Complex Construction Research Articles

Self-shaping free-form spatial structure with large-deformable bunched CFRP rods

Based on the bending-active concept, this paper presents a self-shaping system with bunched carbon fiber-reinforced polymer (CFRP) rods to construct complex curved surfaces, establishing an overall design and construction process. Such component consists of a bunch of slender and easily bendable CFRP rods that are preformed and then bounded by connecting joints. Then, those components form free-form spatial structures. The proposed system offers several advantages: strong designability, low transportation cost, short construction period, polished appearance and low maintenance requirements during service. For component design, to determine the shape of such component, a two-stage mechanical analysis is proposed based on the large deformation theory and finite element analysis (FEA): a form-finding process to determine the deformed component shape through its cross-sections and boundary-load conditions; and a reverse form-finding process to obtain these above-mentioned parameters from a given component shape. The reliability of both methods is verified through in-plane bending tests. A design demonstration of a saddle-shaped curved spatial structure is performed using this method. Overall, the proposed system can guide the actual construction projects of complex curved surfaces with self-shaping bunched CFRP rods.

Beyond convenience: understanding the nexus of food biosafety, service quality, and brand image in online food delivery services

ABSTRACT This study aims to present an integrative model assessing the impact of Food Biosafety Measures (FBM), e-Service Quality (e-SQ), and Product Quality (PQ) on Online Food Delivery Service (OFDS) brand image, e-consumer satisfaction (e-CS), and e-loyalty (e-LO), grounded in Expectation-Disconfirmation Theory (EDT). Employing a cross-sectional survey design, the study analyzed responses from 911 users in Bogotá and Medellín through structural equation modeling (PLS-SEM). A multigroup analysis assessed differences across these two cities. The study found that FBM significantly enhances e-CS but does not directly impact brand image, suggesting other factors, such as e-SQ and PQ, play essential roles. E-SQ and PQ robustly influence brand image, which modestly affects both e-CS and e-LO. The study confirms that e-CS significantly fosters e-LO, with FBM indirectly influencing e-LO via e-CS. For OFDS managers and policymakers, emphasizing food biosecurity, service, and product quality is vital for enhancing consumer satisfaction and fostering loyalty. Geographical nuances revealed through multigroup analysis indicate the importance of region-specific strategies for service quality and market expansion. This research fills a gap in the OFDS literature by empirically validating non-food quality attributes in generating e-CS and, indirectly, e-LO. It highlights the complexity of brand image construction and provides a framework for OFDS brands to augment consumer satisfaction and loyalty in a dynamically evolving sector.

Artificial Intelligence for Mohs and Dermatologic Surgery: A Systematic Review and Meta-Analysis.

Over the past decade, several studies have shown that potential of artificial intelligence (AI) in dermatology. However, there has yet to be a systematic review evaluating the usage of AI specifically within the field of Mohs micrographic surgery (MMS). In this review, we aimed to comprehensively evaluate the current state, efficacy, and future implications of AI when applied to MMS for the treatment of nonmelanoma skin cancers (NMSC). A systematic review and meta-analysis was conducted following PRISMA guidelines across several databases, including PubMed/MEDLINE, Embase, and Cochrane libraries. A predefined protocol was registered in PROSPERO, with literature search involving specific keywords related to AI and Mohs surgery for NMSC. From 23 studies evaluated, our results find that AI shows promise as a prediction tool for precisely identifying NMSC in tissue sections during MMS. Furthermore, high AUC and concordance values were also found across the various usages of AI in MMS, including margin control, surgical recommendations, similarity metrics, and in the prediction of stage and construction complexity. The findings of this review suggest promising potential for AI to enhance the accuracy and efficiency of Mohs surgery, particularly for NMSC.

Cooperative Phosphine-Photoredox Catalysis Enables N-H Activation of Azoles for Intermolecular Olefin Hydroamination.

Catalytic intermolecular olefin hydroamination is an enabling synthetic strategy that offers direct and atom-economical access to a variety of nitrogen-containing compounds from abundant feedstocks. However, despite numerous advances in catalyst design and reaction development, hydroamination of N-H azoles with unactivated olefins remains an unsolved problem in synthesis. We report a dual phosphine and photoredox catalytic protocol for the hydroamination of numerous structurally diverse and medicinally relevant N-H azoles with unactivated olefins. Hydroamination proceeds with high anti-Markovnikov regioselectivity and N-site selectivity. The mild conditions and high functional group tolerance of the reaction permit the rapid construction of molecular complexity and late-stage functionalization of bioactive compounds. N-H bond activation is proposed to proceed via polar addition of the N-H azole to a phosphine radical cation, followed by P-N α-scission from a phosphoranyl radical intermediate. Reactivity and N-site selectivity are classified by azole N-H BDFE and nitrogen-centered radical spin density, respectively, which can serve as a useful predictive aid in extending the reaction to unseen azoles.

