Engineering, Construction and Architectural Management最新文献

Purpose

The main purpose of this study is to reveal the degree of association between lean, building information modeling (BIM) and construction project success. The study further intends to provide strategies for high and low associations of the factors.

Design/methodology/approach

Lean construction and BIM are two important applications that have recently gained popularity in terms of enhancing project success. Considering this impact, this study investigates the synergy between lean construction and BIM and determines to what extent these two contribute to the success of the projects. As a first step, lean, BIM and project success were examined based on an in-depth literature review. In the second stage, a structural equation model (SEM) was established to reflect the relationship among these three through hypotheses. Then, a questionnaire was designed and administered to the construction professionals experienced in both lean and BIM implementation. The SEM was tested using Analysis of Moment Structures (AMOS), an SPSS tool.

Findings

The results indicated that lean implementation has a significant and positive impact on BIM implementation and project success. On the other hand, BIM implementation had a lower significant impact on project success than lean implementation construct.

Research limitations/implications

The results of this study can be used by both policymakers and industry practitioners in terms of developing strategies for effectively using both lean and BIM. The researchers can further develop other implementation models to investigate whether these concepts are more effective in increasing project success when used integratively.

Originality/value

This study considers both the impact of lean and BIM on project success through input from construction practitioners working on large projects. This way, the study fosters the use of lean, BIM or lean–BIM together in construction projects to enhance project success.

Purpose

Megaproject knowledge innovation (MKI) is perceived as a critical strategy for engineering value co-creation and industrial chain upgrading. Ascertaining the impact mechanism of MKI is a crucial initial step towards improving management practices. Within the framework of complex systems in megaprojects, factors exhibit intricate interdependencies. However, the current domain of knowledge has either overlooked or oversimplified this relationship and therefore cannot propose pragmatic and efficacious strategies for enhancing MKI. To close this gap, this study develops a Bayesian network (BN) model aiming to investigate the interdependencies among MKI-related factors and their impact on MKI.

Design/methodology/approach

First, this study implements literature review, expert interview and field investigation to identify the influencing factor nodes for the network model development. Second, a Bayesian network was constructed by integrating the expert knowledge with Dempster-Shafer theory. Next, a MKI measurement model was established using 253 training samples. Finally, the factor significance and optimal MKI improvement strategies are identified from the sensitivity analysis and probabilistic reasoning within the BNs.

Findings

The results indicate that (1) the BN model exhibits significant reliability and holds promotion and application value in formulating MKI management strategies; (2) knowledge sharing, shared vision and leadership are the key influencing factors of MKI; and (3) simultaneously improving institutional pressure, leadership and knowledge sharing is the most optimal strategy to enhance MKI.

Originality/value

This study innovatively introduced the BN method into the domain of MKI management, providing an appropriate approach for modelling complex relationships among factors and investigate nonlinear influences. The developed model raises megaproject stakeholders’ awareness about factors influencing MKI and presents quantified strategies that increase the likelihood of maximising MKI levels. Its ease of generalisability positions it as a promising decision support tool, facilitating the implementation of sustainable MKI practices.

Purpose

Given the growing interest in modern construction techniques and the emergence of innovative technologies, construction site layout planning research has progressively been investigating approaches to adopt innovative concepts and incorporate renewed approaches to improve widespread efficiency. This research develops a decision-making tool that optimizes construction site layout plans. The developed model targets two main objectives: minimizing material transportation costs and maximizing safety by optimally placing facilities on construction sites.

Design/methodology/approach

A novel approach is devised based on the integration of Building Information Modeling and Generative Design (BIM-GD). This engine is used to optimize the multi-objective site layout problems to identify layout alternatives in the early project stages. Parametric modeling uses Dynamo to construct the model and explore constraints initially. Finally, the GD environment is utilized to create different design alternatives, and then the decision-making procedure selects the most appropriate design alternative. Additionally, a case study is applied to validate the effectiveness of the developed model.

