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Wireless sensor networks (WSN), as a solution for buried water pipe monitoring, face a new set of challenges compared to traditional application for above-ground infrastructure monitoring. One of the main challenges for underground WSN deployment is the limited range (less than 3 m) at which reliable wireless underground communication can be achieved using radio signal propagation through the soil. To overcome this challenge, the purpose of this paper is to investigate a new approach for wireless underground communication using acoustic signal propagation along a buried water pipe.

An acoustic communication system was developed based on the requirements of low cost (tens of pounds at most), low power supply capacity (in the order of 1 W-h) and miniature (centimetre scale) size for a wireless communication node. The developed system was further tested along a buried steel pipe in poorly graded SAND and a buried medium density polyethylene (MDPE) pipe in well graded SAND.

With predicted acoustic attenuation of 1.3 dB/m and 2.1 dB/m along the buried steel and MDPE pipes, respectively, reliable acoustic communication is possible up to 17 m for the buried steel pipe and 11 m for the buried MDPE pipe.

Although an important first step, more research is needed to validate the acoustic communication system along a wider water distribution pipe network.

This paper shows the possibility of achieving reliable wireless underground communication along a buried water pipe (especially non-metallic material ones) using low-frequency acoustic propagation along the pipe wall.

Water pipes degrade over time for a variety of pipe-related, soil-related, operational, and environmental factors. Hence, municipalities are necessitated to implement effective maintenance and rehabilitation strategies for water pipes based on reliable deterioration models and cost-effective inspection programs. In the light of foregoing, the paramount objective of this research study is to develop condition assessment and deterioration prediction models for saltwater pipes in Hong Kong.

As a perquisite to the development of condition assessment models, spherical fuzzy analytic hierarchy process (SFAHP) is harnessed to analyze the relative importance weights of deterioration factors. Afterward, the relative importance weights of deterioration factors coupled with their effective values are leveraged using the measurement of alternatives and ranking according to the compromise solution (MARCOS) algorithm to analyze the performance condition of water pipes. A condition rating system is then designed counting on the generalized entropy-based probabilistic fuzzy C means (GEPFCM) algorithm. A set of fourth order multiple regression functions are constructed to capture the degradation trends in condition of pipelines overtime covering their disparate characteristics.

Analytical results demonstrated that the top five influential deterioration factors comprise age, material, traffic, soil corrosivity and material. In addition, it was derived that developed deterioration models accomplished correlation coefficient, mean absolute error and root mean squared error of 0.8, 1.33 and 1.39, respectively.

It can be argued that generated deterioration models can assist municipalities in formulating accurate and cost-effective maintenance, repair and rehabilitation programs.

This paper aims to examine the relationship between the payment of bribes, the access to electricity and the productivity of informal production units (IPUs).

The data used for this study come from the second Survey on Employment and Informal Sector conducted in 2010 by the National Institute of Statistics of Cameroon and representative at the national level. The survey was conducted among 3,560 IPUs. Survey participants reported whether they had been personally affected by corruption in the twelve months preceding the survey. Relying on the data of this survey, the recursive trivariate probit model was used to study the correlation between corruption and access to electricity.

The results reveal that the payment of bribes positively influences IPU access to electricity, and consequently access to this infrastructure has a positive impact on company performance.

A main limitation of this paper is the environment of study in which corruption appeared to be institutionalised. It would therefore be interesting to extend the results obtained by conducting research in other countries and also including other infrastructures such as telecommunications.

The main contribution of this research is to highlight the effectiveness of the fight against corruption and its impact on the access of some basics resources that affect the performance of certain companies. Indeed, the fight against corruption would be easier if economic actors had access to certain resources and fundamental infrastructures for their activities. Thus, improving the supply of resources and infrastructures can be an important lever in the fight against corruption in Africa.

This research addresses a vulnerable sector vis-à-vis the pressure of the actors involved in the provision of a service essential to the activity of companies. It highlights the justification for accepting the use of corruption. Indeed, entrepreneurs are faced with a dilemma between moral standards on the one hand, and economic imperatives on the other. If corruption is a condition of access to electricity which, in turn, improves performance, it is easy to pay bribes to gain access to electricity.

Efficient management of earthmoving equipment is critical for decision-makers in construction engineering management. Thus, the purpose of this paper is to prudently identify, select, manage and optimize the associated decision variables (e.g. capacity, number and speed) for trucks and loaders equipment to minimize cost and time objectives.

