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Construction projects are complex projects taking place in dynamic environments, which necessitates accounting for different uncertainties during the planning stage. There is a significant lack of management tools for repetitive projects accounting for uncertainties in the construction environment. The purpose of this paper is to present an algorithm for the optimized scheduling of repetitive construction projects under uncertainty.

Fuzzy set theory is utilized to model uncertainties associated with various input parameters. The developed algorithm has two main components: optimization component and buffering component. The optimization component presents a dynamic programming approach that processes fuzzy numbers. The buffering component converts the optimized fuzzy schedule into a deterministic schedule and inserts time buffers to protect the schedule against anticipated delays. Agreement Index (AI) is used to capture the user’s desired level of confidence in the produced schedule while sizing buffers. The algorithm is capable of optimizing for cost or time objectives. An example project drawn from literature is analysed to demonstrate the capabilities of the developed algorithm and to allow comparison of results to those previously generated.

Testing the algorithm revealed several findings. Fuzzy numbers can be utilized to capture uncertainty in various inputs without the need for historical data. The modified algorithm is capable of optimizing schedules, for different objectives, under uncertainty. Finally AI can be used to capture users’ desired confidence in the final schedule.

Project planners can utilize this algorithm to optimize repetitive projects schedules, while modelling uncertainty in different input parameters, without the need for relevant historical data.

This study aims to thoroughly examine the trends and developments of crane layout planning (CLP) in the construction field and reveal future research directions for modular integrated construction (MiC).

Through a rigorous systematic mixed-review methodology that integrates bibliometric, scientometric and qualitative analysis, this study explored the crane layout research trend; the scientometric analysis of journal sources and keywords occurrence network; the research contributions and links between influential countries; the classification of research articles based on the type of problems and solution approaches; the qualitative analysis of existing findings and research gaps; and the future research direction for CLP in MiC.

This study found five categories under the CLP domain, namely, crane selection, crane location, integrated crane selection and location, integrated crane location and allocation of supply points and hybrid problems. The major research approaches used to solve CLP is optimization (43%), visualization (23%), decision support systems (16%), simulation (11%) and qualitative techniques (7%). The possible future research directions include artificial intelligence-based models, multi-crane locations, CLP for MiC re-use, dynamic models representing real-life scenarios and building information modeling-based virtual reality models.

Through a mixed-review methodology, this study provides a comprehensive analysis of problem settings and solution methods of CLP while mitigating the subjectivity of traditional review methods. Also, it presents a repertoire on CLP and illuminates future directions for seasoned researchers in the context of MiC.

The purpose of this study is to minimize cost and minimize building condition. Weibull distribution approach was employed to generate deterioration curves over time. The third floor of Concordia University’s Engineering And Visual Arts (EV) Complex in Montreal, Canada, served as a case study to test the maintenance model and determine the optimal maintenance activities to be performed.

This research has demonstrated that there is insufficient fund allocation for the maintenance of non-residential buildings. Therefore, this research focused on designing and developing a maintenance optimization model that provides the type of spaces (architectural system) in a building. Sensitivity analysis was used to calculate weights to validate the model. Particle swarm optimization, based explicitly on multiple objectives, was applied for the optimization problem using MATLAB.

Following 100 iterations, 13 non-dominant solutions were generated. Not only was the overall maintenance cost minimized, but the condition of the building was also maximized. Moreover, the condition prediction model demonstrated that the window system type has the most rapid deterioration in educational buildings.

The model is flexible and can be modified by facility managers to align with the required codes or standards.

Although ground penetrating radar (GPR) technology is commonly used to assess the condition of reinforced-concrete (RC) bridge decks, the GPR data interpretation is not straightforward. Further, the thresholds that define the severity of deterioration are selected arbitrarily. This paper aims to solve a problem associated with GPR results generated by using a numerical amplitude method to assess corrosiveness of bridge decks.

Data, for more than 50 different bridge decks, were collected using a ground-coupled antenna. Depth-correction was performed for the collected data to normalize the reflected amplitude. Using k-means clustering technique, the amplitude values of each bridge deck were classified into four categories. Later, statistical analysis was performed where the threshold values of different categories of corrosion and deterioration are chosen. Monte-Carlo simulation technique was used to validate the value of these thresholds. Moreover, a sensitivity analysis was performed to realize the effect of changing the thresholds in the areas of corrosion.

The final result of this research is a four-category (good, fair, poor and critical) GPR scale with three fixed numerical thresholds (−7.71 dB, −10.04 dB and −14.63 dB) that define these categories. Besides, deterioration curves have been modeled using Weibull function and based on GPR outputs and corrosion areas.