Contextual factors influencing implementation of HIV treatment support strategies for female sex workers living with HIV in South Africa: A qualitative analysis using the Consolidated Framework for Implementation Research

Background: Female sex workers (FSW) face a confluence of multilevel barriers to HIV care. In South Africa, 63% of FSW are living with HIV and <40% are virally suppressed. The objective of this analysis was to identify implementation determinants of two HIV treatment support strategies. Methods: The Siyaphambili trial tested a decentralized treatment provision and an individualized case management strategy aimed to support FSW living with unsuppressed HIV viral loads. We identified a nested sample of trial participants using maximum variation sampling (n=36), as well as a purposively selected sample of implementors (n=12). We used semi-structured interview guides, developed using the Consolidated Framework for Implementation Research (CFIR), and deductively coded the transcripts using CFIR, systematically assessing the strength and valence of implementation. We compared construct ratings to determine whether any constructs distinguished implementation across strategies. Results: Across three CFIR domains (innovation characteristics, inner setting, and outer setting), 12 constructs emerged as facilitating, hindering, or having mixed effects on strategy implementation. The relative advantage, design, adaptability, and complexity constructs of the innovation characteristics, and the work infrastructure construct of the inner setting were strongly influential (±2 or +2). While the majority of construct valence and strength rating (9-12) were not distinguishing across strategies, we observed 3 weakly distinguishing CFIR constructs (relative advantage, complexity, and available resources). Conclusions: Given the potential benefits of differentiated service delivery strategies, identifying the relative importance of implementation determinants facilitates transparency and evaluation, supporting future strategy design and implementation. Optimizing implementation will support addressing inequities in HIV care and treatment services.

Dynamic path planning of autonomous bulldozers using activity-value-optimised bio-inspired neural networks and adaptive cell decomposition

Autonomous bulldozers encounter critical challenges in dynamic path planning in complex construction environments such as dynamic obstacles and narrow passages. Bio-inspired neural networks (BINN) are commonly employed for real-time path planning in dynamic environments. However, the algorithm is not feasible enough to calculate the positive input of neighbour cells, thus resulting in unreasonable distribution of activity values in the obstacle-dense regions. Additionally, the use of regular grids results in excessive computation. These drawbacks cause the algorithm to plan irrational paths and reduce computational efficiency. This study proposed an activity-value-optimised BINN and adaptive cell decomposition for the dynamic path planning of autonomous bulldozers. First, an adaptive cell decomposition method was proposed to model a highly intricate and constantly evolving environment, resulting in a reduction in the computational workload of the BINN. Second, the calculation of the activity value is optimised by enhancing the shunt equation of the BINN, which considers both obstacles and grid levels. This improvement enabled more reasonable distribution results. The smoothness of dynamic path planning is refined by incorporating the turning radius, which can be more effectively integrated with local path planning. Compared with the original BINN algorithm and other baseline algorithms, a practical large-scale hydropower project application demonstrated that the proposed method can effectively reduce the number of computational iterations, enrich the travel direction, and improve path quality while ensuring dynamic obstacle avoidance.

Performance assessment of stairwell smoke prevention techniques in a model high-rise building

The complexities of construction and increased fuel loading in high-rise buildings implied engineers to shift towards performance-based design (PBD) instead of prescription design to ensure a higher level of fire safety. Since PBD is an iterative process, so engineering models and tools based on numerical techniques are generally used to evaluate the trial designs. In the present study, various methods of stairwell smoke prevention have been analysed and compared using Fire Dynamics Simulator (FDS) in a model high-rise building. Optimization is done by defining critical values of smoke layer height and maximum pressure differential as 2 m and 55 Pa, respectively. Results demonstrated that natural and cross ventilation in the stairwell are not fully effective for smoke prevention due to the enhanced stack effect observed in them. Further, the pressurization of stairwell at a minimum differential pressure of 25–30 Pa seems to be effective only when all windows in the stairwell are closed. Effectiveness of single and multiple injection pressurization techniques are also assessed. The single injection system fails to maintain the required pressure differential when multiple doors are opened and causes high-pressure differentials at the top stories. Open windows in the stairwell reduce the effectiveness of the pressurization method. Placing an exhaust duct near windows enhances the effectiveness of the PPV method, even at lower capacities of supply vents, and proves effective when stairwell windows are open by creating a higher stack effect in the exhaust duct. The current research involved a comparative analysis of various stairwell smoke prevention methods, as outlined in NFPA 92 and in accordance with the National Building Code of India. The findings may have broader applicability beyond the studied context.

