GPT Models in Construction Industry: Opportunities, Limitations, and a Use Case Validation

Large Language Models(LLMs) trained on large data sets came into prominence in 2018 after Google introduced BERT. Subsequently, different LLMs such as GPT models from OpenAI have been released. These models perform well on diverse tasks and have been gaining widespread applications in fields such as business and education. However, little is known about the opportunities and challenges of using LLMs in the construction industry. Thus, this study aims to assess GPT models in the construction industry. A critical review, expert discussion and case study validation are employed to achieve the study objectives. The findings revealed opportunities for GPT models throughout the project lifecycle. The challenges of leveraging GPT models are highlighted and a use case prototype is developed for materials selection and optimization. The findings of the study would be of benefit to researchers, practitioners and stakeholders, as it presents research vistas for LLMs in the construction industry.Comment: 58 pages, 20 figure

Copper in water-soil-plant interactions: food chain toxicity due to irrigation with Asa River in Ilorin, Nigeria

Asa River is the most important river that serves as a cheaper and easier disposal alternative to industries and at the same time as a less expensive and dependable water supply to farmers for the production of vegetables in dry season in Ilorin, the capital city of Kwara State, Nigeria. To investigate the effect of Asa River water pollution on water-soil-plant copper (Cu) mobility, a two factor factorial in randomized complete block design (RCBD) survey was conducted. The factors comprised of Factor A: distance between irrigation water sources and Factor B: irrigation history (irrigation duration in years). Four (4) farming locations, which corresponded to a control location 200 m upstream (– 200 m location), 200, 400 and 600 m downstream were selected. At each location, 4 farmers with different irrigation history were selected and the study was replicated thrice. The irrigation history was 0, 10, 20 and 30 years of irrigation with Asa river water. The results indicated that Cu levels in Asa River obtained 600 m downstream of control location, exceeded permissible limits with levels as high as 4.51 mg/L. Soil and plant tissue Cu concentrations were also found to exceed permissible levels, with plant tissue Cu reaching as high as 81.86 mg/kg in Corchorus olitorius

An Integrated Approach of Simulation and Regression Analysis for Assessing Productivity in Modular Integrated Construction Projects

Many nations across the globe face the challenge of housing deficit. Modular integrated construction (MiC), which has the highest level of prefabrication among off-site construction manufacturing (OSM), has been adopted as a fast and reliable construction method to address the housing deficit. Previous studies have assessed the productivity of the prefabrication stage of MiC, while investigations into the productivity of the MiC installation process with the consideration of pragmatic factors, especially for high-rise buildings, are lacking in the literature. Therefore, this study contributes by (1) developing a discrete-event simulation (DES) model to assess the productivity of MiC installation while considering pragmatic factors (e.g., weather conditions, topography, work dimension, etc.) and management conditions (e.g., workers’ motivation, training, equipment maintenance, etc.); (2) developing a mathematical model to understand the relationship between productivity and various resources utilized in MiC installation. After verifying and validating the DES model, it was applied to a case study in Hong Kong. A sensitivity analysis using a full factorial experiment design was conducted to identify the parameters (e.g., number of trucks, tower cranes, different crews) that significantly affect a number of performance measures, such as the project duration, productivity, and total costs. Furthermore, the mathematical model shows high prediction accuracy, as the mean absolute percentage error is 8.93%. This study would help construction practitioners in their decision-making process, while planning a project by providing them with a model that can predict the productivity of the MiC installation process before and during the project implementation

A review on the efficiency of different supplementary cementitious materials as a partial replacement for Portland cement in concrete

The effects of global warming and climate change are important and have attracted the attention of many researchers. Global warming is a result of the presence of increasing amounts of greenhouse gases in the atmosphere. Carbon dioxide, which is largely emitted into the atmosphere during the manufacture of cement clinker, is one of the greenhouse gases. Hence, researchers have explored the use of some waste materials and naturally occurring minerals as a partial replacement for cement in concrete. These materials are often referred to as supplementary cementitious materials (SCMs). Apart from the potential benefits of these SCMs for the properties of concrete, they also bring about a reduction in the amount of waste in landfill sites, as these wastes can cause land, water, and air pollution, thereby posing threats to human health. However, despite the potential benefits of SCMs in the cement and construction industry, they have not been fully utilized especially in developing countries in Africa. This may be due to low awareness of the potential benefits of SCMs among the stakeholders in the construction industry, and also limited availability. Nevertheless, due to extensive research into the usability of different materials as SCM, various materials are available in the construction market as binder systems. Thus, selecting the appropriate binder system to get the desired result for a particular concrete might be difficult for construction personnel. Hence, this study presents a review of the effects of various SCMs on the mechanical and durability properties of concrete. Six SCMs are reviewed. These SCMs include fly ash, silica fume, which are industry by-products; metakaolin, limestone calcined clay, which are naturally occurring minerals; rice husk ash, which is an agricultural waste material; and limestone-fly ash, which is a combination of an industrial by-product and a naturally occurring material. Firstly, an overview of the mechanical and durability properties of concrete is presented. This includes the presentation of general factors affecting the mechanical and durability properties of concrete. Subsequently, the effect of the various SCMs on mechanical (such as strength, elastic modulus, creep, and shrinkage) and durability properties (freeze-thaw, acid attack, sulphate attack, chloride-induced corrosion, carbonation-induced corrosion, and alkali-silica reaction) of concrete are presented. The review shows that the inclusion of appropriate dosage of these SCMs in concrete or mortar enhances their properties. Certain limitations of these SCMs are also discussed. This study also identifies areas of further research in relation to the properties of concrete produced with the SCMs

