| 1. |
- Chen, Shiwei, et al.
(author)
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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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In: ICCREM 2019. - Reston, VA : American Society of Civil Engineers (ASCE). ; , s. 12-24
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Conference paper (peer-reviewed)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. |
- Feng, Kailun, 1991-, et al.
(author)
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Embedding Ensemble Learning into Construction Optimisation : A Computational Reduction Approach
- 2020
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Journal article (peer-reviewed)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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| 3. |
- Feng, Kailun, 1991-, et al.
(author)
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Embedding ensemble learning into simulation-based optimisation : a learning-based optimisation approach for construction planning
- 2023
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In: Engineering Construction and Architectural Management. - : Emerald Group Publishing Limited. - 0969-9988 .- 1365-232X. ; 30:1, s. 259-295
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Journal article (peer-reviewed)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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| 4. |
- Feng, Kailun, et al.
(author)
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Planning Construction Projects in Deep Uncertainty : A Data-Driven Uncertainty Analysis Approach
- 2022
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In: Journal of construction engineering and management. - : American Society of Civil Engineers (ASCE). - 0733-9364 .- 1943-7862. ; 148:8
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Journal article (peer-reviewed)abstract
- Construction planning is significantly affected by many uncertain factors derived from construction tasks, the environments, resources, technologies, personnel, and more. Uncertainty analysis approaches are thus critical to supporting the decision making associated with construction planning. However, the precise probability distributions (PDs) of uncertain factors are sometimes inaccessible, especially for construction projects in a novel context with limited previous experiences or similar references. These situations constitute a deep uncertainty problem, and probability-based methods are no longer applicable for construction planning. To address this challenge, an uncertainty analysis approach that integrates Latin hypercube sampling (LHS), discrete-event simulation (DES), and the patient rule induction method (PRIM) is proposed. Specifically, it is progressed by LHS and DES to generate a wide array of uncertainty scenarios represented by possible PDs to quantify the robustness of various construction decisions; then, PRIM is used to identify the vulnerable scenarios that will jeopardize project completion. The approach was implemented on a real-world project, and the results demonstrated that it was able to identify the most robust construction schemes and vulnerable scenarios for construction planning. This research contributes a data-driven technology that provides an uncertainty analysis approach for construction planning without relying on assumed probability distributions from limited, unreliable project references.
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| 5. |
- Wang, Sen, et al.
(author)
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The importance of Se-related genes in the chondrocyte of Kashin-Beck disease revealed by whole genomic microarray and network analysis
- 2019
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In: Biological Trace Element Research. - : Springer. - 0163-4984 .- 1559-0720. ; 187:2, s. 367-375
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Journal article (peer-reviewed)abstract
- Kashin-Beck disease (KBD) is an endemic, chronic, and degenerative osteoarthropathy. Selenium (Se) deficiency plays important role in the pathogenesis of KBD. We aimed to screen Se-related gene from chondrocytes of patients with KBD. Whole-genome oligonucleotide microarrays were used to detect differentially expressed genes. qRT-PCR was used to confirm the microarray results. Comparative Toxicogenomics Database (CTD) was used to screen Se-related genes from differentially expressed genes. Gene Ontology (GO) classifications and network analysis of Se-related genes were constituted by STRING online system. Three hundred ninety-nine differentially expressed genes were obtained from microarray. Among them, 54 Se-related genes were identified by CTD. The qRT-PCR validation showed that four genes expressed similarly with the ones in the microarray transcriptional profiles. The Se-related genes were categorized into 6 cellular components, 8 molecular functions, 44 biological processes, 10 pathways, and 1 network by STRING. The Se-related gene insulin-like growth factor binding protein 2 (IGFBP2), insulin-like growth factor binding protein 3 (IGFBP3), interleukin 6 (IL6), BCL2, apoptosis regulator (BCL2), and BCL2-associated X, apoptosis regulator (BAX), which involved in many molecular functions, biological processes, and apoptosis pathway may play important roles in the pathogenesis of KBD.
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| 6. |
- Chen, Shiwei, et al.
(author)
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Concrete Construction : How to Explore Environmental and Economic Sustainability in Cold Climates
- 2020
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In: Sustainability. - : MDPI. - 2071-1050. ; 12:9
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Journal article (peer-reviewed)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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| 7. |
- Feng, Kailun, 1991-, et al.
(author)
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A predictive environmental assessment method for construction operations : Application to a Northeast China case study
- 2018
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In: Sustainability. - : MDPI. - 2071-1050. ; 10:11
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Journal article (peer-reviewed)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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| 8. |
- Feng, Kailun, 1991-, et al.
(author)
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An Integrated Environment–Cost–Time Optimisation Method for Construction Contractors Considering Global Warming
- 2018
-
In: Sustainability. - : MDPI. - 2071-1050. ; 10:11
-
Journal article (peer-reviewed)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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| 9. |
- Feng, Kailun, 1991-, et al.
(author)
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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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In: Sustainable cities and society. - : Elsevier. - 2210-6707. ; 50
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Journal article (peer-reviewed)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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| 10. |
- Feng, Kailun, et al.
(author)
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Energy-Efficient Retrofitting under Incomplete Information : A Data-Driven Approach and Empirical Study of Sweden
- 2022
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In: Buildings. - : MDPI. - 2075-5309. ; 12:8
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Journal article (peer-reviewed)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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