Research on the classification of complex noise-mixed microseismic events based on machine vision

Event classification is important for accurately monitoring and warning against rockburst hazards using microseismic technology. Here, we propose an automatic classification method for microseismic events based on machine vision. The method uses Histogram of Oriented Gradient (HOG) integrated with Support Vector Machine (SVM) as the core model (HOG-SVM, HSVM) to classify microseismic events. First, the method uses as input spectrograms generated from microseismic event signals recorded in the field. Next, the HOG method is used to accurately extract the spectral feature information of the useful signals of microseismic events under the interference of noisy signal. Finally, the extracted feature data is used to train SVM, after the training is completed, the SVM is used to classify the microseismic events. The performance of the method for categorizing microseismic events was tested using multiple independent test sets built from data monitored in the field of a mine in Shandong Province. The results show that the method can effectively extract the spectral feature information of useful signals of microseismic events contaminated with noise, with good classification accuracy and robustness to noise. It classifies microseismic events with high accuracy and efficiency compared to well-performing classification methods based on seismic source parameters and typical depth models. The method can provide technical support for the effective classification of microseismic events in complex construction sites, especially in noisy deep underground construction environments.

3D-Printed Hydrogels with High-Strength and Anisotropy Mediated by Chain Rigidity.

Extrusion-based 3D printing is a facile technology to construct complex structures of hydrogels, especially for tough hydrogels that have shown demonstrated potential in load-bearing materials and tissue engineering. However, 3D-printed hydrogels often possess mechanical properties that do not guarantee their usage in tissue-mimicking, load-bearing components, and motion sensors. This study proposes a novel strategy to construct high-strength and anisotropic Fe3+ cross-linked poly(acrylamide-co-acrylic acid)/sodium alginate double network hydrogels. The semi-flexible sodium alginate chains act as a "conformation regulator" to promote the formation of strong intermolecular interactions between polymer chains and lock the more extended conformation exerted by the pre-stretch, enabling the construction of 3D-printed hydrogel structures with high orientation. The equilibrated anisotropic hydrogel filaments with a water content of 50-60 wt.% exhibit outstanding mechanical properties (tensile strength: 9-44MPa; elongation at break: 120-668%; Young's modulus: 7-62MPa; toughness: 26-52MJm- 3). 3D-printed anisotropic hydrogel structures with high mechanical performance show demonstrated potential as loading-bearing structures and electrodes of flexible triboelectric nanogenerators for versatile human motion sensing.

Workforce forecasting in the building maintenance and repair work: Evaluating machine learning and LSTM models

Effective workforce forecasting is critical to strategic management in construction projects, particularly ensuring staffing is optimized for efficient and timely project completion. This aspect holds particular importance in densely populated urban environments, where the complexity and scale of high-rise building construction exacerbate the need for proactive maintenance strategies. This research aims to compare the efficacy of various machine learning models, including support vector machine (SVM), extreme gradient boosting (XGBoost), gradient-boosted decision trees (GBDT), and multi-layer perceptron (MLP) models, in predicting the workforce demand for building maintenance and repair work. Accordingly, 128 sets of data composed of seven indicators collected from the Hong Kong building maintenance and repair contractors were curated to serve as input-dependent variables to forecast the demands for different labor categories in addition to the overall workforce demands. The performance metrics for the four machine learning models were evaluated using mean absolute error (MAE), mean squared error (MSE), root mean squared error (RMSE), and determination coefficient (R2), with GridSearch employed to optimize their hyperparameters for enhanced predictive accuracy. The evaluation using MAE, MSE, RMSE, and R2 revealed that the XGBoost model achieved the most favorable results for predicting workforce demand on the testing sets (0.1790, 0.0582, 0.2412, 0.9915). Compared to MLP (0.1828, 0.0775, 0.2783, 0.8821) and GBDT (0.1828, 0.0828, 0.2878, 0.8739), XGBoost demonstrated a stronger performance in this specific task. Furthermore, the study also explored the potential of a long short-term memory (LSTM) model to forecast the gross value of building maintenance and repair work undertaken by main contractors, which strongly correlates with workforce demand. This approach aimed to capture the consistent growth trend observed across various maintenance activities. Consequently, the models developed in this study serve as valuable tools for optimizing workforce allocation and financial planning, particularly in dynamic and ever-changing environments.

