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This paper aims to use a data-driven approach towards optimizing construction operations. To this extent, it presents a machine learning (ML)-aided optimization approach, wherein the construction cost is predicted as a function of time, resources and environmental impact, which is further used as a surrogate model for cost optimization.

Taking a dataset from literature, the paper has applied various ML algorithms, namely, simple and regularized linear regression, random forest, gradient boosted trees, neural network and Gaussian process regression (GPR) to predict the construction cost as a function of time, resources and environmental impact. Further, the trained models were used to optimize the construction cost applying single-objective (with and without constraints) and multi-objective optimizations, employing Bayesian optimization, particle swarm optimization (PSO) and non-dominated sorted genetic algorithm.

The results presented in the paper demonstrate that the ensemble methods, such as gradient boosted trees, exhibit the best performance for construction cost prediction. Further, it shows that multi-objective optimization can be used to develop a Pareto front for two competing variables, such as cost and environmental impact, which directly allows a practitioner to make a rational decision.

Note that the sequential nature of events which dictates the scheduling is not considered in the present work. This aspect could be incorporated in the future to develop a robust scheme that can optimize the scheduling dynamically.

The paper demonstrates that a ML approach coupled with optimization could enable the development of an efficient and economic strategy to plan the construction operations.

This research aims to create a methodology that integrates optimization techniques into preliminary cost estimates and predicts the impacts of design alternatives of steel pedestrian bridges (SPBs). The cost estimation process uses two main parameters, but the main goal is to create a cost estimation model.

This study explores a flexible model design that uses computing capabilities for decision-making. Using cost optimization techniques, the model can select an optimal pedestrian bridge system based on multiple criteria that may change independently. This research focuses on four types of SPB systems prevalent in Egypt and worldwide. The study also suggests developing a computerized cost and weight optimization model that enables decision-makers to select the optimal system for SPBs in keeping up with the criteria established for that system.

In this paper, the authors developed an optimization model for cost estimates of SPBs. The model considers two main parameters: weight and cost. The main contribution of this study based on a parametric study is to propose an approach that enables structural engineers and designers to select the optimum system for SPBs.

The implications of this research from a practical perspective are that the study outlines a feasible approach to develop a computerized model that utilizes the capabilities of computing for quick cost optimization that enables decision-makers to select the optimal system for four common SPBs based on multiple criteria that may change independently and in concert with cost optimization during the preliminary design stage.

The model can choose an optimal system for SPBs based on multiple criteria that may change independently and in concert with cost optimization. The resulting optimization model can forecast the optimum cost of the SPBs for different structural spans and road spans based on local unit costs of materials cost of steel structures, fabrication, erection and painting works.

The authors developed a computerized model that uses spreadsheet software's capabilities for cost optimization, enabling decision-makers to select the optimal system for SPBs meeting the criteria established for such a system. Based on structural characteristics and material unit costs, this study shows that using the optimization model for estimating the total direct cost of SPB systems, the project cost can be accurately predicted based on the conceptual design status, and positive prediction outcomes are achieved.

Supply chains (SCs) have become increasingly vulnerable to catastrophic events/disruptions that may be natural or man-made. Hurricanes, tsunamis and floods are natural disasters, whereas man-made disasters may be strikes, terrorist attacks, etc. Failure at any point in the SC network has the potential to cause the entire network to fail. SCs must therefore be properly designed to survive well in the disruption scenario. The capability of successful survival (of the firm’s SC) against those adverse events/happenings is termed as resilience; and, the SC designed under resilience consideration is called a resilient SC. Effective supplier selection is considered as a key strategic consideration in SC management. It is felt that apart from considering traditional suppliers selection criterions, suppliers’ resiliency strategy must be incorporated while selecting a potential supplier which can provide best support to the firm even in the disaster/disruption scenario. The purpose of this paper is to focus aspects of evaluation and selection of resilience supplier by considering general as well as resiliency strategy, simultaneously.

In this work, subjectivity associated with ill-defined (vague) evaluation information has been tackled through logical exploration of fuzzy numbers set theory. Application of VIKOR embedded with fuzzy mathematics has been utilized here. Sensitivity analysis has been performed to reflect the effect of decision-makers’ (DM) risk bearing attitude in selecting the best potential supplier in a resilient SC. A case empirical example has also been presented.

The work attempts to focus on a decision-making procedural hierarchy towards effective supplier selection in a resilient SC. The work exhibits application potential of VIKOR method integrated with fuzzy set theory to select potential supplier based on general strategy as well as resiliency strategy. The final supplier selection score (obtained by considering general strategy) and that of obtained by analyzing resiliency strategy have been combined to get a final compromise solution. The decision-support framework thus reported here also considers DMs’ risk bearing attitude.

The study bears significant impact to the industry managers who are trying to adapt resiliency strategy in their SC followed by potential supplier selection in the context of resilient SC.

