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Construction companies need a knowledge management system to collate, share and ultimately apply this knowledge in various projects. One of the most important elements that determine the time estimates of any construction project is productivity. Such projects have a predilection towards uncertainty and therefore require new generation of prediction models that utilizes available historical data. The research presented in this paper develops, using fuzzy approach, a knowledge base to analyze, extract and infer any underlying patterns of the data sets to predict the duration and productivity of a construction process. A six-step protocol has been followed to create this model: ( i) determine which factors affect productivity; ( ii) select those factors that are critical; ( iii) build the fuzzy sets; ( iv) generate the fuzzy rules and models; ( v) develop the fuzzy knowledge base; and ( vi) validate the efficacy and function of these models in predicting the productivity construction process. The fuzzy knowledge base was validated and verified using a case study and the results were satisfactory with 92.00% mean validity. In conclusion, the developed models and system demonstrated the ability of a knowledge base management to predict the patterns and productivity of different construction operations. [ABSTRACT FROM AUTHOR]

Fuzzy models and Artificial Neural Network (ANN) systems are two well-known areas of soft-computing that have significantly helped researchers with decision-making under uncertainties. Uncertainty, an ever-present factor in construction projects, has made such intelligent systems very attractive to the construction industry. Estimating the productivity of construction operations, as a basic element of project planning and control, has become a remarkable target for forecasting models. A glimpse into this interdisciplinary field of research exposes the need for a system, that (1) models the effect of qualitative and quantitative variables on construction productivity with an improved accuracy of estimation and (2) has the ability to deal with both crisp and fuzzy input variables in one single framework. Neural-Network-Driven Fuzzy Reasoning (NNDFR), as one of the hybrid intelligent structures, displays a great potential for modeling datasets among which clear clusters are recognizable. The weakness of NNDFR in auto-tuning the design of fuzzy membership functions along with this model's insufficient attention to the optimization of number of clusters has created an area for further research. In this paper, the parameters (fuzzifier and number of clusters) of the proposed system are optimized by using Genetic Algorithm (GA) to fine-tune the system for the highest possible level of accuracy that can be exploited for productivity estimation. The proposed model is also capable of dealing with a combination of crisp and fuzzy input variables by using a hybrid modeling approach based on the application of the alpha-cut technique. The developed model helps researchers and practitioners use historical data to forecast the productivity of construction operations with a level of accuracy greater than what could be offered by traditional techniques. [ABSTRACT FROM AUTHOR]

Decision-makers frequently argue that municipal infrastructure is at risk. Given the current state of risk for infrastructure, municipalities face challenging decisions to plan the integrated repair/renewal of road, water, and sewer (RWS) networks. The research presented in this article identifies a methodology and framework that facilitates decision-making processes used during corridor rehabilitation project planning. A risk-based decision-making approach is developed to aid integrating repair/renewal of RWS assets. The risk model was developed using a mixed Delphi-Analytical Hierarchy Process approach. The impacts of four main consequences of failure with eighteen subfactors were considered. The RWS networks indices were amalgamated and grouped into an overall integrated risk index using K-means Clustering technique. Data provided by the City of Guelph was used in a case study in order to demonstrate the model features. Results show that pipe/road size and accessibility factors had the highest impact on the integrated risk index. The developed models can be used by researchers and practitioners (municipal engineers and consultants) to prioritize corridor rehabilitation projects thereby easing the challenge faced by stakeholders regarding the future of municipal infrastructure. [ABSTRACT FROM AUTHOR]

Selecting the appropriate contractor is a significant step to highway project success. The process of selection is subjected to uncertainty and influence of criteria other than bid price. This paper presents an approach to prioritize competitive contractors at the prebidding stage for highway projects by utilizing the analytic network process (ANP) and Monte Carlo simulation. Both techniques are integrated on a single platform to build the proposed model. Initially, the main quantitative and qualitative criteria affecting the contractors' selection process are identified and studied. The effective criteria in the selection process were obtained from experts and literature. A structured questionnaire was designed and sent to experts in highway projects. The ANP was used to prioritize these criteria subsequently, and Monte Carlo simulation was utilized to develop the selection model. The applicability of the proposed model was tested using four real cases of highway projects. It was concluded that using the lowest bid price as a sole criterion for selecting the best contractors may not result in an optimum solution. [ABSTRACT FROM AUTHOR]

