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Publications (10 of 38) Show all publications
Jassim, H., Krantz, J., Lu, W. & Olofsson, T. (2019). A Model to Reduce Earthmoving Impacts.
Open this publication in new window or tab >>A Model to Reduce Earthmoving Impacts
2019 (English)Article in journal (Refereed) Submitted
National Category
Construction Management Infrastructure Engineering
Identifiers
urn:nbn:se:ltu:diva-73322 (URN)
Available from: 2019-03-26 Created: 2019-03-26 Last updated: 2019-07-17
Chen, S., Feng, K. & Lu, W. (2019). A Simulation-Based Optimisation for Contractors in Precast Concrete Projects. In: : . Paper presented at 10th Nordic Conference on Construction Economics and Organization, 7-8 May 2019, Tallinn, Estonia (pp. 137-145). Emerald Group Publishing Limited, 2
Open this publication in new window or tab >>A Simulation-Based Optimisation for Contractors in Precast Concrete Projects
2019 (English)Conference paper, Published paper (Refereed)
Abstract [en]

Purpose– This paper aims to provide decision support for precast concrete contractors about both precastconcrete supply chain strategies and construction configurations.

Design/Methodology/Approach– This 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.

Findings– The 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/Implications– Building simulation model costs a lot of time and effort. The data requirement of the proposed method is high.

Practical Implications– The proposed SOSC approach can provide decision support for PC contractorsby optimising supply chain strategies and construction configurations.

Originality/Value– This 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.

Place, publisher, year, edition, pages
Emerald Group Publishing Limited, 2019
Keywords
Precast concrete, Supply chain, Building construction, Discrete event simulation, Particle swarm optimization, Simulation-based optimization
National Category
Construction Management
Research subject
Construction Management and Building Technology
Identifiers
urn:nbn:se:ltu:diva-74055 (URN)10.1108/S2516-285320190000002019 (DOI)
Conference
10th Nordic Conference on Construction Economics and Organization, 7-8 May 2019, Tallinn, Estonia
Available from: 2019-05-27 Created: 2019-05-27 Last updated: 2019-05-27Bibliographically approved
Feng, K., Lu, W. & Wang, Y. (2019). Assessing environmental performance in early building design stage: An integrated parametric design and machine learning method. Sustainable cities and society, 50, Article ID 101596.
Open this publication in new window or tab >>Assessing environmental performance in early building design stage: An integrated parametric design and machine learning method
2019 (English)In: Sustainable cities and society, ISSN 2210-6707, Vol. 50, article id 101596Article in journal (Refereed) Published
Abstract [en]

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.

Place, publisher, year, edition, pages
Elsevier, 2019
Keywords
Building early design, Parametric design, Machine learning, Environmental impact, Prediction intervals
National Category
Construction Management
Research subject
Construction Management and Building Technology
Identifiers
urn:nbn:se:ltu:diva-75133 (URN)10.1016/j.scs.2019.101596 (DOI)000484255800005 ()2-s2.0-85067201337 (Scopus ID)
Note

Validerad;2019;Nivå 2;2019-06-28 (johcin)

Available from: 2019-06-28 Created: 2019-06-28 Last updated: 2019-09-27Bibliographically approved
Jassim, H., Lu, W. & Olofsson, T. (2019). Determining the environmental impact of material hauling with wheel loaders during earthmoving operations. Journal of the Air and Waste Management Association, 69(10), 1195-1214
Open this publication in new window or tab >>Determining the environmental impact of material hauling with wheel loaders during earthmoving operations
2019 (English)In: Journal of the Air and Waste Management Association, ISSN 1096-2247, E-ISSN 2162-2906, Vol. 69, no 10, p. 1195-1214Article in journal (Refereed) Published
Abstract [en]

A method has been developed to estimate the environmental impact of wheel loaders used in earthmoving operations. The impact is evaluated in terms of energy use and emissions of air pollutants (CO2, CO, NOx, CH4, VOC, and PM) based on the fuel consumption per cubic meter of hauled material. In addition, the effects of selected operational factors on emissions during earthmoving activities were investigated to provide better guidance for practitioners during the early planning stage of construction projects. The relationships between six independent parameters relating to wheel loaders and jobsite conditions (namely loader utilization rates, loading time, bucket payload, horsepower, load factor, and server capacity) were analyzed using artificial neural networks, machine performance data from manufacturer’s handbooks, and discrete event simulations of selected earthmoving scenarios. A sensitivity analysis showed that the load factor is the largest contributor to air pollutant emissions, and that the best way to minimize environmental impact is to maximize the wheel loaders’ effective utilization rates. The new method will enable planners and contractors to accurately assess the environmental impact of wheel loaders and/or hauling activities during earthmoving operations in the early stages of construction projects.