Fabrication and Characterization of Collagen–Magnetic Particle Composite Microbeads for Targeted Cell Adhesion and Proliferation

The integration of the biocompatibility of collagen and the remote-control ability of magnetic elements serves as both a cell scaffold and an actuator. We studied the preparation, characterization, and potential applications of collagen–magnetic particle composite microbeads (CMPMBs). The interplay among collagen concentration, particle size, and surface roughness was found to influence cell adhesion and proliferation. Adsorption and desorption tests showed the reversible attachment of the particles to magnetic sheets, enabling precise spatial control and targeted cell delivery. The particles demonstrated their utility as cell carriers, supporting cell migration and proliferation. These findings showcase the potential of CMPMBs as a promising platform for advanced cell delivery and tissue regeneration applications. The ability to fine-tune particle properties and manipulate them using magnetic fields offers new possibilities for creating complex tissue constructs and controlling cellular behavior, which could contribute to the development of more effective regenerative therapies and tissue engineering approaches.

The Relationship between the Level of Religiousness of the Christian Orthodox Female Students of Teachers’ Education Faculties in Serbia and Their Attitudes towards Orthodox-Moral Issues

Religion is a complex construct that defines not only the historical and social identity of a nation, but also the personal identity of an individual. The attitude towards religion can be conditioned by tradition, political ideology, true faith, education, etc. In our research, we have tried to establish the level of religiousness of the female students of teacher education faculties in Serbia, belonging to the Orthodox Christianity as the dominant confession in Serbia. We examined their attitudes towards some of the moral challenges encountered by believers, including abortion, prostitution, same-sex marriages, the use of cannabis, and euthanasia. Using a snowball non-discriminative online sample of 336 female Orthodox students, we found that the students assessed themselves as above-average religious and that out of three dimensions of religiousness measured in the questionnaire, the lowest scores were recorded for the dimension of the effect of faith on their behavior. The study showed that the level of religiousness is a good predictor of attitudes towards abortion, prostitution, and same-sex marriages, but not towards the use of cannabis. Moreover, religiousness and attitudes towards prostitution are positively correlated, which is directly opposite to religious teachings. This is why a question arises as to whether we can speak about a return to faith or merely a return to the traditional model of manifesting the religious as an antipode to the secular organization in force until 1989. The results of our research point to the latter conclusion.

Green synthesis of carbon dots from mature green tea leaves for label-free fluorescence sensing of chromium(VI)

Hexavalent chromium (Cr(VI)) has been established as a highly carcinogenic and mutagenic entity. Therefore, there has been a persistent demand for affordable, sensitive, and quick sensors to monitor the presence of Cr(VI) in aqueous systems. A straightforward, cost-efficient, and eco-friendly approach has been reported in this work in terms of blue-fluorescent carbon dots (CDs, 5.2 nm) being realized in a single-step hydrothermal process. For this purpose, fully grown green tea leaves as the main carbon source were deployed due to being cheap, and environmentally sustainable and readily accessible sources. These CDs exhibit good water solubility, photo-stability and photo-emission (maximal emission at 438 nm for 360 nm excitation wavelength), anti-interference, and low cytotoxicity. The article demonstrates a straightforward fluorescent test for the precise detection and determination of Cr(VI). This was realized through the construction of a non-luminous complex which led to fluorescence quenching via the inner filter effect (IFE). Accordingly, the system can be employed as a label-free nanosensor. Under optimal condition (pH 4 and 5 min reaction time), the fluorescence quenching efficiency of CDs exhibited a well-defined linear correlation in the 0.01–8 mg/L concentration range of Cr(VI). A linear equation represents the system sensor as F0/F = 0.2101x + 1.0016 (R2 = 0.99). The limit of detection (LOD) has been as minimal as 0.004 mg/L (s/k = 3). These findings infer CDs as a potential cost-effective nanosensor for Cr(VI) identification in tap water specimens.

Recent Innovations in Footwear and the Role of Smart Footwear in Healthcare-A Survey.