Findings

The results indicate the effectiveness of the proposed GD tool and its potential for more complex applications. The GD engine examined optimal layout plans, balancing different objectives and adhering to appointed geometric constraints. A case study was conducted to assess the model's effectiveness and showcase its suitability. Construction Site Layout Planning (CSLP) is an essential step in design that can influence considerable aspects, such as material transportation expenses and different safety standards on the site. Employing visual programming for parametric modeling within Dynamo-Revit creates an expedient and user-friendly platform for planning engineers who may require more programming expertise to create and program algorithmic models visually. Utilizing GD in CSLP has proven to be a powerful tool with consequential prospects for improving applications and executing more models.

Practical implications

The findings from this framework are intended to help construction practitioners select the most appropriate site layout during early project stages while incorporating different safety criteria inside construction sites to alleviate actual safety risks.

Originality/value

A new approach is proposed that utilizes an integrated BIM-GD engine to optimize multi-objective site layout problems. This approach targets two main objectives: minimizing material transportation costs and maximizing safety by optimally placing facilities in construction sites.

Purpose

The main objectives of this study are to (1) develop and test a cost contingency learning model that can generalize initially estimated contingency amounts by analyzing back the multiple project changes experienced and (2) uncover the hidden link of the learning networks using a curve-fitting technique for the post-construction evaluation of cost contingency amounts to cover cost risk for future projects.

Design/methodology/approach

Based on a total of 1,434 datapoints collected from DBB and DB transportation projects, a post-construction cost contingency learning model was developed using feedforward neural networks (FNNs). The developed model generalizes cost contingencies under two different project delivery methods (i.e. DBB and DB). The learning outputs of generalized contingency amounts were curve-fitted with the post-construction schedule and cost information, specifically aiming at uncovering the hidden link of the FNNs. Two different bridge projects completed under DBB and DB were employed as illustrative examples to demonstrate how the proposed modeling framework could be implemented.

Findings

With zero or negative values of change growth experienced, it was concluded that cost contingencies were overallocated at the contract stage. On the other hand, with positive values of change growth experienced, it was evaluated that set cost contingencies were insufficient from the post-construction standpoint. Taken together, this study proposed a tangible post-construction evaluation technique that can produce not only the plausible ranges of cost contingencies but also the exact amounts of contingency under DBB and DB contracts.

Originality/value

As the first of its kind, the proposed modeling framework provides agency engineers and decision-makers with tangible assessments of cost contingency coupled with experienced risks at the post-construction stage. Use of the proposed model will help them evaluate the allocation of appropriate contingency amounts. If an agency allocates a cost contingency benchmarked from similar projects on aspects of the base estimate and experienced risks, a set contingency can be defended more reliably. The main findings of this study contribute to post-construction cost contingency verification, enabling agency engineers and decision-makers to systematically evaluate set cost contingencies during the post-construction assessment stage and achieving further any enhanced level of confidence for future cost contingency plans.

Purpose

This study aims to enhance the precision and efficiency of English Bond brick masonry by developing three unified templates: Tc, T1, and T2. These templates are designed to address the complexities in the placement of bricks, particularly queen closers and brickbats, in English Bond patterns.

Design/methodology/approach

The research introduces a unique numbering system integrated within each template to facilitate accurate brick placement. Template Tc is designed explicitly for corner masonry, while T1 and T2 are developed for linear masonry along the X and Y directions, respectively. The approach standardizes the bricklaying process, ensuring proper alignment and bricks overlap.

Findings

Applying these templates significantly improves the efficiency and pace of English bond brick masonry. They simplify the construction process, reduce errors, and enhance the overall quality of masonry work. The study demonstrates that using these templates results in consistent and high-quality brickwork, surpassing traditional speed, efficiency, and accuracy methods.

Originality/value

This study contributes a novel, systematic approach to bricklaying in English Bond masonry. The introduction of unified templates aids in skill acquisition for professionals and learners, ensuring flawless execution of masonry projects. This innovative approach holds great value in modern construction practices, promoting standardization and excellence in masonry work.

