Isolation of design phase from construction has made the design changes inevitable in construction projects. Extensive literature appraisal has acknowledged the detrimental effect of design changes on project performances. However, the impact and causes of design changes have been divided up, either separately or project specific. As a result, the relationship between impact and causes of design changes could not be established for general construction. The primary objective of this paper is to examine the impact of design changes on project cost and identifying actions responsible for these changes. The objectives of the study were achieved through a systematic review of past literature published in well-established journals, and contents analyzed. From the extensive literature review, it was established that the design change is one of the predominant factors to cost overrun, and in some cases, may upshot into cost overrun between 5 and 40% of the project cost. Also, many causes of design changes resulting in cost overrun within the perspective of the owner, consultant, and contractors are explored. Some projects experienced closure as a result of owner induced design changes, although these changes may not be significant in number.  Design changes as a result of consultants and contractors in some cases might have reduced impact but are frequent. For each consideration, most events leading to design changes can be eliminated by improving on communication and coordination between stakeholders. The main contribution of this research is to bring together the impact and causes of design changes on cost under one platform for effectively managing the design process.

Purpose Environmental issues caused by the production of Portland cement have led to it being replaced by waste materials such as fly ash, which is more economical and safer for the environment. Also, fly ash is a material with sustainable properties. Therefore, this paper aims to focus on the development of sustainable construction materials using 100% high-calcium fly ash and potassium hydroxide (KOH)-based alkaline solution and study the engineering properties of the resulting fly ash-based geopolymer concrete. Laboratory tests were conducted to determine the mechanical properties of the geopolymer concrete such as compressive strength, flexural strength, curing time and slump. In phase I of the study, carbon nanotubes (CNTs) were added to determine their effect on the strength of the geopolymer mortar. The results derived from the experiments indicate that mortar and concrete made with 100% fly ash C require an alkaline solution to produce similar (comparable) strength characteristics as Portland cement concrete. However, it was determined that increasing the amount of KOH generates a considerable amount of heat causing the concrete to cure too quickly; therefore, it is notable to forming a proper bond was unable to form a stronger bond. This study also determined that the addition of CNTs to the mix makes the geopolymer concrete tougher than the traditional concrete without CNT. Design/methodology/approach Tests were conducted to determine properties of the geopolymer concrete such as compressive strength, flexural strength, curing time and slump. In Phase I of the study, CNTs were studied to determine their effect on the strength of the geopolymer mortar. Findings The results derived from the experiments indicate that mortar and concrete made with 100% fly ash C require an alkaline solution to produce the same strength characteristics as Portland cement concrete. However, it was determined that increasing the amount of KOH generates too much heat causing the concrete to cure too quickly; therefore, it is notable to forming a proper bond. This study also determined that the addition of CNTs to the mix makes the concrete tougher than concrete without CNT. Originality/value This study was conducted at the construction engineering and management concrete laboratory at North Dakota State University in Fargo, North Dakota. All the experiments were conducted and analyzed by the authors.

Purpose This paper aims to discuss the evaluation of the compressive and splitting tensile strength of concrete mixes containing different proportions of up to 20 per cent glass aggregate. Portions of sand in concretes with and without admixtures were replaced with measurements of glass aggregates. Design/methodology/approach “Glascrete” is a term used for concrete in which crushed glass is used as a substitute for all or part of the aggregates. Glass can be recycled many times without changing its properties, making it an ideal material in concrete. Overall, 144 cubes and 144 cylinders of glascretes were prepared with different admixtures and subjected to compressive and splitting tensile strength test. Findings A comparison with a 21-day control mix indicated that glass aggregates are replacing sand in concrete ranging from 5 to 20 per cent by volume, resulting in 3.8-10.6 per cent and 3.9-16.4 per cent fall in compressive and tensile strength, respectively. However, the use of mineral admixture improved the properties of the mixes at 3, 7, 14 and 21 days. Social implications Cities worldwide are congested, and even those with the best waste-management system would have issues with waste disposal after the year 2030. Consequently, waste management is a current issue for cities all over the world. Originality/value This study aims to evaluate the physical properties of mortar mixes that contain different volumes of waste glass as substitutes for fine aggregate with or without additives. Mineral additives are used to improve the mechanical properties of glascrete mixes in addition to its chemical resistance by absorbing the OH− ions responsible for the possible alkali-silica reaction (ASR). It also reduces the adverse effects of mix-dimensional stability. Water-reducing admixtures are used to reduce the impact of the ASR by minimizing the amount of moisture in concrete, in effect decreasing the possible expansion of any produced gel. In this research, compressive and splitting tensile strength of concrete mortar containing waste glass of limited substitutions is evaluated.

Geostatistical methods such as simple, ordinary, and universal kriging are not multivariate models in the usual statistical function. Notwithstanding, simple, ordinary, and universal kriging techniques utilize random function models that include unlimited random variables while modeling one attribute. The cokriging technique is a multivariate estimation method that can simultaneously model two or more attributes, defined with the same domains as coregionalization. For a successful structural analysis, it is necessary to have a minimum amount of each domain's measured attributes. The assumption is that data integration methods such as cokriging may yield more reliable models because their strength is drawn from multiple variables. This study investigates the impact of the population as a variable on traffic volumes. The investigation adopts the annual average daily traffic (AADT) from  Montana, Minnesota, and Washington as one attribute and countywide population as a second attribute (or factor controlling traffic volumes). AADT data for this research span from 2009 to 2016. The cross-validation results of the model types explored with the cokriging technique are successfully used to evaluate the interpolation technique's performance and select optimal models for each state. The investigation results based on the cross-validation confirm the model's usefulness. The interpolation surface maps from the Montana and Minnesota models accurately represent the states' traffic and population density. The Washington model had a few exceptions; therefore, it did not necessarily represent the traffic and population density. An indication that other factors may impact the results. Consequently, it is worth exploring the impact of tourism, shopping, recreation centers, and possible transiting patterns throughout the state.