The developed numerical GPR-based scale and deterioration models are expected to help the decision-makers in assessing the corrosiveness of bridge decks accurately and objectively. Hence, they will be able to take the right intervention decision for managing these decks.

This paper extends the authors’ previous research work investigating resilience for municipal infrastructure from an asset management perspective. Therefore, this paper aims to formulate a pavement resilience index while incorporating asset management and the associated resilience indicators from the authors’ previous research work.

This paper introduces a set of holistic-based key indicators that reflect municipal infrastructure resiliency. Thenceforth, the indicators were integrated using the weighted sum mean method to form the proposed resilience index. Resilience indicators weights were determined using principal components analysis (PCA) via IBM SPSS®. The developed framework for the PCA was built based on an optimization model output to generate the required weights for the desired resilience index. The output optimization data were adjusted using the standardization method before performing PCA.

This paper offers a mathematical approach to generating a resilience index for municipal infrastructure. The statistical tests conducted throughout the study showed a high significance level. Therefore, using PCA was proper for the resilience indicators data. The proposed framework is beneficial for asset management experts, where introducing the proposed index will provide ease of use to decision-makers regarding pavement network maintenance planning.

The resilience indicators used need to be updated beyond what is mentioned in this paper to include asset redundancy and structural asset capacity. Using clustering as a validation tool is an excellent opportunity for other researchers to examine the resilience index for each pavement corridor individually pertaining to the resulting clusters.

This paper provides a unique example of integrating resilience and asset management concepts and serves as a vital step toward a comprehensive integration approach between the two concepts. The used PCA framework offers dynamic resilience indicators weights and, therefore, a dynamic resilience index. Resiliency is a dynamic feature for infrastructure systems. It differs during their life cycle with the change in maintenance and rehabilitation plans, systems retrofit and the occurring disruptive events throughout their life cycle. Therefore, the PCA technique was the preferred method used where it is data-based oriented and eliminates the subjectivity while driving indicators weights.

This study aims to explore the tower crane safety factors (TCSFs) that influence tower crane safe operations (TCSOs) in modular integrated construction (MiC). It evaluates how the adoption of these factors contributes to achieving TCSOs and promoting sustainable practices (SPs) within MiC.

To achieve this aim, the study employed a systematic search to ensure a comprehensive collection of variables. Additionally, it conducted a questionnaire survey involving professionals and utilized a brainstorming technique to categorize the different variables. Finally, partial least squares structural equation modeling (PLS-SEM) was employed to test the relationship between TCSOs and SPs.

The results of measurement models indicated strong convergent and discriminant validity, with each observed variable correlating well with its latent variable. Moreover, a significant positive correlation between TCSOs and SPs was evidenced by a path coefficient (β = 0.755) and a p-value of <0.05. Lastly, the structural model revealed that the independent variables strongly influence the dependent variable (i.e. SPs) by 57%, underscoring safety's pivotal role in advancing sustainability within MiC projects. These findings provide empirical evidence that improving tower crane safety can directly enhance sustainable practices, offering a dual benefit of increased safety and sustainability for the construction sector.

This study makes a unique and previously undiscovered contribution to the field by identifying the TCSFs in MiC and employing a novel approach by utilizing PLS-SEM to create a unique mathematical model. It offers valuable insights into the relationship between TCSFs, TCSOs and SPs, thus contributing to methodological advancements within Safety Science and providing a foundation for future research and practical implementation in the construction industry.

This study underscores the critical importance of well-functioning sewer systems in achieving smart and sustainable urban drainage within cities. It specifically targets the pressing issue of sewer overflows (SO), widely recognized for their detrimental impact on the environment and public health. The primary purpose of this research is to bridge significant research gaps by investigating the root causes of SO incidents and comprehending their broader ecological consequences.

To fill research gaps, the study introduces the Multi-Phase Causal Inference Fuzzy-Based Framework (MCIF). MCIF integrates the fuzzy Delphi technique, fuzzy DEMATEL method, fuzzy TOPSIS technique and expert interviews. Drawing on expertise from developed countries, MCIF systematically identifies and prioritizes SO causes, explores causal interrelationships, prioritizes environmental impacts and compiles mitigation strategies.

The study's findings are multifaceted and substantially contribute to addressing SO challenges. Utilizing the MCIF, the research effectively identifies and prioritizes causal factors behind SO incidents, highlighting their relative significance. Additionally, it unravels intricate causal relationships among key factors such as blockages, flow velocity, infiltration and inflow, under-designed pipe diameter and pipe deformation, holes or collapse, providing a profound insight into the intricate web of influences leading to SO.

This study introduces originality by presenting the innovative MCIF tailored for SO mitigation. The combination of fuzzy techniques, expert input and holistic analysis enriches the existing knowledge. These findings pave the way for informed decision-making and proactive measures to achieve sustainable urban drainage systems.