Implications: There is an urgent need for effective ways of benchmarking and mitigating emissions due to construction operations, and particularly those due to construction equipment, during the pre-construction phase of construction projects. Artificial Neural Networks (ANN) are shown to be powerful tools for analyzing the complex relationships that determine the environmental impact of construction operations and for developing simple models that can be used in the early stages of project planning to select machine configurations and work plans that minimize emissions and energy consumption. Using such a model, it is shown that the fuel consumption and emissions of wheel loaders are primarily determined by their engine load, utilization rate, and bucket payload. Moreover, project planners can minimize the environmental impact of wheel loader operations by selecting work plans and equipment configurations that minimize wheel loaders’ idle time and avoid bucket payloads that exceed the upper limits specified by the equipment manufacturer.

Place, publisher, year, edition, pages
Taylor & Francis, 2019
National Category
Construction Management Environmental Analysis and Construction Information Technology
Research subject
Construction Management and Building Technology
Identifiers
urn:nbn:se:ltu:diva-74570 (URN)10.1080/10962247.2019.1640805 (DOI)000482496000001 ()
Note

Validerad;2019;Nivå 2;2019-10-08 (johcin);

Artikeln har tidigare förekommit som manuskript i avhandling.

Available from: 2019-06-14 Created: 2019-06-14 Last updated: 2019-10-08Bibliographically approved
Feng, K., Lu, W., Olofsson, T., Chen, S., Yan, H. & Wang, Y. (2018). A predictive environmental assessment method for construction operations: Application to a Northeast China case study. Sustainability, 10(11), Article ID 3868.
Open this publication in new window or tab >>A predictive environmental assessment method for construction operations: Application to a Northeast China case study
Show others...
2018 (English)In: Sustainability, ISSN 2071-1050, E-ISSN 2071-1050, Vol. 10, no 11, article id 3868Article in journal (Refereed) Published
Abstract [en]

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.

Place, publisher, year, edition, pages
MDPI, 2018
Keywords
environmental impacts, construction process simulation, process-based life cycle assessment, construction operations, supply chain
National Category
Infrastructure Engineering Construction Management
Research subject
Structural Engineering; Construction Management and Building Technology
Identifiers
urn:nbn:se:ltu:diva-71690 (URN)10.3390/su10113868 (DOI)000451531700042 ()2-s2.0-85055572047 (Scopus ID)
Note

Validerad;2018;Nivå 2;2018-11-21 (jochin) 

Available from: 2018-11-21 Created: 2018-11-21 Last updated: 2019-02-27Bibliographically approved
Feng, K., Lu, W., Chen, S. & Wang, Y. (2018). An Integrated Environment–Cost–Time Optimisation Method for Construction Contractors Considering Global Warming. Sustainability, 10(11), Article ID 4207.
Open this publication in new window or tab >>An Integrated Environment–Cost–Time Optimisation Method for Construction Contractors Considering Global Warming
2018 (English)In: Sustainability, ISSN 2071-1050, E-ISSN 2071-1050, Vol. 10, no 11, article id 4207Article in journal (Refereed) Published
Abstract [en]

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.

Place, publisher, year, edition, pages
MDPI, 2018
Keywords
construction contractor, environment-cost-time optimisation, particle swarm optimisation, discrete-event simulation, construction planning
National Category
Infrastructure Engineering Construction Management
Research subject
Construction Management and Building Technology; Structural Engineering
Identifiers
urn:nbn:se:ltu:diva-71814 (URN)10.3390/su10114207 (DOI)000451531700381 ()2-s2.0-85056598920 (Scopus ID)
Note

Validerad;2018;Nivå 2;2018-11-29 (svasva)

Available from: 2018-11-29 Created: 2018-11-29 Last updated: 2019-02-27Bibliographically approved
Jassim, H. S. .., Lu, W. & Olofsson, T. (2018). Assessing energy consumption and carbon dioxide emissions of off-highway trucks in earthwork operations: an artificial neural network model. Journal of Cleaner Production, 198, 364-380
Open this publication in new window or tab >>Assessing energy consumption and carbon dioxide emissions of off-highway trucks in earthwork operations: an artificial neural network model
2018 (English)In: Journal of Cleaner Production, ISSN 0959-6526, E-ISSN 1879-1786, Vol. 198, p. 364-380Article in journal (Refereed) Published
Abstract [en]