This paper addresses an innovative multiobjective and multivariable mathematical optimization model to generate a Pareto-optimality set of solutions that offers insights of optimal tradeoffs between minimizing earthmoving activity’s cost and time. The proposed model has three major stages: first, define all related decision variables for trucks and loaders and detect all related constraints that affect the optimization model; second, derive the mathematical optimization model and apply the multiobjective genetic algorithms and classify all inputs and outputs related to the mathematical model; and third, model validation.

The efficiency of the proposed optimization model has been validated using a case study of earthmoving activities based on data collected from the real-world construction site. The outputs of the conducted optimization process promise the model’s originality and efficiency in generating optimal solutions for optimal time and cost objectives.

This model provides the decision-maker with an efficient tool to select the optimal design variables to minimize the activity's time and cost.

The maintainability of urban spaces has become critical with rapid urbanization to create an effective and safe environment for the increasing population. The absence of scientific studies exploring the factors that affect urban space maintainability (USM) has hindered the incorporation of maintainability aspects during the urban space planning and designing stages. This paper aims to establish critical factors for USM.

Qualitative content analysis is performed under an abductive approach to developing USM factors. A bibliometric search is conducted using databases including Scopus Elsevier, Emerald Insight, Science Direct, IEEE XPLORE and the American Society of Civil Engineers. The selected primary data set comprises journal papers on USM published after 2000. Seventy-three journal articles are selected through a comprehensive screening procedure and subjected to further analysis. The literature findings are processed via a software-assisted systematic coding and visualizing of the key data using NVivo 12 software. The coded USM factors are validated based on experts’ consensus statements by conducting an expert focus group discussion.

Twelve critical factors are established for USM; they include six design stage-related factors, one construction stage-related factor and five operational stage-related factors.

Established USM factors give an insight into the main focus areas when incorporating maintainability into urban spaces.

The authors establish a set of maintainability factors for urban spaces based on the life cycle stages. USM factors such as vegetation management, interdepartmental coordination and work zone safety draw attention to context-specific aspects of USM.

Policymakers are developing national strategic plans to encourage organizations to adopt Construction 4.0 technologies. However, organizations often adopt the recommended technologies without aligning with organizational vision. Furthermore, there is no prioritization on which Construction 4.0 technology should be adopted, including the impact of the technologies on different criteria such as safety and health. Therefore, this study aims to evaluate Construction 4.0 technologies listed in a national strategic plan that targets the enhancement of safety and health.

A list of Construction 4.0 technologies from a national strategic plan is evaluated using the fuzzy technique for order preference by similarity to ideal solution (TOPSIS) method. Then, the data are analyzed using reliability, fuzzy TOPSIS, normalization, Pareto, sensitivity, ranking and correlation analyses.

The analyses identified six Construction 4.0 technologies that are critical in enhancing safety and health: Internet of Things, autonomous construction, big data and predictive analytics, artificial Intelligence, building information modeling and augmented reality and virtualization. In addition, six pairs of Construction 4.0 technologies illustrate strong relationships.

This study contributes to the existing body of knowledge by ranking a list of Construction 4.0 technologies in a national strategic plan that targets the enhancement of safety and health. Decision-makers can use the study findings to prioritize the technologies during the adoption process. Also, to the best of the authors’ knowledge, this study is the first to evaluate the impact of Construction 4.0 technologies listed in a national strategic plan on a specific criterion.

This study aims to develop workplace well-being indexes for construction sites of different project types (infrastructure, high-rise and low-rise). Accordingly, the study objectives are to identify the critical factors that affect workplace well-being at construction sites, compare the critical factors between different project types, categorize the critical factors into subgroups and compute indexes for the critical factors and subgroups.

Data from a systematic literature review and semi-structured interviews with construction industry professionals were used to extract 19 potential factors that affect workplace well-being. Then, a structured questionnaire survey was distributed, and 169 valid responses were collected. Finally, the data were analyzed using normalized mean analysis, agreement analysis, factor analysis and fuzzy synthetic evaluation.