Smart shoes have ushered in a new era of personalised health monitoring and assistive technologies. Smart shoes leverage technologies such as Bluetooth for data collection and wireless transmission, and incorporate features such as GPS tracking, obstacle detection, and fitness tracking. As the 2010s unfolded, the smart shoe landscape diversified and advanced rapidly, driven by sensor technology enhancements and smartphones' ubiquity. Shoes have begun incorporating accelerometers, gyroscopes, and pressure sensors, significantly improving the accuracy of data collection and enabling functionalities such as gait analysis. The healthcare sector has recognised the potential of smart shoes, leading to innovations such as shoes designed to monitor diabetic foot ulcers, track rehabilitation progress, and detect falls among older people, thus expanding their application beyond fitness into medical monitoring. This article provides an overview of the current state of smart shoe technology, highlighting the integration of advanced sensors for health monitoring, energy harvesting, assistive features for the visually impaired, and deep learning for data analysis. This study discusses the potential of smart footwear in medical applications, particularly for patients with diabetes, and the ongoing research in this field. Current footwear challenges are also discussed, including complex construction, poor fit, comfort, and high cost.

Quality of school inclusion and digital technologies: A pilot study

The quality of school inclusion of children with disabilities is a complex construct that emerges from the interaction between individual needs and different systems and processes; therefore, it needs to be investigated through a multi-method approach, using tools capable of grasping its complexity.The general goal of the study is to describe and test the modified version of two instruments: ICP and SELFIE. The ICP (the Inclusive Classroom Profile; Soukakou, 2016) is an observational tool aimed at measuring and quantifying school inclusion quality through the observation of the teachers' daily practices and the analysis of the school's policy. For the present research, the ICP was adapted to the Italian school context. Moreover, other minor changes were applied to adapt the tool to the primary and secondary levels of education. The SELFIE (Self-reflection on Effective Learning by Fostering Innovation through Educational Technology; European Commission, 2018) analyses the use of digital technologies in the school context. Originally developed as a selfevaluation questionnaire, for the present study it has been transformed into a guided interview, allowing the research team to collect more comprehensive quantitative data.The tools were tested in a pilot study involving a kindergarten and a 3rdgrade class in the municipality of Genoa. Results outline the strengths and weaknesses of the modified versions of the instruments and further changes are proposed. Furthermore, the SELFIE interviews content analysis highlights valuable insights regarding kindergarten and primary school teachers' use of digital technologies.

HYDROGEOLOGICAL RESEARCH AND TERRAIN CHARACTERIZATION ON A SECTION OF ROAD 2B SARAJEVO-FOČA, AREA OF FBiH

Roadways are complex construction (line) facilities that have specific characteristics in terms of planning, design and execution of the route. Due to the length of the roads, many characteristics of the terrain such as lithological, hydrogeological, engineering geological and geomorphological change along the route, as a result of which different effects of endodynamic and exodynamic processes and phenomena occur on certain sections. This points to the necessity of an interdisciplinary approach when creating the conceptual and implementation project, in order to choose the optimal solution based on all the influencing parameters. The route of the road that passes through the Entity of the Federation of Bosnia and Herzegovina, for the most part stretches along the valley of the Željeznica and Dobrinja rivers. The section is located at stations 0+000.00 – 3+250.00, and continues at stations 19+800.00 – 26+100.00, and the total length is 9,550 m'. The area of the studied terrain is located in the zone of the inner Dinarides of Bosnia and Herzegovina, where the pre- mountainous and moderate-continental climate prevails. The elevations of the terrain start at 505 m.a.s.l. in the part of the route near the Stupska petlje loop, and increase to an elevation of 535 m.a.s.l., in the part of Aerodromski settlement, while the second part of the section stac.19+800.00 – 26+100.00 is defined by elevations of 778 m.a.s.l. – 830 m.a.s.l. Key words: hydrogeological characteristics of the aquifer, route of the Sarajevo-Foča road.

Validity in evaluation: where is the argument-based approach heading?