Methods capable of predicting the energy use and CO2 emissions of off-highway trucks, especially in the initial planning phase, are rare. This study proposed an artificial neural networks (ANN) model to assess such energy use and CO2 emissions for each unit volume of hauled materials associated with each hauling distance. Data from discrete event simulations (DES), an off-highway truck database, and different site conditions were simultaneously analyzed to train and test the proposed ANN model. Six independent quantities (i.e., truck utilization rate, haul distance, loading time, swelling factor, truck capacity, and grade horsepower) were used as the input parameters for each model. The developed model is an efficient tool capable of assessing the energy use and CO2 emissions of off-highway trucks in the initial planning stage. The results revealed that the grade horsepower and haul distances yield a significant increase in the environmental impact of the trucks. In addition, the results demonstrated that, for a given set of project conditions, the environmental impact of trucks can reduced by improving their utilization rate and reducing the loading time.

Place, publisher, year, edition, pages
Elsevier, 2018
Keywords
Off-highway truck, energy consumption, CO2 emission, Simulation, ANN prediction model, initial planning stage
National Category
Construction Management
Research subject
Construction Management and Building Technology
Identifiers
urn:nbn:se:ltu:diva-70115 (URN)10.1016/j.jclepro.2018.07.002 (DOI)2-s2.0-85053160594 (Scopus ID)
Note

Validerad;2018;Nivå 2;2018-08-08 (andbra)

Available from: 2018-07-12 Created: 2018-07-12 Last updated: 2019-09-13Bibliographically approved
Feng, K., Chen, S. & Lu, W. (2018). Machine learning based construction simulation and optimization. In: M. Rabe; A.A. Juan; N. Mustafee; A. Skoogh; S. Jain; B. Johansson (Ed.), Proceedings of the 2018 Winter Simulation Conference: . Paper presented at 2018 Winter Simulation Conference, WSC 2018; The Swedish Exhibition and Congress CentreGothenburg; Sweden; 9 - 12 December 2018 (pp. 2025-2036). IEEE, Article ID 8632290.
Open this publication in new window or tab >>Machine learning based construction simulation and optimization
2018 (English)In: Proceedings of the 2018 Winter Simulation Conference / [ed] M. Rabe; A.A. Juan; N. Mustafee; A. Skoogh; S. Jain; B. Johansson, IEEE, 2018, p. 2025-2036, article id 8632290Conference paper, Published paper (Refereed)
Abstract [en]

Building construction comprises interaction and interdependence among processes. Discrete-event simulation (DES) is widely applied to model these processes interaction. To find optimal construction plans, optimization technique is usually integrated with DES. However, present simulation-optimization integrated method directly invokes simulation model within optimization algorithms, which is found significantly computationally expensive. This study proposes a machine learning based construction simulation and optimization integrated method. After trained by DES, the machine learning model accelerates simulation-optimization integration by nearly real-time providing fitness evaluation within optimization. This method was implemented into a real construction project for construction time-cost-environment optimization. Results show that proposed machine learning based method significantly reduce computing time compared with original simulation-optimization integration. Less than 1% of construction cost and time improvement were miss, while greenhouse gas emissions obtained same performance. The new method could be a more effective DES and optimization integration approach for practical engineering application.

Place, publisher, year, edition, pages
IEEE, 2018
National Category
Construction Management
Research subject
Construction Management and Building Technology
Identifiers
urn:nbn:se:ltu:diva-73236 (URN)10.1109/WSC.2018.8632290 (DOI)2-s2.0-85062601673 (Scopus ID)9781538665725 (ISBN)
Conference
2018 Winter Simulation Conference, WSC 2018; The Swedish Exhibition and Congress CentreGothenburg; Sweden; 9 - 12 December 2018
Available from: 2019-03-18 Created: 2019-03-18 Last updated: 2019-03-18Bibliographically approved
Jassim, H., Lu, W. & Olofsson, T. (2018). Quantification of Energy Consumption and Carbon Dioxide Emissions During Excavator Operations. In: Advanced Computing Strategies for Engineering: 25th EG-ICE International Workshop 2018, Lausanne, Switzerland, June 10-13, 2018, Proceedings, Part I. Paper presented at 25th EG-ICE International Workshop 2018, Lausanne, Switzerland, June 10-13, 2018 (pp. 431-453). Cham
Open this publication in new window or tab >>Quantification of Energy Consumption and Carbon Dioxide Emissions During Excavator Operations
2018 (English)In: Advanced Computing Strategies for Engineering: 25th EG-ICE International Workshop 2018, Lausanne, Switzerland, June 10-13, 2018, Proceedings, Part I, Cham, 2018, p. 431-453Conference paper, Published paper (Refereed)
Abstract [en]