The study findings revealed that there are 11, 11, 8 and 12 critical factors across overall infrastructure, high-rise and low-rise construction projects. Out of those, six critical factors are overlapping across project types, including “general safety and health monitoring,” “salary package,” “timeline of salary payment,” “working hours,” “communication between workers” and “planning of the project.” Accordingly, the critical factors can be categorized into two subgroups within each project type. Finally, the development of indexes shows that infrastructure construction projects have the greatest index compared to other project types.

This study contributes to filling the current knowledge gap by developing workplace well-being indexes at construction sites across different project types. The indexes would assist decision-makers in understanding the current state of workplace well-being. This increases the commitment and recognition of well-being across different construction project types.

This study aims to investigate the impact of the 2013 privatization of Nigeria’s energy sector on the technical performance of the Benin Electricity Distribution Company (BEDC) and its workforce.

This study used a questionnaire-based approach, and 196 participants were randomly selected. Analytical tools included standard deviation, Spearman rank correlation and regression analysis.

Before privatization, the energy sector, managed by the power holding company of Nigeria, suffered from inefficiencies in fault detection, response and billing. However, privatization improved resource utilization, replaced outdated transformers and increased operational efficiency. However, in spite of these improvements, BEDC faces challenges, including unstable voltage generation and inadequate staff welfare. This study also highlighted a lack of experience among the trained workforce in emerging electricity technologies such as the smart grid.

This study’s focus on BEDC may limit its generalizability to other energy companies. It does not delve into energy sector privatization’s broader economic and policy implications.

The positive outcomes of privatization, such as improved resource utilization and infrastructure investment, emphasize the potential benefits of private ownership and management. However, voltage generation stability and staff welfare challenges call for targeted interventions. Recommendations include investing in voltage generation enhancement, smart grid infrastructure and implementing measures to enhance employee well-being through benefit plans.

Energy sector enhancements hold positive social implications, uplifting living standards and bolstering electricity access for households and businesses.

This study contributes unique insights into privatization’s effects on BEDC, offering perspectives on preprivatization challenges and advancements. Practical recommendations aid BEDC and policymakers in boosting electricity distribution firms’ performance within the privatization context.

This chapter analyzes the link between the digital divide, infrastructure regulation, and disaster planning and relief through a case study of the flood in San Jose, California triggered by the Anderson dam’s overtopping in February 2017 and an examination of communication failures during the 2018 wildfire in Paradise, California. This chapter theorizes that regulatory decisions construct social and disaster vulnerability. Rooted in the Whole Community approach to disaster planning and relief espoused by the United Nations and the Federal Emergency Management Agency, this chapter calls for leadership to end the digital divide. It highlights the imperative of understanding community information needs and argues for linking strategies to close the digital divide with infrastructure and emergency planning. As the Internet’s integration into society increases, the digital divide diminishes access to societal resources including disaster aid, and exacerbates wildfire, flood, pandemic, and other risks. To mitigate climate change, climate-induced disaster, protect access to social services and the economy, and safeguard democracy, it argues for digital inclusion strategies as a centerpiece of community-centered infrastructure regulation and disaster relief.

The world is increasingly threatened by climate change. As the dimensions of this danger grow, it becomes essential to develop the most effective policies to mitigate its impacts and adapt to these new conditions. Technology is one of the most crucial components of this process, and this study focuses on examining climate change adaptation technologies. The aim of the study is to investigate the entire spectrum of technology actors and to concentrate on the technology citation network established from the past to the present, aiming to identify the core actors within this structure and provide a more comprehensive outlook.

The study explores patent citation relationships using social network analysis. It utilizes patent data published between 2000 and 2023 and registered by the US Patent and Trademark Office.

Study findings reveal that technologies related to greenhouse technologies in agriculture, technologies for combatting vector-borne diseases in the health sector, rainwater harvesting technologies for water management, and urban green infrastructure technologies for infrastructure systems emerge as the most suitable technologies for adaptation. For instance, greenhouse technologies hold significant potential for sustainable agricultural production and coping with the adverse effects of climate change. Additionally, ICTs establish intensive connections with nearly all other technologies, thus supporting our efforts in climate change adaptation. These technologies facilitate data collection, analysis, and management, contributing to a better understanding of the impacts of climate change.

Existing patent analysis methods often fall short in detailing the unique contributions of each technology within a technological network. This study addresses this deficiency by comprehensively examining and evaluating each technology within the network, thereby enabling us to better understand how these technologies interact with each other and contribute to the overall technological landscape.