Background: The annual average daily traffic (AADT) data from road segments are critical for roadway projects, especially with the decision-making processes about operations, travel demand, safety-performance evaluation, and maintenance. Regular updates help to determine traffic patterns for decision-making. Unfortunately, the luxury of having permanent recorders on all road segments, especially low-volume roads, is virtually impossible. Consequently, insufficient AADT information is acquired for planning and new developments. A growing number of statistical, mathematical, and machine-learning algorithms have helped estimate AADT data values accurately, to some extent, at both sampled and unsampled locations on low-volume roadways. In some cases, roads with no representative AADT data are resolved with information from roadways with similar traffic patterns. Methods: This study adopted an integrative approach with a combined systematic literature review (SLR) and meta-analysis (MA) to identify and to evaluate the performance, the sources of error, and possible advantages and disadvantages of the techniques utilized most for estimating AADT data. As a result, an SLR of various peer-reviewed articles and reports was completed to answer four research questions. Results: The study showed that the most frequent techniques utilized to estimate AADT data on low-volume roadways were regression, artificial neural-network techniques, travel-demand models, the traditional factor approach, and spatial interpolation techniques. These AADT data-estimating methods’ performance was subjected to meta-analysis. Three studies were completed: R squared, root means square error, and mean absolute percentage error. The meta-analysis results indicated a mixed summary effect: 1. all studies were equal; 2. all studies were not comparable. However, the integrated qualitative and quantitative approach indicated that spatial-interpolation (Kriging) methods outperformed the others. Conclusions: Spatial-interpolation methods may be selected over others to generate accurate AADT data by practitioners at all levels for decision making. Besides, the resulting cross-validation statistics give statistics like the other methods' performance measures.

Background: Geostatistics focuses on spatial or spatiotemporal datasets. Geostatistics was initially developed to generate probability distribution predictions of ore grade in the mining industry; however, it has been successfully applied in diverse scientific disciplines. This technique includes univariate, multivariate, and simulations. Kriging geostatistical methods, simple, ordinary, and universal Kriging, are not multivariate models in the usual statistical function. Notwithstanding, simple, ordinary, and universal kriging techniques utilize random function models that include unlimited random variables while modeling one attribute. The coKriging technique is a multivariate estimation method that simultaneously models two or more attributes defined with the same domains as coregionalization. Objective: This study investigates the impact of populations on traffic volumes as a variable. The additional variable determines the strength or accuracy obtained when data integration is adopted. In addition, this is to help improve the estimation of annual average daily traffic (AADT). Methods Procedures, Process: The investigation adopts the coKriging technique with AADT data from 2009 to 2016 from  Montana, Minnesota, and Washington as primary attributes and population as a controlling factor (second variable). CK is implemented for this study after reviewing the literature and work completed by comparing it with other geostatistical methods. Results, Observations, and Conclusions: The Investigation employed two variables. The data integration methods employed in CK yield more reliable models because their strength is drawn from multiple variables. The cross-validation results of the model types explored with the CK technique successfully evaluate the interpolation technique's performance and help select optimal models for each state. The results from Montana and Minnesota models accurately represent the states' traffic and population density. The Washington model had a few exceptions. However, the secondary attribute helped yield an accurate interpretation. Consequently, the impact of tourism, shopping, recreation centers, and possible transiting patterns throughout the state is worth exploring.

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PurposeChallenges associated with ensuring the accuracy and reliability of cost estimation of highway construction bid items are of significant interest to state highway transportation agencies. Even with the existing research undertaken on the subject, the problem of inaccurate estimation of highway bid items still exists. This paper aims to assess the accuracy of the cost estimation methods employed in the selected studies to provide insights into how well they perform empirically. Additionally, this research seeks to identify, synthesize and assess the impact of the factors affecting highway unit prices because they affect the total cost of highway construction costs.Design/methodology/approachThis paper systematically searched, selected and reviewed 105 papers from Scopus, Google Scholar, American Society of Civil Engineers (ASCE), Transportation Research Board (TRB) and Science Direct (SD) on conceptual cost estimation of highway bid items. This study used content and nonparametric statistical analyses to determine research trends, identify, categorize the factors influencing highway unit prices and assess the combined performance of conceptual cost prediction models.FindingsFindings from the trend analysis showed that between 1983 and 2019 North America, Asia, Europe and the Middle East contributed the most to improving highway cost estimation research. Aggregating the quantitative results and weighting the findings using each study's sample size revealed that the average error between the actual and the estimated project costs of Monte-Carlo simulation models (5.49%) performed better compared to the Bayesian model (5.95%), support vector machines (6.03%), case-based reasoning (11.69%), artificial neural networks (12.62%) and regression models (13.96%). This paper identified 41 factors and was grouped into three categories, namely: (1) factors relating to project characteristics; (2) organizational factors and (3) estimate factors based on the common classification used in the selected papers. The mean ranking analysis showed that most of the selected papers used project-specific factors more when estimating highway construction bid items than the other factors.Originality/valueThis paper contributes to the body of knowledge by analyzing and comparing the performance of highway cost estimation models, identifying and categorizing a comprehensive list of cost drivers to stimulate future studies in improving highway construction cost estimates.