A building deteriorates over time due to aging, wear and tear, and inadequate maintenance. Building diagnosis requires a sound knowledge of engineering, building defects, and detection tools to assess the condition of a building. The physical deterioration of a building reduces its ability to perform its intended function, while environmental deterioration influences the comfort and health of building occupants. This study presents a multi-tiered framework for the inspection of building elements and the environmental conditions of a building.

A three-tiered building inspection framework is proposed in this study, which consists of the following: Tier-I—a preliminary inspection, Tier-II—a detailed inspection, and Tier-III—an expert investigation. Each tier of inspection assesses the severity of building defects using different technologies for different levels of inspection.

Proposed multi-tier inspection framework is tested and implemented on a case study. Results were promising, with organized data management on a common platform for both physical and environmental condition inspection having the potential to save time.

The application program developed for the implementation of structured multi-tiered building inspection provides better documentation and data management for building inspection data that can save time involved in manual data operations in traditional paper-based processes.

The construction industry is facing an enormous number of challenges due to continuous advancements in construction technologies and techniques. Hence, construction management theories have to confront critical newly issues concerning market globalization and construction innovations. The key factor to address these challenges is to ameliorate the competitive abilities of the competing construction firms. In this context, measuring competitiveness of construction firms is an efficacious approach to amplify their competitive growth and profitability. To this end, the purpose of this research paper is to design a three-tier multi-criteria decision making model for competitiveness assessment and benchmarking of construction companies, meanwhile tackling a wide range of essential factors and attributes that covers broad aspects of the present competitive market.

In the first tier, four new pillars (4P) of competitiveness assessment are introduced for construction firms, namely, organization performance, project performance, environment and client and innovation and development. These pillars are able to aid in construction firms’ management on both long and short term basis. Hence, 21 key competitive factors and eighty key competitive criteria are identified, incorporated and analyzed in this research study. The second tier encapsulates carrying out a questionnaire survey in the Canadian and Vietnamese market to garner two main sets of information. The first set of information incorporates responses of the pairwise comparisons between competitiveness factors and criteria. The second set involves gathering utility scores pertinent to each competitiveness criteria. The developed model then leverages the use of analytical hierarchy process to scrutinize the relative importance priorities of competitiveness factors and criteria. The third tier of the developed model encompasses the use of multi-attribute utility theory to compute competitiveness scores for construction companies through blending criteria’ relative importance weights alongside their respective utility functions. In addition, the third tier comprises conducting a sensitivity analysis to derive the most important criteria influencing the overall competitiveness of construction companies. The developed model is tested and validated using three case studies; one construction company from Canada and two construction companies from Vietnam.

Results demonstrated that the developed model has a potential to render a synthesized and methodical performance evaluation for the competitive ability of a given construction company. Furthermore, it was found that Vietnamese companies are more considerate towards pillars pertaining to environment and client while Canadian companies are more attentive towards innovation and development. The outcome of sensitivity analysis revealed that effectiveness of cost management highly affects the competitive ability of Vietnamese companies while effectiveness of cost management exhibits the most significant influence on the competitive of Canadian companies.

The developed model can benefit construction companies to understand their competitiveness in their market and diagnose their strengths and weaknesses. It is also can be useful in efficient utilization of their limited resources and development of sustainable and long-term strategic plans strategic plans, which consequently leads to maintaining better position in their dynamic business markets.

Literature review manifests that reported competitiveness assessment models and practices are not able to address present challenges, technologies and developments in construction market.

The purpose of this research is to focus on the evaluation of heritage buildings' sustainability. BIM modeling was necessary for the design of the sustainability assessment model for Heritage Buildings (SAHB). Using ArchiCAD®, energy simulations were performed for two case studies (Murabba Palace, Saudi Arabia, and Grey Nuns Building, Canada), and the developed model was validated through sensitivity analysis.

Heritage buildings (HBs) are unique and must be preserved for future generations. This article focuses on a sustainability assessment model and rating scale for heritage buildings in light of the need for their conservation. Regional variations were considered in the model development to identify critical attributes whose corresponding weights were then determined by fuzzy logic. Data was collected via questionnaires completed by Saudi Arabian and Canadian experts, and Fuzzy TOPSIS was also applied to eliminate the uncertainties present when human opinions are involved.

Results showed that regional variations were sufficiently addressed through the multi-level weight consideration in the proposed model. Comparing the nine identified factors that affect the sustainability of HBs, energy and indoor environmental quality were of equal weight in both case studies.

This study will be helpful for the design of a globally applicable sustainability assessment model for HBs. It will also enable decision-makers to prepare maintenance plans for HBs.