COMPARATIVE ASSESSMENT OF RICE HUSK ASH, POWDERED GLASS AND CEMENT AS LATERITIC SOIL STABILIZERS

This paper compares the stabilizing effects of three different materials, namely: rice husk ash, powdered glass, and cement on the properties of lateritic soil. The basic properties of the lateritic soil were first obtained through colour, moisture content determination, specific gravity, particle size distribution and Atterberg limits tests. Each of the stabilizing materials was then mixed with the lateritic soil in varying percentages of 2.5%, 5%, 7.5%, 10%, 12.5% and 15% by weight of the soil. Thereafter, compaction and California bearing ratio (CBR) tests were carried out on the sample mixes to determine the effects of the materials on the lateritic soil. Chemical tests were also carried out on the samples to determine their percentage oxides composition. The compaction test showed that the highest maximum dry densities (MDD) obtained for the mixed samples were 2.32 g/cm3 (at 2.5% cement addition), 2.28g/cm3 (at 5% powdered glass (PG) addition) and 2.18 g/cm3 (at 5% rice husk ash (RHA) addition) with corresponding optimum moisture contents (OMC) of 10.06%, 14.3% and 12.31% respectively. The CBR tests showed that the CBR values increased in all cases as the materials were added with those of the cement and powdered glass giving the highest values and showing close semblance under unsoaked conditions. The chemical test showed that the significant oxides present in the cement, powdered glass and rice husk ash were CaO (53.60%), SiO2 (68.45%) and SiO2 (89.84%) respectively

COMPARATIVE ASSESSMENT OF RICE HUSK ASH, POWDERED GLASS AND CEMENT AS LATERITIC SOIL STABILIZERS

This paper compares the stabilizing effects of three different materials, namely: rice husk ash, powdered glass, and cement on the properties of lateritic soil. The basic properties of the lateritic soil were first obtained through colour, moisture content determination, specific gravity, particle size distribution and Atterberg limits tests. Each of the stabilizing materials was then mixed with the lateritic soil in varying percentages of 2.5%, 5%, 7.5%, 10%, 12.5% and 15% by weight of the soil. Thereafter, compaction and California bearing ratio (CBR) tests were carried out on the sample mixes to determine the effects of the materials on the lateritic soil. Chemical tests were also carried out on the samples to determine their percentage oxides composition. The compaction test showed that the highest maximum dry densities (MDD) obtained for the mixed samples were 2.32 g/cm3 (at 2.5% cement addition), 2.28g/cm3 (at 5% powdered glass (PG) addition) and 2.18 g/cm3 (at 5% rice husk ash (RHA) addition) with corresponding optimum moisture contents (OMC) of 10.06%, 14.3% and 12.31% respectively. The CBR tests showed that the CBR values increased in all cases as the materials were added with those of the cement and powdered glass giving the highest values and showing close semblance under unsoaked conditions. The chemical test showed that the significant oxides present in the cement, powdered glass and rice husk ash were CaO (53.60%), SiO2 (68.45%) and SiO2 (89.84%) respectively

An Integrated Approach of Simulation and Regression Analysis for Assessing Productivity in Modular Integrated Construction Projects