Trenchless technology (TT) includes a large family of methods utilized for installing and rehabilitating underground utility systems with minimal surface disruption and destruction resulting from conventional excavation. Productivity of TT techniques is affected by a number of subjective factors that need to be evaluated. A productivity index (PI) model is developed in order to represent this subjective effect in refining productivity assessment. The analytic hierarchy process and fuzzy logic are used to develop the proposed PI model that relies on the actual performance of 12 subfactors under three main categories: management, environmental, and physical conditions. The developed PI model resulted in PI equal to 0.7323 and 0.7251 for microtunneling and horizontal directional drilling (HDD) projects, respectively. Multiattribute decision support system software is developed to determine the PI for a specific TT technique using Visual Basic. The PI model is tested, which shows reasonable results. This research is relevant to both industry practitioners and researchers. It provides practitioners with a model that justifies their productivity calculation by quantifying subjective factors effect, which will affect their schedule and cost estimation for trenchless projects. In addition, it provides researchers with the development methodology for the PI model. [ABSTRACT FROM AUTHOR]

The estimating process of pile construction productivity and cost is intricated because of several factors: unseen subsurface obstacles; lack of contractor experience; site planning; and pile equipment maintainability. This study intends to assess cycle time, productivity, and cost for pile construction considering the effect of the above factors using regression technique. Data were collected through designated questionnaires, site interviews, and telephone calls to experts in different construction companies. Many variables have been considered in the pile construction process. Seven regression linear models have been designed and validated to assess productivity, cycle time, and cost. Consequently, three sets of charts have been developed based upon the validated models to provide the decision maker with a solid planning, scheduling, and control tool for pile construction projects. This research is relevant to both industry practitioners and researchers. It provides sets of charts and models for practitioners’ usage to schedule and price out pile construction projects. In addition, it provides the researchers with the methodology of designating regression models for the pile construction process, its limitations, and future suggestions. [ABSTRACT FROM AUTHOR]

Bored piles are vital elements for highway bridge foundation. A large number of factors oversees productivity and cost estimation processes for piles, which creates many problems for the time and cost estimators of such process. Therefore, current study is designed to diagnose these problems and assess productivity, cycle time, and cost for pile construction using the artificial neural network (ANN). Data were collected for this study through designated questionnaires, site interviews, and telephone calls to experts in different construction companies. Many variables have been considered to manage the piling construction process. Three-layer, feed forward, and fully connected ANNs were trained with an architecture of seven input neurons, five output neurons, and different hidden layer neurons. The ANN models were validated and proved their robustness in output assessments. Three sets of charts have been developed to assess productivity, cycle time, and cost. This research is relevant to both industry practitioners and researchers. It provides sets of charts for practitioners’ usage to schedule and price out pile construction projects. In addition, it provides researchers with a methodology of applying ANN to pile construction process, its limitation, and future suggestions. [ABSTRACT FROM AUTHOR]

A concrete batch plant is an important element in any concrete construction process, whether it is working as a central mixing plant onsite or is offsite supplying ready mixed concrete to a project. This study tackles the problem of optimizing plant production to maximize profit and, if possible, revenue. A linear programming model has been designed to optimize the plant operation. The maximum production rate for each type of concrete can be obtained by solving the model under the given constraints. A sensitivity analysis is conducted to provide management with a flexible range of prices per cubic yard (cubic meter) and material storage limits. In addition, a model has been designed to determine the optimal number of transit mixers based upon the required quantity of concrete. A chart has been developed to determine the quantities of concrete ingredient materials required daily to organize the available storage space and to plan their delivery. [ABSTRACT FROM AUTHOR]

Modular construction is considered as the future of the construction industry. While the sector is growing rapidly in some areas, there are still barriers to overcome. For more than two decades, several regions and countries, such as mainland China, Hong Kong, and Singapore, were using prefabricated/precast components to construct high-rise buildings in both private and public sectors. Such practices suffered from several limitations, which warrant the need for modular construction. Modular construction method needs to evolve so as to meet different regional requirements and restrictions, namely regulations, economy, market and building types. But the regional requirements and constraints on adopting modular construction remain unexplored. These differences are not widely appreciated. Hence, this research aims to perform comparative analysis of modular construction practices in terms of policies, specifications, and real projects in Hong Kong, Singapore, and mainland China. The results of the comparative analysis indicate that Singapore has developed a relatively effective policy system to help the construction industry embrace modular construction, while the three regions have not issued authoritative specifications. In general, modular construction still needs more support from the government, especially in terms of technical guidance and innovation. Cases studies show that the sustainability performance of modular construction is not balanced in terms of the economic, social and environmental dimensions. The lessons and challenges of modular construction drawn from the cases are also summarized. The construction industry should work closely with the government to achieve sustainable construction of modular building. The developed research on modular construction will increase the understanding of common practices in close countries where the industry in each can benefit from such practices to advance the domain. Image 1042 • This study aims to perform comparative analysis of modular construction practices. • The three regions have not issued authoritative specifications. • The sustainability performance of modular construction is not balanced. • Construction industry should work closely with the government in modular building. [ABSTRACT FROM AUTHOR]