Exploration of VIKOR embedded with fuzzy set theory towards suppliers’ evaluation and selection by considering general and resiliency criteria both. The decision-support module(s) adapted in this paper considers DMs’ risk bearing attitude to arrive the best compromise solution.

The purpose of this study is to empirically verify the role of determinants of information quality in shaping attitudes and intention of respondents from major metro cities of India towards electronic word of mouth (e-WOM) using personal identifying information (PII) as a moderator.

For the purpose of measuring the role of information quality in predicting attitude towards e-WOM (moderated by PII), the study proposes and validates the research framework by collecting responses from 375 respondents with diverse demographics, belonging to select metro cities of India.

All the constructs of information quality have a significantly positive effect on attitude of travellers towards e-WOM. Attitude further impacts the behavioural intention to book via an online travel agency/agent (OTA). PII successfully moderated all the hypothesized relationships with attitude except review sidedness.

The study offers a rationale to fill the gap between information quality and attitude as well as exploring new relationships using PII as a moderating variable. It further opens new avenues in researching the behavioural intention of travellers from metro cities of India with the disruptions created by Web 2.0.

OTAs in this competitive time must try to harmonize their systems to create content that is timely updated, unique, convincing and useful to build long-term relationships and generate continued revenues.

In the context of OTAs in India, this study has successfully identified the most relevant constructs of information quality and examined their effectiveness in creating a positive attitude towards e-WOM. The use of contemporary construct “PII” as a moderator will help marketers develop effective strategies for reducing anonymity in reviews and capturing the intention of experience-oriented travellers from the metro cities of India.

The purpose of this paper is to harness the potential of microwave pre-treatment to prepare carbon from locally available Sterculia foetida fruit shells for adsorption of heavy metals, particularly Nickel ions (Ni++), from effluent.

The pre-treatment methods comprise conventional methods as sun drying and oven drying as well as high intensity microwave drying. Response surface methodology was employed to analyse the optimization of the process. The adsorption behavioural characteristics of the material were established applying adsorption isotherms.

Adsorption of Ni++ was found to be effective in microwave drying at output power of 300 W. It was observed that the maximum adsorption capacity was attained at pH 6; an adsorbent dosage of 0.25 mgml−1 and initial metal ion concentration of 20 ppm with an interactive effect of initial concentration and dosage.

The research puts emphasise on prospecting of novel biomass for carbonization and application of the same for effective adsorption. Available literature on Sterculia foetida is very limited and this work will serve to create database on the amenability of processing.

It was December 13, 2010. The Government of Uttar Pradesh announced their plan to urbanize the entire area along the Yamuna Expressway (YE) in order to prevent haphazard growth of urban sprawls on the flanks of the YE. The YE was conceived in 1997 as a dream project of Ms Mayawati, the then Chief Minister of Uttar Pradesh, with the idea of reducing the travel time between Delhi (and the larger National Capital Region) and Agra. It was a 165 km long expressway and was proposed to run from Greater Noida to Agra via Mathura. Amidst issues concerning land acquisition, and various protests and litigations, the deadline for completion of the project had extended beyond its original completion date of February 8, 2010 to April 2013. Meanwhile, the project cost had escalated from Rs 2500 crore (cr) in the year 2000 to about Rs 10,000 cr as of December 2010. By then, about 80% work on the expressway had been completed. The project was finally expected to be completed around April 2011. This was, however, subject to the pending court judgements and mitigation of risks as perceived by Jaypee Infratech, the concessionaire of the project.

This paper aims to investigate the effect of post-processing techniques on dimensional accuracy of laser sintering (LS) of Nylon and Alumide^® and fused deposition modelling (FDM) of acrylonitrile butadiene styrene (ABS) materials.

Additive manufacturing (AM) of test pieces using LS of Nylon and Alumide^® powders, as well as the FDM of ABS materials, were first conducted. Next, post-processing of the test pieces involved tumbling, shot peening, hand finishing, spray painting, CNC machining and chemical treatment. Touch probe scanning of the test pieces was undertaken to assess the dimensional deviation, followed by statistical analysis using Chi-square and Z-tests.

The deviation ranges of the original built parts with those being subjected to tumbling, shot peening, hand finishing, spray painting, CNC machining or chemical treatment were found to be different. Despite the rounding of sharp corners and the removal of small protrusions, the dimensional accuracy of relatively wide surfaces of Nylon or Alumide^® test pieces were not significantly affected by the tumbling or shot peening processes. The immersion of ABS test pieces into an acetone bath produced excellent dimensional accuracy.

Only Nylon PA2200 and Alumide^® processed through LS and ABS P400 processed through FDM were investigated. Future work could also examine other materials and using parts produced with other AM processes.

The service bureaus that produce prototypes and end-use functional parts through AM will be able to apply the findings of this investigation.

This research has outlined the differences of post-processing techniques such as tumbling, shot peening, hand finishing, spray painting, CNC machining and chemical treatment. The paper discusses the advantages and disadvantages of each of those methods and suggests that the immersion of ABS test pieces into an acetone bath produced excellent dimensional accuracy.