Many nations across the globe face the challenge of housing deficit. Modular integrated construction (MiC), which has the highest level of prefabrication among off-site construction manufacturing (OSM), has been adopted as a fast and reliable construction method to address the housing deficit. Previous studies have assessed the productivity of the prefabrication stage of MiC, while investigations into the productivity of the MiC installation process with the consideration of pragmatic factors, especially for high-rise buildings, are lacking in the literature. Therefore, this study contributes by (1) developing a discrete-event simulation (DES) model to assess the productivity of MiC installation while considering pragmatic factors (e.g., weather conditions, topography, work dimension, etc.) and management conditions (e.g., workers’ motivation, training, equipment maintenance, etc.); (2) developing a mathematical model to understand the relationship between productivity and various resources utilized in MiC installation. After verifying and validating the DES model, it was applied to a case study in Hong Kong. A sensitivity analysis using a full factorial experiment design was conducted to identify the parameters (e.g., number of trucks, tower cranes, different crews) that significantly affect a number of performance measures, such as the project duration, productivity, and total costs. Furthermore, the mathematical model shows high prediction accuracy, as the mean absolute percentage error is 8.93%. This study would help construction practitioners in their decision-making process, while planning a project by providing them with a model that can predict the productivity of the MiC installation process before and during the project implementation

Municipal Solid Waste Collection and Coverage Rates in Sub-Saharan African Countries: A Comprehensive Systematic Review and Meta-Analysis

The annual volume of waste generated in sub-Saharan Africa (SSA) increased from 81 million tonnes to 174 million tonnes per year between 2012 and 2016 and is projected to reach 269 million tonnes in 2030. In 2018, SSA’s municipal solid waste (MSW) collection coverage was estimated at 44%. Concerned that the waste generation rate outweighs the collection pace, we conducted a systematic review of studies on MSW collection to examine the current situation in the region concerning the waste collection and coverage rates and to highlight the impediments to rapid progress in waste collection using the lens of four cities. Findings reveal that, despite the involvement of private waste collectors, collection and coverage rates are still below the desired 100% with backlogs of uncollected waste in public spaces, especially in low-income neighbourhoods where coverage remains abysmally low. This study fortifies the systematic discussion on MSW collection and coverage rates by conducting a meta-analysis. The result of the analysis shows that the waste collection and coverage rates are 65% and 67% in SSA, respectively. Aside from the paucity of data on waste generation rate and characterisation, most available data are incongruent. The review further shows that although several studies have been carried out on waste disposal, waste treatment and recycling in SSA studies directly focused on MSW collection are still few, leaving room for more research in this area. The review offers suggestions on how collection and coverage rates can be increased and equally proposes a strategy for reducing scavenging activities in the region’s unsanitary landfills, given its concomitant health impacts on the scavengers

Integrated intelligent models for predicting water pipe failure probability

Sustainable management of water distribution networks (WDNs) is essential to ensure the continuous supply of water. However, the water pipes in WDNs often experience unprecedented failure, which causes disruption in services, flooding, increased maintenance costs, and reduced water quality. Although researchers have developed models to predict the failure of water pipes, the literature lacks fully optimized and robust models. Therefore, this study proposes a new methodology to develop optimized models for predicting the failure probability of water pipes by fusing logistic regression with genetic algorithms. The methodology was applied to the data of the Hong Kong WDN, and experiments were conducted to optimize the hyperparameters and features of logistic regression models. The performance of the proposed methodology is evaluated using five key metrics: accuracy, precision, recall, F1 score, and Area Under the Curve (AUC). The results show significant improvement over conventional approaches, with the best model achieving an F1 score of 0.868 and an AUC of 0.944. These results show that the model can effectively predict the failure probability of water pipes. The relative contribution of each feature to the model's outcome was investigated using the SHapley Additive exPlanations. Additionally, a web application based on the proposed methodology in this study was developed for Hong Kong that other water utility management can benefit from, which can facilitate reliable decision-making for the management of WDNs

Drosophila melanogaster (Meigen, 1830): A Potential Model for Human Diseases

Over some time, Drosophila melanogaster (Meigen, 1830), commonly called fruit fly, has been used as a model organism in both scientific and medical research. Drosophila in comparison with other mammalian species shares some basic features like physiological, biological, biochemical, and neurological resemblances which make them suitable for use for biomedical research. Fruit fly can be maintained efficiently at a reduced cost in the laboratory, and it is endorsed as an alternative model compared to other vertebrates. It is confirmed and documented that almost 75 % of human disease-causing genes have functional similarities in Drosophila. Nevertheless, the use of D. melanogaster as a model organism was not narrowed to genetic research only, but several experiments. The use of this organism as a model for human diseases has also led to findings like neurodegenerative diseases, Huntington’s disease, spinocerebellar ataxia type 3, cancer, cardiovascular, inflammation and infectious diseases, and metabolic disorders. The fly is used as an ideal model organism for neurodegenerative disease studies such as Alzheimer’s and Parkinson’s, which have become more predominant in today's aging population due to its complex nervous system which conserved neurological function, and the human disease-related loci. In this review, we presented and discussed Drosophila melanogaster as a model to study several human diseases