Excavators are one of the most energy-intensive elements of earthwork operations. Predicting the energy consumption and CO₂ emissions of excavators is therefore critical in order to mitigate the environmental impact of earthwork operations. However, there is a lack of method for estimating such energy consumption and CO₂ emissions, especially during the early planning stages of these activities. This research proposes a model using an artificial neural network (ANN) to predict an excavator’s hourly energy consumption and CO₂ emissions under different site conditions. The proposed ANN model includes five input parameters: digging depth, cycle time, bucket payload, engine horsepower, and load factor. The Caterpillar handbook’s data, that included operational characteristics of twenty-five models of excavators, were used to develop the training and testing sets for the ANN model. The proposed ANN models were also designed to identify which factors from all the input parameters have the greatest impact on energy and emissions, based on partitioning weight analysis. The results showed that the proposed ANN models can provide an accurate estimating tool for the early planning stage to predict the energy consumption and CO₂ emissions of excavators. Analyses have revealed that, within all the input parameters, cycle time has the greatest impact on energy consumption and CO₂ emissions. The findings from the research enable the control of crucial factors which significantly impact on energy consumption and CO₂ emissions.

Product and process platforms have been promoted as possible enablers of increased productivity in civil engineering. However, such platforms are traditionally applied in mass manufacturing industries where production assets are employed in continuous production of uniform products, which strongly facilitates continuous improvement. The discontinuous nature of project-based production in civil engineering restricts such possibilities. Thus, if platforms are implemented there is a need for methods capable of evaluating the performance of integrated product and production process configurations in specific projects. A possibility, explored in this paper, is to use database-driven simulation. As a case study, a configurable simulation model, based on standardized process patterns and values stored in a platform, has been developed of the production for a bridge concept. The presented results provide evidence that database-driven simulation can support efficient platform evaluation and development by ...

Greenhouse gas (GHG) emissions from construction processes are a serious concern globally. Of the several approaches taken to assess emissions, Life Cycle Assessment (LCA) based methods do not just take into account the construction phase, but consider all phases of the life cycle of the construction. However, many current LCA approaches make general assumptions regarding location and effects, which do not do justice to the inherent dynamics of normal construction projects. This study presents a model to assess the embodied energy and associated GHG emissions, which is specifically adapted to address the dynamics of infrastructure construction projects. The use of the model is demonstrated on the superstructure of a prefabricated bridge. The findings indicate that Building Information Models/Modeling (BIM) and Discrete Event Simulation (DES) can be used to efficiently generate project-specific data, which is needed for estimating the embodied energy and associated GHG emissions in construction settings. This study has implications for the advancement of LCA-based methods (as well as project management) as a way of assessing embodied energy and associated GHG emissions related to construction.

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.

A lean‐agile model of homebuilders’ production systems is proposed in this research. Value stream mapping is utilized to clarify the proposed lean‐agile model, in which pull and Kanban are used to ensure smooth production upstream of the de‐coupling point, while fluctuating market demands in terms of variety of homes and variability of volumes are managed by the agile process downstream of the de‐coupling point. The de‐coupling point is used to provide components to downstream agile process and shield upstream smooth production from market fluctuations. A simulation model is developed to evaluate and validate the effectiveness of the proposed lean‐agile model. Simulation experiments show that the lean‐agile model prevents the accumulation of high inventory levels and thus provides better customization opportunities for clients compared to even‐flow‐construction. It also provides a more stable process with shorter cycle times compared to sales‐driven production. The proposed lean‐agile model offers new possibilities for homebuilders to manage the balance between meeting fluctuating market demands and stabilizing the production system.

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.