The evolution of the concept of validity is examined in the context of the integration of Generative Artificial Intelligence and ethical stances, and with it, informed decision-making. The methodology used includes the history of concepts as laid out by Koselleck, analyzing how the concept of validity is a fundamental concept. The method used is a literature review, analyzing historical and contemporary perspectives and arguments from influential authors such as Messick and Kane. This conceptual journey leads us to recognize that validity is not a monolithic entity, but a complex fabric of multiple theoretical and practical threads, ranging from the internal logic of evaluations to the repercussions of their application in society. Furthermore, validity is recognized as a complex construct that cannot be simplified to a single aspect or characteristic of a test or evaluation, differentiating between validity and validation. The five historical periods distinguished in the literature that reflect paradigmatic changes in the understanding of validity were: gestational, crystallization, fragmentation, reunification, deconstruction, culminating with the period of diffusion. The most relevant conclusion is that validity is not static but dynamic, evolving with context and application. It also emphasizes the need for continuous validation adapted to emerging challenges, such as Generative Artificial Intelligence (GenAI), with the goal of ensuring that evaluations are accurate and fair amid a growing trend on ideas of quantum computing.

Attitudes, Motivation, and Beliefs of Online Foreign Language Learners Amidst the Pandemic

Covid-19 pandemic was an event which undeniably changed the paradigm of language learning. Online language learning has emerged as a significant catalyst, shedding light on distinctive attitudes, motivations, and beliefs among learners. The aim of the present study was to explore how learners articulated their attitudes, motivations, and beliefs in this context. To achieve these objectives, two hundred thirteen respondents participated in the study, completing modified versions of the AMTB (Attitude/Motivation Test Battery) and BALLI (Beliefs about Language Learning Inventory) questionnaires. In addition, eight online language learners were engaged in one-on-one interviews. Quantitative data were subjected to descriptive statistical analysis, while qualitative data were transcribed and examined using thematic analysis techniques as proposed by Auerbach & Silverstein (2003). Drawing upon the Complex Dynamic Theory by Larsen-Freeman (2017), the study's findings revealed that foreign language learning during the Covid-19 pandemic constitutes a complex dynamic construct that is profoundly influenced by learners' attitudes, motivations, and beliefs. These findings align with prior research, suggesting that learners generally have positive attitudes toward language learning and the target language community, and they exhibit a high level of motivation in their pursuit of online language acquisition.

Mathematics Teaching-Learning Methodology and Its Impact on Academic Performance

Mathematical logical thinking is essential for the development of cognitive constructs of complexity, where the symbolic and abstract take shape, hence the importance of teaching-learning of mathematics, however the strategies in the educational process are not always diverse. which may have an impact on academic performance. Objectives: identify the main methodological elements of teaching-learning mathematics and their relationship with academic performance. Methodology: the research was developed based on a quantitative, non-experimental and descriptive approach. An instrument designed with 12 questions was used, whose Cronbach's Alpha validity was .890, indicating internal consistency. The sample was 383 teachers from public and private educational entities, characterized by not having normality with a value of p .000. Results: The type of classes in public and private educational institutions is generally synthetic-analytical libraries, the institutional guide text and sources from institutional are used as guide materials, in addition, educators reinforce memory and understanding in students to enhance learning significantly, they implement problem solving and presentations, and evaluating through tests and projects. Conclusions: the mathematics teaching-learning methodology that the educators of the Sierra regime apply implies a synthetic-analytical modality, in addition, strategies are used that vary according to the type of educational entity.

Identification of Shallow Groundwater Facies and Flow Patterns in Batang Regency, Central Java, Indonesia

Abstract Population growth as a result of a subsequent increase in the economic center and industrial complex construction has also proven to influence the shallow groundwater occurrence in Batang Regency, Central Java, Indonesia. The region per se, had a minimum amount of observation and literature, especially regarding the issue namely groundwater flow pattern and facies distribution. This research discussed hydrogeological assessment through systematic mapping to investigate shallow groundwater occurrence, including 90 observation & measurement points consisting of 89 dug well and one water spring. Based on purposive random sampling with upstream-downstream consideration, 73 of 90 samples were analyzed in the laboratory to determine groundwater hydrochemistry. The results indicated that the shallow groundwater pattern in Batang Regency generally flows from south to north with a dominant southwest-northeast pattern. Based on the trilinear piper diagram, shallow groundwater facies in the study area are characterized by calcium bicarbonate, alkaline chloride, alkaline bicarbonate, and mixed types. The distribution of calcium bicarbonate facies is widely spread in the southern part of the study area. In contrast, alkaline bicarbonate is mainly found between the coastline and hilly in the south. On the other hand, alkaline chloride is distributed evenly along the coastal area. Intermixing of different groundwater facies is expected due to its positive correlation between TDI and major ions plot in the composition diagram.