Purpose

Integrating project scheduling and material ordering problems is vital in realistically estimating project cost and duration. Also, the quality level of materials is important as one of the key project success factors.

Design/methodology/approach

In this paper, a three-objective mathematical model is presented for green project scheduling with materials ordering problems considering rental resources. The first objective is to minimize the total cost of the project site and logistics. The second objective is to minimize the environmental impacts of producing materials and the third objective is to maximize the total quality of materials. Since costs trigger several challenges in projects, cost constraints are considered in this model for the first time and also the cost of delay in supplying of materials by the suppliers has been deducted from the project costs. Subsequently, the model was implemented in a real case and solved by the Lagrangian Relaxation algorithm as an exact method on GAMS software for model validation.

Findings

Based on sensitivity analysis of some parameters, the findings indicate that the cost constraint and lead time have considerable effects on the project duration. Also, integrating project scheduling and material ordering improves the robustness of the project schedule.

Originality/value

The primary contributions of the present research can be stated as follows: considering the cost constraints in the project scheduling with material ordering problem, incorporating the rental resources and taking the quality levels of materials as well as the environmental impacts into account.

Purpose

The paper aims to develop a useful tool that can reliably and accurately find the critical paths of high-rise buildings and provide optimal solutions considering the uncertainty based on Monte Carlo simulation (MCS) to enhance project implementation performance by assisting site workers and project managers in high-rise building engineering.

Design/methodology/approach

This research proposes an approach integrating the improved nondominated sorting genetic algorithm II (NSGA-II) considering uncertainty and delay scenarios simulated by MCS with the technique for order preference by similarity to an ideal solution.

Findings

The results demonstrate that the proposed approach is capable of generating optimal solutions, which can improve the construction performance of high-rise buildings and guide the implementation management for shortening building engineering project schedule and cost under the delay conditions.

Research limitations/implications

In this study, only the construction data of the two floors was focused due to the project at the construction stage, and future work can analyze the whole construction stage of the high-rise building to examine the performance of the approach, and the multi-objective optimization (MOO) only considered two factors as objectives, where more objectives, such as schedule, cost and quality, can be expanded in future.

Practical implications

The approach proposed in this research can be successfully applied to the construction process of high-rise buildings, which can be a guidance basis for optimizing the performance of high-rise building construction.

Originality/value

The innovations and advantages derived from the proposed approach underline its capability to handle project construction scheduling optimization (CSO) problems with different performance objectives under uncertainty and delay conditions.

Purpose

As the scale of mega transportation infrastructure projects (MTIs) continues to expand, the complexity of engineering construction sharply increases and decision-making sustainability faces severe challenges. Decision-making for mega transportation infrastructure projects unveils the knowledge-intensive characteristic, requiring collaborative decisions by cross-domain decision-makers. However, the exploration of heterogeneous knowledge fusion-driven decision-making problems is limited. This study aims to improve the deficiencies of existing decision-making by constructing a knowledge fusion-driven multi-attribute group decision model under fuzzy context to improve the sustainability of MTIs decision-making.

Design/methodology/approach

This study utilizes intuitionistic fuzzy information to handle uncertain information; calculates decision-makers and indicators weights by hesitation, fuzziness and intuitionistic fuzzy entropy; applies the intuitionistic fuzzy weighted averaging (IFWA) operator to fuse knowledge and uses consensus to measure the level of knowledge fusion. Finally, a calculation example is given to verify the rationality and effectiveness of the model.

Findings

This research finally constructs a two-level decision model driven by knowledge fusion, which alleviates the uncertainty and fuzziness of decision knowledge, promotes knowledge fusion among cross-domain decision-makers and can be effectively applied in practical applications.

Originality/value

This study provides an effective decision-making model for mega transportation infrastructure projects and guides policymakers.