A number of studies have assessed the energy consumed and carbon dioxide emitted by construction machinery during earthwork operations. However, little attention has been paid to predicting these variables during planning phases of such operations, which could help efforts to identify the best options for minimizing environmental impacts. Excavators are widely used in earthwork operations and consume considerable amounts of fuel, thereby generating large quantities of carbon dioxide. Therefore, rigorous evaluation of the energy consumption and emissions of different excavators during planning stages of project, based on characteristics of the excavators and projects, would facilitate selection of optimal excavators for specific projects, thereby reducing associated environmental impacts. Here we describe use of artificial neural networks (ANNs), developed using data from Caterpillar’s handbook, to model the energy consumption and CO2 emissions of different excavators per unit volume of earth handled. We also report a sensitivity analysis conducted to determine effects of key parameters (utilization rate, digging depth, cycle time, bucket payload, horsepower, load factor, and hauler capacity) on excavators’ energy consumption and CO2 emissions. Our analysis shows that environmental impacts of excavators can be most significantly reduced by improving their utilization rates and/or cycle times, and reducing their engine load factor. We believe our ANN models can potentially improve estimates of energy consumption and CO2 emissions by excavators. Their use in planning stages of earthworks projects could help planners make informed decisions about optimal excavator(s) to use, and contractors to evaluate environmental impacts of their activities. Finally, we describe a case study, based on a road construction project in Sweden, in which we use empirical data on the quantities and nature of the materials to be excavated, to estimate the environmental impact of using different excavators for the project

Place, publisher, year, edition, pages
Cham: , 2018
Series
Lecture Notes in Computer Science, ISSN 0302-9743 ; 10863
National Category
Construction Management
Research subject
Construction Management and Building Technology
Identifiers
urn:nbn:se:ltu:diva-69572 (URN)10.1007/978-3-319-91635-4_22 (DOI)2-s2.0-85049074506 (Scopus ID)978-3-319-91634-7 (ISBN)978-3-319-91635-4 (ISBN)
Conference
25th EG-ICE International Workshop 2018, Lausanne, Switzerland, June 10-13, 2018
Available from: 2018-06-15 Created: 2018-06-15 Last updated: 2019-06-14Bibliographically approved
Yang, B., Olofsson, T., Wang, F. & Lu, W. (2018). Thermal comfort in primary school classrooms: A case study under subarctic climate area of Sweden. Building and Environment, 135, 237-245
Open this publication in new window or tab >>Thermal comfort in primary school classrooms: A case study under subarctic climate area of Sweden
2018 (English)In: Building and Environment, ISSN 0360-1323, E-ISSN 1873-684X, Vol. 135, p. 237-245Article in journal (Refereed) Published
Abstract [en]

Limited studies were focused on primary school buildings especially under subarctic climate. Thermal comfort of children was assumed to be similar as that of adults, which may cause inaccuracy. To fill data blank and enrich global database, a field study was performed from late fall 2016 to early spring 2017 covering whole heating period in north part of Sweden. Indoor CO2 concentration was continuously monitored to evaluate indoor ventilation. Thermal comfort related parameters were continuously measured and predicted mean vote (PMV) was calculated. Subjective questionnaire surveys were performed every week except holidays. Subjective thermal sensation value (TSV) was always higher than objective PMV, which reflected thermal adaptation. The thermal adaptation became not obvious in middle and late winter because of long term exposure to heating environments. Heating system should be intensified gradually in early heating period, operated based on actual outdoor climate instead of experience in middle and late heating periods, extended under part load operation in early spring if necessary. The new 13─point TSV scale was pointed out by other researchers and tested inthis study, which can explore tiny TSV deviations from thermally neutral status and reflect more accurate thermal sensations.

Place, publisher, year, edition, pages
Elsevier, 2018
National Category
Construction Management
Research subject
Construction Management and Building Technology
Identifiers
urn:nbn:se:ltu:diva-68022 (URN)10.1016/j.buildenv.2018.03.019 (DOI)000430784300020 ()2-s2.0-85043997706 (Scopus ID)
Note

Validerad;2018;Nivå 2;2018-03-20 (andbra)

Available from: 2018-03-20 Created: 2018-03-20 Last updated: 2018-06-08Bibliographically approved
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Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0002-4695-5369

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