the parametric simulation approach. Over 70 per centof time and effort used to simulate and compare alternative plans is saved owing to SOSC.Research Limitations/ImplicationsBuilding simulation model costs a lot of time and effort. The data requirement of the proposed method is high.Practical ImplicationsThe proposed SOSC approach can provide decision support for PC contractorsby optimising supply chain strategies and construction configurations.Originality/ValueThis paper has two contributions: one is in providing a decision support tool SOSC tooptimise both supply chain strategies and construction configurations, while the other is in building aprototype of SOSC and testing it in a case study.
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| 3. |
- Chen, Shiwei, et al.
(författare)
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Concrete Construction : How to Explore Environmental and Economic Sustainability in Cold Climates
- 2020
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Ingår i: Sustainability. - : MDPI. - 2071-1050. ; 12:9
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Tidskriftsartikel (refereegranskat)abstract
- In many cold regions around the world, such as northern China and the Nordic countries,on‐site concrete is often cured in cold weather conditions. To protect the concrete from freezing or excessively long maturation during the hardening process, contractors use curing measures. Different types of curing measures have different effects on construction duration, cost, and greenhouse gas emissions. Thus, to maximize their sustainability and financial benefits, contractors need to select the appropriate curing measures against different weather conditions. However, there is still a lack of efficient decision support tools for selecting the optimal curing measures, considering the temperature conditions and effects on construction performance. Therefore, the aim of this study was to develop a Modeling‐Automation‐Decision Support (MADS) framework and tool to help contractors select curing measures to optimize performance in terms of duration, cost, and CO2 emissions under prevailing temperatures. The developed framework combines a concrete maturity analysis (CMA) tool, a discrete event simulation (DES), and a decision support module to select the best curing measures. The CMA tool calculates the duration of concrete curing needed to reach the required strength, based on the chosen curing measures and anticipated weather conditions. The DES simulates all construction activities to provide input for the CMA and uses the CMA results to evaluate construction performance. To analyze the effectiveness of the proposed framework, a software prototype was developed and tested on a case study in Sweden. The results show that the developed framework can efficiently propose solutions that significantlyreduce curing duration and CO2 emissions.
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| 4. |
- Feng, Kailun, 1991-, et al.
(författare)
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A predictive environmental assessment method for construction operations : Application to a Northeast China case study
- 2018
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Ingår i: Sustainability. - : MDPI. - 2071-1050. ; 10:11
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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.
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| 5. |
- Feng, Kailun, 1991-, et al.
(författare)
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An Integrated Environment–Cost–Time Optimisation Method for Construction Contractors Considering Global Warming
- 2018
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Ingår i: Sustainability. - : MDPI. - 2071-1050. ; 10:11
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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.
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| 6. |
- Feng, Kailun, 1991-, et al.
(författare)
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Assessing environmental performance in early building design stage : An integrated parametric design and machine learning method
- 2019
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Ingår i: Sustainable cities and society. - : Elsevier. - 2210-6707. ; 50
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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.
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| 7. |
- Feng, Kailun, 1991-, et al.
(författare)
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Embedding Ensemble Learning into Construction Optimisation : A Computational Reduction Approach
- 2020
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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.
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| 8. |
- Feng, Kailun, 1991-, et al.
(författare)
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Embedding ensemble learning into simulation-based optimisation : a learning-based optimisation approach for construction planning
- 2023
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Ingår i: Engineering Construction and Architectural Management. - : Emerald Group Publishing Limited. - 0969-9988 .- 1365-232X. ; 30:1, s. 259-295
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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.
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| 9. |
- Feng, Kailun, et al.
(författare)
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Energy-Efficient Retrofitting under Incomplete Information : A Data-Driven Approach and Empirical Study of Sweden
- 2022
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Ingår i: Buildings. - : MDPI. - 2075-5309. ; 12:8
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Tidskriftsartikel (refereegranskat)abstract
- The building performance simulation (BPS) based on physical models is a popular method to estimate the expected energy-savings of energy-efficient building retrofitting. However, many buildings, especially the older building constructed several decades ago, do not have full access to complete information for a BPS method. Incomplete information generally comes from the information that is missing, such as the U-value of part building components, due to incomplete documentation or component deterioration over time. It also comes from the case-specific incomplete information due to different documentation systems. Motivated by the available big data of real-life building performance datasets (BPDs), a data-driven approach is proposed to support the decision-making of building retrofitting selections under incomplete information conditions. The data-driven approach constructed a Performance Modelling with Data Imputation (PMDI) with integrated backpropagation neural networks, fuzzy C-means clustering, principal component analysis, and trimmed scores regression. An empirical study was conducted on real-life buildings in Sweden, and the results validated that the PMDI method can model the performance ranges of energy-efficient retrofitting for family house buildings with more than 90% confidence. For a target building in Stockholm, the suggested retrofitting measure is expected to save energy by 12,017~17,292 KWh/year.
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| 10. |
- Feng, Kailun, et al.
(författare)
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Energy-efficient retrofitting with incomplete building information : a data-driven approach
- 2022
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Ingår i: E3S web of conferences. - : EDP Sciences.
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Konferensbidrag (refereegranskat)abstract
- The high-performance insulations and energy-efficient HVAC have been widely employed as energy-efficient retrofitting for building renovation. Building performance simulation (BPS) based on physical models is a popular method to estimate expected energy savings for building retrofitting. However, many buildings, especially the older building constructed several decades ago, do not have full access to complete information for a BPS method. To address this challenge, this paper proposes a data-driven approach to support the decision-making of building retrofitting under incomplete information. The data-driven approach is constructed by integrating backpropagation neural networks (BRBNN), fuzzy C-means clustering (FCM), principal component analysis (PCA), and trimmed scores regression (TSR). It is motivated by the available big data sources from real-life building performance datasets to directly model the retrofitting performances without generally missing information, and simultaneously impute the case-specific incomplete information. This empirical study is conducted on real-life buildings in Sweden. The result indicates that the approach can model the performance ranges of energy-efficient retrofitting for family houses with more than 90% confidence. The developed approach provides a tool to predict the performance of individual buildings from different retrofitting measures, enabling supportive decision-making for building owners with inaccessible complete building information, to compare alternative retrofitting measures.
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