In the construction industry, contractors usually manage and execute multiple projects simultaneously. Typically, this situation involves sharing different types of resources, including cash, equipment, and manpower. The management of resources becomes a major challenge in these problems. In this situation, contractors are concerned with optimizing a number of different objectives which often conflict with one another. These objectives include duration, total cost, financing cost, required cash, profit, and resource fluctuations. This paper presents the development of a multiobjective scheduling optimization model for multiple construction projects using the fast elitist nondominated sorting genetic algorithm (NSGA-II). The purpose of the proposed model is to obtain optimal trade-offs between different projects' objectives. The scheduling optimization model comprises submodels of project scheduling, resource allocation and leveling, and cash flow forecasting. The optimization model was successfully implemented and tested using different case studies of multiple projects of different sizes. The developed model is expected to help construction companies in solving the problems of prioritizing projects under resource-conflict conditions, allocating limited resources, and optimizing all the projects' multiple objectives under certain funding limits. [ABSTRACT FROM AUTHOR]

Most construction agencies have a quality management system in order to control and manage the quality of their final product. Once the project is over, the testing results are kept in archives in which they are rarely re-visited or utilized. Quality testing results carry much information about the contractor performance that could be useful during the contractor evaluation/selection process. Previous attempts to implement the Best Value (BV) used the average performance records as the expected performance, which was utilized to evaluate contractors. The objective of this research is to develop, based on random data obtained from the contractor's performance records, a methodology that provides decision makers with the level of confidence or risk associated with the contractor selection using the BV model. Simulation technique is used to develop the BV model and analysis. Field performance data have been used to obtain the Percentage Defective, which indicates the contractor's performance in the BV model. The analysis of data indicates that performance follows a normal distribution. Sensitivity analysis of the BV model illustrates the significance of the weights in the BV model, which demands special attention when selecting the parameters’ weights. The developed methodology provides the decision makers with the confidence and risk associated with their selection decision. [ABSTRACT FROM PUBLISHER]

The dynamic nature of today’s construction industry compels construction partners to seek strategies in order to improve performance. Current research introduces a performance evaluation model for construction companies in order to provide a proper tool for a company’s managers, owners, shareholders, and funding agencies to evaluate the performance of construction companies. The model developed helps a company’s management to make the right decisions. Financial, economical, and industrial data are collected from Egyptian construction companies for nine consecutive years (1992–2000). Five indices (models) are developed: company performance score, economy performance score, industry performance score, performance index, and performance grade. The models developed consider companies in four construction sectors: general building, heavy, special trade, and real estate. These models accommodate the effect of macroeconomic and industry related factors and company size on the performance evaluation. The final outcome of current research is a performance grade, which provides the performance of a construction company. The developed model is validated, which shows robust results. [ABSTRACT FROM AUTHOR]

Sustainability principles should be implemented during all the phases of the decision-making process of constructing residential buildings to achieve maximum gains without compromising the function of such projects. This study identified and examined the critical success factors (CSFs) required for implementing value management (VM) in residential building projects. With a view to the sustainability of such the projects. The CSFs for VM were identified from previous studies, which were then contextually adjusted using both semi-structured interviews and a pilot study via the Exploratory Factor Analysis (EFA) technique. The EFA results showed that the VM CSFs could be categorized into four constructs: stakeholders and knowledge, culture and environment, workshop dynamics, and standardization. Questionnaires were then administered to 214 building stakeholders within the Egyptian building industry. Partial least square structural equation modelling (PLS-SEM) was used to develop the CSFs model. Despite that Egyptian professionals with limited practice experiences were quite knowledgeable about VM, results showed that stakeholders and knowledge were crucial CSFs for implementing VM. The results of this study will be a guide for decision-makers to reduce costs and improve sustainability by introducing VMs in the Egyptian construction industry. • Partial least squares-structural equation models (PLS-SEM) for predicting the impact of critical success factors (CSFs) for value management (VM) were validated. • The VM knowledge and awareness among Egyptian residential building practitioners are in a moderate to a high level. • Established that most Egyptian residential building practitioners did not practice VM in their projects. • Stakeholders and knowledge, culture and environment, workshop dynamics, and standardization identified as CSFs for VM. • The proposed PLS-SEM model priorities the VM CSFs and Offering VM implementation "road map" for sustainable residential building success. [ABSTRACT FROM AUTHOR]